In the past year, frontier AI labs such as OpenAI, xAI, Anthropic, Meta, and Google have all released real-time voice services. Yet voice apps also have the highest requirements for latency, privacy, and customization. It’s difficult to have a one-size-fits-all voice AI solution.

In this article, we’ll explore how to use open-source technologies to create voice AI agents that utilize your custom knowledge base, voice style, actions, fine-tuned AI models, and run on your own computer.

What We’ll Cover:

• Prerequisites

• What it Looks Like

• Two Voice AI Approaches

• The Voice AI Orchestrator

  • Configure an ASR

  • Run and configure a VAD

  • Configure an LLM

  • Configure a TTS

  • Configure MCP and actions

• Local AI With LlamaEdge

• Conclusion

Prerequisites

You’ll need to have and know a few things to most effectively follow along with this tutorial:

• Access to a Linux-like system. Mac or Windows WSL suffice too.

• Be comfortable with command line (CLI) tools.

• Be able to run server applications on the Linux system.

• Have/get free API keys from Groq and ElevenLabs.

• Optional: be able to compile and build Rust source code.

• Optional: have/get an EchoKit device or assemble your own.

What it Looks Like

The key software component we will cover is the echokit_server project. It is an open-source agent orchestrator for voice AI applications. That means it coordinates services such as LLMs, ASR, TTS, VAD, MCP, search, knowledge/vector databases, and others to generate intelligent voice responses from user prompts.

The EchoKit server provides a WebSocket interface that allows compatible clients to send and receive voice data to and from it. The echokit_box project provides an ESP32-based firmware that can act as a client to collect audio from the user and play TTS-generated voice from the EchoKit server. You can see a couple of demos here. You can assemble your own EchoKit device or purchase one.

Of course, you can also use a pure software client that conforms to the echokit_server WebSocket interface. The project publishes a JavaScript web page that you can run locally to connect to your own EchoKit server as a reference.

In the rest of the article, I will discuss how it’s implemented and how to deploy the system for your own voice AI applications.

Two Voice AI Approaches

When OpenAI released its “realtime voice” services in October 2024, the consensus was that voice AI required “end-to-end” AI models. Traditional LLMs take text as input and then respond in text. The voice end-to-end models take voice audio data as input and respond in voice audio data as well. The end-to-end models could reduce latency since the voice processing, understanding, and generation are done in a single step.

But an end-to-end model is very difficult to customize. For example, it’s impossible to add your own prompt and knowledge to the context for each LLM request, or to act on the LLM's thinking or tool-call responses, or to clone your own voice for the response.

The second approach is to use an “agent orchestration” service to tie together multiple AI models, using one model’s output as the input for the next model. This allows us to customize or select each model and manipulate or supplement the model input at every step.

• The VAD model is used to detect conversation turns in the user's speech. It determines when the user is finished speaking and is now expecting a response.

• The ASR/STT model turns user speech into text.

• The LLM model generates a text response, including MCP tool calls.

• The TTS model turns the response text into voice.

The issue with multi-model and multi-step orchestration is that it can be slow. A lot of optimizations are needed for this approach to work well. For example, a useful technique is to utilize streaming input and output wherever possible. This way, each model doesn’t have to wait for the complete response from the upstream model.

The EchoKit server is a stream-everything, highly efficient AI model orchestrator. It is entirely written in Rust for stability, safety, and speed.

The Voice AI Orchestrator

The EchoKit server project is an open-source AI service orchestrator focused on real-time voice use cases. It starts up a WebSocket server that listens for streaming audio input and returns streaming audio responses.

You can build the echokit_server project yourself using the Rust toolchain. Or, you can simply download the pre-built binary for your computer.

# for x86 / AMD64 CPUs
curl -LO https://github.com/second-state/echokit_server/releases/download/v0.1.0/echokit_server-v0.1.0-x86_64-unknown-linux-gnu.tar.gz
unzip echokit_server-v0.1.0-x86_64-unknown-linux-gnu.tar.gz

# for arm64 CPUs
curl -LO https://github.com/second-state/echokit_server/releases/download/v0.1.0/echokit_server-v0.1.0-aarch64-unknown-linux-gnu.tar.gz
unzip echokit_server-v0.1.0-aarch64-unknown-linux-gnu.tar.gz

Then, run it as follows:

nohup ./echokit_server &

It reads the config.toml file from the current directory. At the top of the file, you can configure the port on which the WebSocket server listens. You can also specify a WAV file that is downloaded to the connected EchoKit client device as a welcome message.

addr = "0.0.0.0:8000"
hello_wav = "hello.wav"

Configure an ASR

When the EchoKit server receives the user's voice data, it first sends the data to an ASR service to convert it into text.

There are many compelling ASR models available today. The EchoKit server can work with any OpenAI-compatible API providers, such as OpenAI itself, x.ai, OpenRouter, and Groq.

In our example, we use Groq’s Whisper ASR service. Whisper is a state-of-the-art ASR model released by OpenAI. Groq provides specialized hardware chips to run it very fast. You will first get a free API key from Groq. Then, configure the ASR service as follows. Notice the “prompt” for the Whisper model. It is a tried-and-true prompt to reduce hallucination of the Whisper model.

[asr]
url = "https://api.groq.com/openai/v1/audio/transcriptions"
api_key = "gsk_XYZ"
model = "whisper-large-v3"
lang = "en"
prompt = "Hello\n你好\n(noise)\n(bgm)\n(silence)\n"

Run and configure a VAD

In order to carry out a voice conversation, participants must detect each other's intentions and speak only when a turn arises. VAD (Voice Activity Detection) is a specialized AI model used to detect activities and, in particular, when the speaker has finished and expects an answer.

In EchoKit, we have VAD detection on both the device and the server.

• Device-side VAD: It detects human language. The device ignores background noise, music, keyboard sounds, and dog barking. It only sends human voice to the server.

• Server-side VAD: It processes the audio stream in 100ms (0.1s) chunks. Once it detects that the speaker has finished, it sends all transcribed text to the LLM and starts waiting for the LLM’s response stream.

The server-side VAD is optional, since the device-side VAD can also generate “conversation turn” signals. But due to the limited computing resources on the device, adding the server-side VAD can dramatically improve the overall VAD performance.

We’re porting the popular Silero VAD project from Python to Rust, and creating the silero_vad_server project. Build the project as instructed. You can start the VAD server on your EchoKit server’s port 9094 as follows:

VAD_LISTEN=0.0.0.0:9094 nohup target/release/silero_vad_server &

You might be wondering: why port to Rust? While many AI projects are written in Python for ease of development, Rust applications are often much lighter, faster, and safer at deployment. So, we’ll leverage AI tools like RustCoder to port as much Python code as possible to Rust. The EchoKit software stack is largely written in Rust.

The VAD server is a WebSocket service that listens on port 9094. As we discussed, the EchoKit server will stream audio to this WebSocket and stop the ASR when a conversation turn is detected. Therefore, we’ll add the VAD service to the EchoKit server’s ASR config section in config.toml.

[asr]
url = "https://api.groq.com/openai/v1/audio/transcriptions"
api_key = "gsk_XYZ"
model = "whisper-large-v3"
lang = "en"
prompt = "Hello\n你好\n(noise)\n(bgm)\n(silence)\n"
vad_realtime_url = "ws://localhost:9094/v1/audio/realtime_vad"

Configure an LLM

Once the ASR service transcribes the user's voice into text, the next step in the pipeline is the LLM (Large Language Model). It’s the AI service that actually “thinks” and generates an answer in text.

Again, the EchoKit server can work with any OpenAI-compatible API providers for LLMs, such as OpenAI itself, x.ai, OpenRouter, and Groq. Since the voice service is highly sensitive to speed, we’ll choose Groq again. Groq supports a number of open-source LLMs. We’ll choose the gpt-oss-20b model released by OpenAI.

[llm]
llm_chat_url = "https://api.groq.com/openai/v1/chat/completions"
api_key = "gsk_XYZ"
model = "openai/gpt-oss-20b"
history = 20

The “history” field indicates how many messages should be kept in the context. Another crucial feature of an LLM application is the “system prompt,” where you instruct the LLM how it should “behave.” You can specify the system prompt in the EchoKit server config as well.

[[llm.sys_prompts]]
role = "system"
content = """
You are a comedian. Engage in lighthearted and humorous conversation with the user. Tell jokes when appropriate.

"""

Since Groq is very fast, it can process very large system prompts in under one second. You can add a lot more context and instructions to the system prompt. For example, you can give the application “knowledge” about a specific field by putting entire books into the system prompt.

Configure a TTS

Finally, once the LLM generates a text response, the EchoKit server will call a TTS (text to speech) service to convert the text into voice and stream it back to the client device.

While Groq has a TTS service, it’s not particularly compelling. ElevenLabs is a leading TTS provider that offers hundreds of voice characters. It can express emotions and supports easy voice cloning. In the config below, you’ll put in your ElevenLabs API key and select a voice.

[tts]
platform = "Elevenlabs"
token = "sk_xyz"
voice = "VOICE-ID-ABCD"

The ElevenLabs TTS models and API services are all great, but they are not open-source. A very compelling open-source TTS, known as GPT-SoVITS, is also available.

You can port GPT-SoVITS from Python to Rust and create an open-source API server project called gsv_tts. It allows easy cloning of any voice. You can run a gsv_tts API server by following its instructions. Then, you can configure the EchoKit server to stream text to it and receive streaming audio from it.

[tts]
platform = "StreamGSV"
url = "http://gsv_tts.server:port/v1/audio/stream_speech"
speaker = "michael"

Configure MCP and actions

Of course, an “AI agent” is not just about chatting. It is about performing actions on specific tasks. For example, the “US civics test prep” use case, which I shared as an example video at the beginning of this article, requires the agent to get exam questions from a database, and then generate responses that guide the user toward the official answer. This is accomplished using LLM tools and actions.

• The LLM detects that the user is requesting a new question.

• Instead of responding in natural language, it responds with a JSON structure that instructs the agent to "get a new question and answer."

• The EchoKit server intercepts this JSON response and retrieves the question and answer from a database.

• The EchoKit server sends the question and answer back to the LLM.

• The LLM formulates a natural language response based on the question and answer.

• The EchoKit server generates a voice response using its TTS service.

As you can see, the EchoKit server needs to perform a few extra steps behind the scenes before it responds in voice. The EchoKit server leverages the MCP protocol for this. The function to look up questions and answers is provided by an open-source MCP server called ExamPrepAgent.

The MCP protocol standardizes the tools and functions for LLMs to call. There are many MCP servers available for all kinds of different tasks. ExamPrepAgent is just one of them.

We are running this MCP server on port 8003. With the MCP server up and running, you only need to add the following configuration to EchoKit server’s config.toml.

[[llm.mcp_server]]
server = "http://localhost:8003/mcp"
type = "http_streamable"

With MCP integration, the EchoKit AI agent can now perform actions. It can call APIs to send messages, make payments, or even turn electronic devices on or off.

Local AI With LlamaEdge

You’ve now seen the open-source EchoKit device working with the open-source EchoKit server to understand and respond to users in voice. But the AI models we use, while also open-source, run on commercial cloud providers. Can we run AI models using open-source technologies at home?

LlamaEdge is an open-source, cross-platform API server for AI models. It supports many mainstream LLM, ASR, and TTS models across Linux, Mac, Windows, and many CPU/GPU architectures. It’s perfect for running AI models on home or office computers. It also provides OpenAI-compatible API endpoints, which makes them very easy to integrate into the EchoKit server.

To install LlamaEdge and its dependencies, run the following shell command. It will detect your hardware and install the appropriate software that can fully take advantage of your GPUs (if any).

curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install_v2.sh | bash -s

Then, download an open-source LLM model. I am using Google's Gemma model as an example.

curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-Q5_K_M.gguf

Download the cross-platform LlamaEdge API server.

curl -LO https://github.com/second-state/LlamaEdge/releases/latest/download/llama-api-server.wasm

Start an LLamaEdge API server with the Google Gemma LLM model. by default, it listens on localhost port 8080.

wasmedge --dir .:. --nn-preload default:GGML:AUTO:gemma-3-4b-it-Q5_K_M.gguf llama-api-server.wasm -p gemma-3

Test the OpenAI compatible API on that server.

curl -X POST http://localhost:8080/v1/chat/completions \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{"messages":[{"role":"system", "content": "You are a helpful assistant. Try to be as brief as possible."}, {"role":"user", "content": "Where is the capital of Texas?"}]}'

Now, you can add this local LLM service to your EchoKit server configuration.

[llm]
llm_chat_url = "http://localhost:8080/v1/chat/completions"
api_key = "NONE"
model = "default"
history = 20

The LlamaEdge project supports more than LLMs. It runs the Whisper ASR model and the Piper TTS model as OpenAI-compatible API servers as well.

Conclusion

The voice AI agent software stack is complex and deep. EchoKit is an open-source platform that ties together and coordinates all those components. It provides a good vantage point for us to learn about the entire stack.

I can’t wait to see what you build!

Yet, a critical paradox lies at the heart of their software engineer training: most university programs still prepare them for “middle-layer” duties – wiring together pre-built libraries, cloud services, and hardware they rarely see or touch, treating the physical world as a distant abstraction.

This narrow educational focus has tangible consequences. It can blunt creativity and problem-solving skills, leaving graduates ill-prepared to design the complete, resilient solutions that society urgently needs.

This disconnect is reflected in surprising employment statistics, where computer science graduates can face higher unemployment rates than those in some non-technical fields. More importantly, it creates a generation of specialists who understand software in isolation but may lack the holistic perspective to build systems that are secure, robust, and seamlessly integrated with the physical world.

This handbook argues for a necessary evolution: a new, end-to-end engineering education that fuses software, hardware, robotics, mechanics, and cybersecurity into a single, coherent toolkit. It provides a blueprint for educators, industry leaders, and aspiring engineers to build a new generation of creators who can think across disciplines, solve complex problems from concept to deployment, and drive meaningful, sustainable progress. The moment demands not just programmers, but true system architects.

By the end of this handbook, you’ll be able to:

1. Articulate why traditional "middle integration" software education is no longer sufficient for today's technological challenges.

2. Define the core principles of End-to-End Engineering and how it integrates software with hardware, robotics, and mechanics.

3. Analyze the economic, societal, and demographic forces that demand a new, more versatile type of engineer.

4. Incorporate cybersecurity and ethical design as foundational pillars of system development, not as afterthoughts.

5. Develop a framework for overseeing and validating AI-driven systems to ensure they are reliable and secure.

6. Outline a practical, year-by-year curriculum for implementing an end-to-end engineering program.

7. Identify the benefits of this holistic approach for graduates, industry, and society as a whole.

8. Formulate strategies for overcoming common challenges in implementation, from faculty training to infrastructure investment.

Table of Contents

1. Inspiration for this Handbook

2. Why End‑to‑End Engineering Matters

3. Understanding End-to-End vs. Middle Integration in Engineering

4. Economic Challenges and Opportunities for Software Engineers

5. The Role of Institutions in Cultivating End‑to‑End Engineers

6. Proposed Reforms: Designing End-to-End Programs

7. Benefits for Graduates and Society

8. Overcoming Challenges in Implementation

9. Conclusion: A Path Forward for Engineering Education

10. Further Resources

Inspiration for this Handbook

The current educational landscape

In our complex and rapidly evolving digital world, the role of higher education as a foundation for innovation and societal progress is more crucial than ever. The rigorous systems established by universities are essential for cultivating the expertise that drives our economies forward.

At the same time, the current educational landscape presents significant opportunities for growth and adaptation. The financial model for higher education is a subject of ongoing discussion, as substantial investments from grants and endowments exist alongside rising levels of student debt. This is causing many to wonder how to best align resources with student outcomes and evolving industry needs.

This dynamic is contributing to a noticeable shift in how learners approach higher education. University degrees are no longer always seen as the exclusive pathway to a skilled career – and this trend is reflected in enrollment data across the globe.

In regions from the United States to Canada and Armenia and beyond, a significant number of university positions that were once highly competitive now remain unfilled. In response, many prospective students are diversifying their educational portfolios, pursuing industry-recognized certifications from technology leaders like Google, AWS, and Microsoft, or engaging in self-directed learning.

This suggests a broader re-evaluation of educational return on investment, as the traditional assumption of a guaranteed path from a degree to employment comes under greater scrutiny.

Evolving educational systems

Established institutions, by their nature, often take a measured approach to curricular change. This can sometimes create a gap between traditional programs and the fast-paced innovation occurring in the technology sector, where open-source knowledge and new learning platforms are becoming increasingly prevalent.

We should consider diverse global strategies in this conversation. For example, China’s model of offering extensive scholarships to international students highlights an approach focused on attracting global talent. Likewise, its emergence as a leading contributor to open-source projects and academic research demonstrates a powerful commitment to widespread knowledge sharing.

The ultimate goal of any educational system is to equip graduates with durable and relevant skills. A student’s education can be viewed as their professional operating system. A strong foundation provides the essential hardware, while a modern, integrated curriculum installs the powerful, adaptable software needed to solve complex problems and create value.

This presents a compelling opportunity for a strategic evolution in higher education. By fostering greater collaboration between academia and industry and thoughtfully integrating new hands-on learning models, we can enhance the impact and accessibility of our educational systems. The path forward lies in building a more responsive, inclusive, and sustainable framework that empowers the next generation of innovators to meet the challenges of the future.

You can download a free copy of the ebook version of this handbook here.

And you can listen to it as a podcast here:

Why End‑to‑End Engineering Matters

Employment data tell a cautionary tale. Computer science graduates currently face about 6.1% unemployment, while computer engineering majors experience a 7.5% rate – higher than fields like art history (3%) or journalism (4.4%). This mismatch stems from curricula that prize isolated coding skills over the interdisciplinary fluency modern industry expects.

Big-tech titans such as Apple, Amazon, Alphabet, Meta, Microsoft, Nvidia, and Tesla push the frontier of AI and automation, but they also expose society to new vulnerabilities – from misinformation cascades to brittle supply-chain software. And there are valid criticisms of universities – such as outdated approaches that reinforce these vulnerabilities in various ways. For example, many university programs focus courses on stitching together third-party APIs or cloud SDKs, leading students to depend on vendor ecosystems rather than building foundational technologies themselves. But, these institutions remain invaluable assets for any country.

MIT is still MIT, and Stanford continues to produce some of the world's best engineers, driving innovation through cutting-edge programs. Universities overall generate a massive workforce that transforms fields, along with groundbreaking research papers that advance global knowledge.

But many universities are being left behind due to insufficient investment in the education system and systemic inefficiencies, which are causing huge troubles for the entire world. For instance, nations need to keep pace with aging populations, where rising old-age dependency ratios – projected to increase significantly by 2055 – mean fewer workers supporting more retirees. This will potentially requiring two people to effectively pay for one non-worker through higher taxes and social security burdens.

This is evident in aging societies like Japan, Denmark, and Finland, where top personal income tax rates exceed 55%, and citizens face mounting fiscal pressures to fund pensions and healthcare.

Security is another critical concern: even nuclear agencies are being hacked, as seen in the July 2025 breach of the U.S. National Nuclear Security Administration (NNSA) by Chinese state-sponsored hackers exploiting Microsoft SharePoint vulnerabilities.

These issues highlight the urgent need for universities to foster resilient, skilled talent that can safeguard economies and societies. What this likely means is a shift away from traditional models – like over-relying on international student tuition and exorbitant fees – toward hands-on, open-source styles that democratize learning.

For example, organizations like freeCodeCamp, alongside tech giants such as Google, Microsoft, and Amazon, are open-sourcing vast engineering content that rivals entire university curricula, all without massive endowments or campus infrastructures.

Google's AI tools, like NotebookLM for generating educational content, OpenAI's agents for interactive learning, and productivity boosters such as Cursor (despite its limitations in studies showing 19% slower task completion due to bugs) are unlocking doors previously locked by institutional barriers.

These innovations allow single engineers to achieve more, as industry can no longer afford inefficiencies. This has been made clear by companies rapidly adopting alternatives to traditional systems, swapping locked gates for open pathways to boost output and adaptability.

In the context of educational institutions, end-to-end curricula offer a different path. By combining rigorous software foundations with hardware prototyping, robotics labs, mechanical design, and embedded security, universities can graduate engineers who understand an entire system’s life cycle – from concept sketches and circuit diagrams to secure deployment in the field.

Such breadth does more than widen a résumé. It also empowers graduates to spot hidden failure points, slash integration overhead, and create novel products that are both robust and ethically sound. The payoff is twofold. First, students gain adaptability: a graduate who can write control firmware, machine-learning inference code, and penetration tests is far harder to automate or outsource.

Second, industry gains innovators who can push technology forward without leaning exclusively on closed-source toolchains. This reduces systemic risk and diversifies the ecosystem.

This handbook sets out the full case for such a transformation. We will examine the economic and societal forces demanding new skills, survey pioneering institutions already leading the charge, and map a practical blueprint for universities ready to pivot.

The goal is simple: equip tomorrow’s engineers to build end-to-end solutions that drive progress responsibly – and ensure they share equitably in the value they create.

Understanding End-to-End vs. Middle Integration in Engineering

The Scope of Traditional Software Engineering

Traditional software engineering education focuses on intermediary roles, where engineers develop software to bridge users and systems – such as connecting databases to applications, devices to networks, or algorithms to outputs.

This "middle" integration approach often involves working with pre-existing hardware, such as laptops from manufacturers like Dell or Apple, and leveraging APIs or cloud services provided by leading tech companies.

While it’s effective in specific contexts, this focus can lead to inefficiencies, as engineers dedicate significant time to managing integrations rather than creating innovative solutions. Also, reliance on third-party tools can introduce complexities, including compatibility issues or security vulnerabilities, which require ongoing maintenance and can limit creative problem-solving.

For example, engineers working with cloud platforms may spend considerable effort resolving version conflicts or debugging third-party APIs, diverting resources from developing new features. This dynamic can also expose systems to risks, as external tools may contain outdated libraries or vulnerabilities that require constant updates.

The 2020 SolarWinds hack, which compromised organizations through a supply chain attack, illustrates the challenges of fragmented development, where reliance on external components can introduce unforeseen risks.

The Vision of End-to-End Engineering

End-to-end engineering education adopts a holistic approach, training students to oversee every stage of system development, from ideation to deployment. This encompasses software development, hardware prototyping, mechanical engineering for physical systems like robotics, and cybersecurity to ensure system integrity.

For instance, an end-to-end engineer might design a robotic arm’s software, optimize its mechanical components for precision and durability, and embed security protocols to protect against cyber threats. This comprehensive skill set helps engineers create integrated, resilient systems that minimize reliance on external tools and enhance system reliability.

The benefits of this approach are multifaceted. Robotics training equips engineers to address physical constraints, such as sensor accuracy, motor efficiency, or material strength, fostering innovation in fields like autonomous vehicles, industrial automation, and medical robotics.

Mechanical engineering bridges the digital and physical realms, enabling engineers to design systems that interact seamlessly with the real world.

Cybersecurity integration is critical in an era of increasing connectivity, as devices like robots and IoT systems face growing risks of cyber threats. For example, industrial robots designed with embedded security can prevent disruptions like the Stuxnet attack, which targeted control systems, ensuring operational continuity and safety.

Addressing Curriculum Gaps

Current software engineering curricula, typically spanning 120-130 credits over four years, cover foundational topics such as mathematics (calculus, linear algebra), programming languages (Python, Java, C++), data structures, and software design principles. While these are essential, programs often include courses like introductory chemistry or unrelated electives that may not align with modern industry needs, consuming valuable time and resources.

Meanwhile, key interdisciplinary skills – robotics, mechanical engineering, and cybersecurity – are often underrepresented, leaving graduates less prepared for real-world challenges where software must integrate with hardware under security constraints.

This curriculum gap can impact graduates’ economic outcomes. At companies like Meta, engineers earn competitive salaries ($210,000 to $3.67 million annually, including bonuses and stock), yet the broader distribution of corporate profits, such as Meta’s $39 billion in 2023, tends to favor executives and shareholders.

Similarly, Vivaro, an online casino platform based in Armenia, has leveraged the country’s relatively low labor costs and favorable government relations to achieve rapid growth with minimal regulatory oversight, highlighting how companies can benefit from localized economic advantages.

This dynamic underscores how reliance on integration-focused roles can limit engineers’ ability to capture the full value of their work, as companies maximize profits through strategic labor and regulatory practices.

End-to-end education addresses this by equipping engineers with versatile skills to innovate independently, pursue entrepreneurial ventures, or lead multidisciplinary projects, enabling them to contribute meaningfully and share more equitably in the value they create.

Pioneering Models for the Future

Institutions like MIT are leading the way with programs that integrate computer science, electrical engineering, robotics, and cybersecurity.

MIT’s Department of Electrical Engineering and Computer Science (EECS) offers courses like "Robotics: Science and Systems," where students design complete robotic solutions, blending software, hardware, and security. These programs produce graduates who excel in diverse roles, from developing secure autonomous systems to founding innovative startups.

Similarly, Stanford’s AI and Robotics track combines software development with mechanical engineering and cybersecurity, preparing students for complex challenges like secure drone navigation.

By adopting such models, educational institutions can better prepare students for a rapidly evolving industry, ensuring they are equipped to navigate and contribute to a technology-driven world.

Economic Challenges and Opportunities for Software Engineers

Today’s software engineers face a complex landscape of economic and societal pressures that are fundamentally reshaping their roles. Much of the work has shifted from pure invention to integration, often centering on stitching together proprietary clouds and third-party APIs.

This moves engineering effort toward upkeep – resolving version conflicts, debugging vendor libraries, and managing deployment pipelines – rather than creating foundational technology. This dynamic not only suppresses an engineer's individual earning potential, as disproportionate profits flow to leadership and investors, but also leaves businesses vulnerable to vendor lock-in and supply-chain shocks.

Dual Nature of AI

Compounding this challenge is the dual nature of modern artificial intelligence. While AI tools promise to accelerate code generation, their practical application reveals significant limitations and challenges. Real-world studies, such as the METR study, show that developers often overestimate AI's productivity benefits and can face slowdowns of nearly 20% due to the time spent fixing flawed or inefficient code.

This highlights that human oversight remains indispensable, especially when AI outputs must interface with custom hardware or meet strict safety standards.

The opportunity lies with engineers who understand the full system – electronics, mechanics, and secure architecture – and can effectively validate and harden AI-driven solutions.

Societal Challenges

Simultaneously, society is placing new and urgent demands on the engineering profession. An aging global population and declining birth rates are tightening the economic noose, with fewer workers supporting more retirees. This demographic headwind necessitates greater automation in manufacturing, food production, and healthcare.

The engineers who can deliver these solutions – by designing robotic arms for harvesting, smart greenhouses for urban farming, or humanoid helpers for elder care – will be at the forefront of tackling this challenge and opening new economic frontiers.

Beyond this, in a world flooded by misinformation and clickbait, engineers have an ethical duty to build systems that prioritize truth and transparency, embedding features like content-verification protocols and secure data handling to foster a trustworthy digital environment.

Changing Demands

These evolving demands expose a critical disconnect in traditional education. Costly four-year degrees too often leave graduates with narrow skill sets and surprisingly high unemployment rates (6-7.5%) that rival non-technical fields. This mismatch arises from curricula that prioritize isolated foundational skills or include unrelated electives over the practical, interdisciplinary training modern industry requires.

The path forward is through a more streamlined and relevant education that acts as a catalyst for resilience. By replacing less applicable courses with accelerated, hands-on projects, institutions can transform learners from passive code-integrators into formidable innovators.

Globally, leading institutions are already recognizing this need. In Nordic countries like Sweden and Finland, programs that integrate sustainability, ethics, and interdisciplinary skills are producing graduates who excel at innovation.

By adopting similar approaches – offering real-world modules in robotics prototyping, embedded security, and end-to-end system integration – we can empower engineers to meet today's complex demands and build the resilient, automated, and trustworthy systems our world urgently needs.

The Role of Institutions in Cultivating End‑to‑End Engineers

As technology shifts ever faster – reintegrating software with custom hardware, AI-driven automation, and secure connected systems – traditional universities risk obsolescence unless they reinvent themselves. Beyond breaking down academic silos, forward‑looking institutions will need to embrace four key strategies:

1. Embrace Agility Through Continuous Curriculum Evolution

Modular, Stackable Credentials
Universities should offer micro‑certificates in robotics prototyping, embedded security, or systems integration alongside full degrees. Students and professionals can assemble just the modules they need, when they need them – mirroring the on‑demand model of platforms like Coursera or Google’s own AI toolkits.

Real‑Time Industry Feedback Loops
They should also have rolling curriculum reviews with employer advisory boards. If a new sensor technology or cloud‑native inference engine emerges, courses can pivot within months, not years, ensuring graduates never learn outdated tools.

2. Partner with EdTech Leaders – Don’t Compete Alone

Leverage Existing Toolchains
Rather than ignoring Google’s free AI labs or Microsoft’s cloud credits, universities can integrate them directly into their coursework. Assignments could require deploying a hardware‑accelerated model on Google Coral or securing an Azure‑hosted IoT network.

Co‑Create Open Educational Resources
Institutions could also collaborate on open‑source textbooks, interactive labs, and tutorial videos – both to amplify institutional reach and to demonstrate that the university is part of, not apart from, today’s creator economy.

3. Prioritize User‑Centric Design in Education

Student and Employer Needs First
Schools should also treat their “customers” (students and hiring companies) as co‑designers. Conduct regular surveys and job‑task analyses: What exact blend of Linux kernel debugging, CAD design, and cryptographic key management does the next‑gen engineer need? Then build courses to match.

Flexible Delivery Modalities
They could also combine in‑person maker‑space workshops with online simulators (for example, Gazebo robotics, virtual FPGA labs) so that learners worldwide can participate – reducing geographic and economic barriers.

4. Cultivate an Ecosystem of Lifelong Learning

Alumni‑for‑Credit Programs
Universities could offer discounted, advanced modules for graduates to return and upskill as hardware standards or threat landscapes evolve. This continuous‑learning pathway turns one‑off degrees into multi‑decade partnerships.

Innovation Incubators and Industry Challenges
They could also host hackathons, sponsored capstone projects, and startup incubators right on campus. When students design and pitch end‑to‑end solutions for real companies – say, a secure medical‑robotics prototype – they graduate not just with a diploma, but with market‑tested experience and potential investors.

5. Staying Relevant – and Un-gatekeeping

With Google, Apple, and a legion of online platforms freely distributing cutting‑edge AI, robotics toolkits, and interactive tutorials, any institution that clings to century‑old lecture halls and fixed curricula looks increasingly like a barrier, not a gateway. To avoid that fate:

Shift from “Seat Time” to “Skill Proof”: Replace rigid credit hours with outcomes‑based assessments – portfolios, live demos, and secure system audits prove mastery far better than final exams.

Align incentives around impact, not enrollment: Reward faculty for evolving courses, publishing open resources, and mentoring student startups rather than gatekeeping admissions or ballooning class sizes.

By viewing themselves not as ivory‑tower knowledge guardians but as agile partners in an ever‑changing tech ecosystem, educational institutions can remain indispensable. They’ll graduate engineers who wield software and hardware with equal fluency, who adapt on the fly, and who drive innovation – and who never fear being “left behind” by the next big Google toolkit.

Proposed Reforms: Designing End-to-End Programs

Curriculum Transformation

To implement end-to-end engineering education, institutions should redesign curricula to prioritize interdisciplinary skills across a structured timeline like the following:

1. Year 1: Core Foundations – Focus on mathematics (calculus, linear algebra, probability) and programming (Python, C++, JavaScript), introducing systems thinking, basic robotics concepts, and an overview of cybersecurity principles. This foundational year ensures students build a strong technical base while gaining exposure to interdisciplinary applications.

2. Year 2: Software and Hardware Integration – Combine software development with mechanical engineering, emphasizing hands-on projects like robot prototyping. Courses might include designing simple robotic systems, such as a sensor-based navigation device, to connect digital and physical systems and introduce students to hardware constraints.

3. Year 3: Cybersecurity and Ethics – Teach cybersecurity principles, such as encryption and secure system design, alongside AI ethics to promote responsible technology development. Projects could involve securing IoT devices or analyzing AI-generated code for vulnerabilities, preparing students for real-world challenges.

4. Year 4: Capstone Projects – Require students to design and deploy real-world systems, such as secure IoT devices, autonomous robots, or energy-efficient automation systems, integrating all learned disciplines. These projects should involve collaboration with industry partners or research labs to ensure practical relevance.

This structure prioritizes practical, relevant skills, replacing less applicable courses with interdisciplinary modules that align with industry needs.

Faculty and Resources

Recruiting faculty with expertise in robotics, mechanical engineering, and cybersecurity is essential for delivering a robust curriculum. Institutions can support collaboration through training programs, workshops, and incentives like joint research grants. For example, faculty from computer science and mechanical engineering could co-teach courses on robotic system design, fostering an interdisciplinary approach.

Investments in infrastructure, such as robotics labs, 3D printing facilities, and cybersecurity simulation environments, are necessary but can be costly. Institutions can implement phased rollouts, starting with virtual simulations or open-source tools to reduce initial expenses.

Grants from organizations like the National Science Foundation (NSF) or partnerships with industry can offset costs, ensuring long-term sustainability. For instance, virtual robotics platforms like Gazebo allow students to simulate robot designs before building physical prototypes, making training more accessible.

Industry Collaboration

Partnerships with industry provide hands-on experience, ensuring students gain practical skills aligned with market needs. These collaborations should prioritize ethical practices, focusing on projects that address societal challenges, such as sustainable technology, secure systems, or healthcare robotics.

For example, joint labs with companies developing energy-efficient automation systems can enhance learning while fostering responsible development. Institutions must ensure partnerships emphasize student development and societal benefit, avoiding scenarios where corporate priorities overshadow educational goals.

Accessible and Flexible Pathways

To make end-to-end education accessible, institutions can offer accelerated programs, such as three-year degrees or modular bootcamps, incorporating AI tools to enhance efficiency.

For example, once they’ve learned key programming concepts, students could use AI-assisted coding platforms to prototype systems, learning to validate outputs for accuracy and security. Online platforms can broaden access, enabling diverse populations to benefit from comprehensive training. Partnerships with community colleges and vocational programs can create pathways for underrepresented groups, fostering an inclusive engineering workforce.

Continuous Curriculum Evolution

To remain relevant, institutions must continuously evolve their curricula to reflect emerging technologies and industry trends. This includes incorporating advancements in AI, such as generative models or reinforcement learning, and addressing new cybersecurity threats, like quantum computing risks. Regular feedback from alumni, industry partners, and students can ensure curricula stay aligned with real-world needs, preparing graduates for long-term success.

Benefits for Graduates and Society

Enhancing Graduate Outcomes

End-to-end education prepares graduates for a competitive market, reducing unemployment risks and enabling higher earnings. With skills in AI oversight, robotics, and hardware design, graduates can pursue roles in high-demand fields like healthcare robotics, secure IoT systems, or autonomous vehicle development, commanding 10-20% higher salaries due to their interdisciplinary expertise.

For example, engineers trained in robotics and cybersecurity can design secure medical robots, addressing the growing demand for healthcare automation.

By launching startups or freelancing, end-to-end engineers can innovate independently, bypassing traditional corporate structures and sharing more directly in the value they create.

Societal Contributions

Society benefits significantly from resilient, secure systems designed by end-to-end engineers. Secure robots and IoT devices protect critical infrastructure, such as manufacturing plants, hospitals, or transportation networks, from cyber threats.

For example, a secure robotic system in a hospital could ensure reliable operation of surgical robots, improving patient outcomes. Training in AI ethics ensures graduates prioritize societal good, mitigating risks like misinformation by designing platforms with robust content verification.

Accessible, accelerated programs promote equity, fostering diverse talent pools and countering job polarization, where AI enhances 25% of roles but automates others. By making education more inclusive, institutions can reduce disparities, ensuring underrepresented groups have access to high-demand careers in engineering.

Sustainability and Global Impact

Sustainability is a key benefit of end-to-end education. Engineers trained in holistic design can create energy-efficient systems, such as optimized robots for logistics or manufacturing, aligning with global environmental goals.

For instance, a robotic system designed to minimize energy consumption in a warehouse could reduce carbon emissions, contributing to sustainability efforts. Institutions adopting this model produce leaders who drive innovation and inclusive growth, addressing global challenges like climate change and digital equity.

Ethical Technology Development

End-to-end education fosters ethical awareness, equipping graduates to combat societal challenges like misinformation and system vulnerabilities. By integrating AI ethics and cybersecurity, graduates can design technologies that prioritize public good, ensuring platforms and systems are trustworthy and resilient. This approach aligns with the growing demand for ethical technology, as emphasized by many in the field who believe in the importance of critical thinking and responsibility in engineering.

Overcoming Challenges in Implementation

Faculty Engagement and Training

Transitioning to end-to-end programs may face resistance from faculty accustomed to traditional, siloed teaching. Institutions can address this through training workshops, collaborative research opportunities, and incentives like joint research grants.

For example, as mentioned above, faculty from computer science and mechanical engineering could co-develop courses on robotic system design, fostering interdisciplinary collaboration. Hiring experts in robotics, cybersecurity, and mechanical engineering ensures a capable teaching staff equipped to deliver comprehensive curricula.

Infrastructure Investment

The cost of infrastructure, such as robotics labs, 3D printing facilities, and cybersecurity simulation environments, poses a significant hurdle. Institutions can implement phased rollouts, starting with virtual simulations using tools like ROS (Robot Operating System) or Gazebo, which allow students to prototype systems without physical hardware. Grants from organizations like the NSF or partnerships with industry can offset costs, while open-source tools enhance accessibility, ensuring equitable access to training.

Curriculum and Accreditation

Redesigning curricula to meet accreditation standards, such as those set by ABET, requires a modular approach that integrates interdisciplinary skills while maintaining compliance. Institutions can pilot programs to test reforms, gradually incorporating modules like robotics or cybersecurity into existing curricula.

For example, a pilot program might introduce a robotics module in year two, allowing institutions to assess outcomes before full implementation. Regular reviews ensure curricula remain aligned with industry needs and accreditation requirements.

Building Stakeholder Support

Securing stakeholder support requires demonstrating the benefits of end-to-end education, including lower unemployment rates (potentially dropping below 3% with holistic training), higher graduate earnings (10-20% above traditional programs), and societal impact through secure, sustainable systems.

Engaging alumni, industry partners, and students in curriculum design builds trust and ensures relevance. For instance, advisory boards with industry representatives can provide insights into emerging trends, aligning programs with market demands.

Promoting Equity and Access

To ensure equitable access, institutions should leverage online platforms and modular degrees, reducing costs and reaching diverse populations. Partnerships with community colleges and vocational programs can create pathways for underrepresented groups, fostering an inclusive engineering workforce.

For example, online courses in robotics or cybersecurity can provide access to students in remote or underserved areas, while modular bootcamps allow working professionals to upskill efficiently.

Addressing Scalability

Scaling end-to-end programs requires strategic planning to balance quality and accessibility. Institutions can start with small cohorts, refining curricula based on feedback before expanding. Collaborations with other universities or online education platforms can share resources, reducing costs and increasing reach. For instance, a consortium of universities could develop shared virtual labs, enabling cost-effective training across institutions.

Conclusion: A Path Forward for Engineering Education

The case for end-to-end engineering education is compelling in a world shaped by AI, interconnected systems, and evolving societal needs. Traditional software engineering programs, with their focus on intermediary roles, must evolve to prepare graduates for the complexities of modern industries.

By integrating software development with robotics, mechanical engineering, and cybersecurity, institutions can produce versatile, innovative engineers who lead in a technology-driven world.

Reforms require bold action: transforming curricula to prioritize interdisciplinary skills, investing in faculty and infrastructure, fostering ethical industry partnerships, and promoting accessible pathways.

Case studies from MIT, Stanford, Vanderbilt, and global institutions like those in Nordic countries demonstrate the transformative potential of this approach, with graduates excelling in diverse roles, founding startups, and building resilient systems. Emerging programs at institutions like ETH Zurich and the University of Toronto further highlight the global applicability of end-to-end education.

Challenges like faculty resistance, infrastructure costs, and accreditation hurdles can be addressed through strategic planning, including phased rollouts, grants, and stakeholder engagement. Online platforms and partnerships with community colleges ensure equity, fostering a diverse talent pool that drives inclusive growth.

End-to-end education is not just an opportunity – it’s a necessity for equipping engineers to navigate a complex, technology-driven world. By embracing this model, institutions can empower the next generation to build innovative, secure, and sustainable systems that benefit society, ensuring a resilient and equitable future for all.

Further Resources:

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You’ve been grinding through tutorials. You've built a portfolio site, maybe deployed a few projects on GitHub. And now you're trying to land a job or join a team.

Then the interviews start.

Suddenly, people ask:

• "Are you familiar with Agile?"

• "Have you worked in a Scrum environment?"

• "What’s your experience with sprints?"

Cue the imposter syndrome. Because no one teaches this stuff in JavaScript 101.

This guide is for you.

I’ll help make the Scrum process – a very common way developers work together – make actual sense. I’ll walk you through the basics, but also tell you what developers actually do, how standups feel when you're new, and what’s expected of you when you’re no longer coding in a vacuum.

Let’s break it down.

Here’s what we’ll cover:

• What Even Is Scrum?

• The Three Roles in Scrum (and Who Does What)

• The Scrum Rhythm: What a Sprint Actually Looks Like

• Who attends the Ceremonies:

• Standups: Where You Talk Like a Human, Not a Robot

• Sprint Planning

• What’s a User Story and Why Does It Sound Like a Children’s Book?

• What Counts as “Done”? Definition of Done and Why It’s Important

• Demos, Retros, and Saying the Hard Things

• Tools You Might Encounter

• If You’re Preparing for a Job, Here’s What You Can Do

• Final Thoughts

What Even Is Scrum?

Scrum is not a tool. It’s not a software. It’s not some elite thing only PMs care about.

It’s a lightweight framework that helps software teams build things incrementally, together, in short focused cycles called sprints.

Scrum is used by everyone from FAANG teams to indie dev shops because it helps:

• Keep teams aligned

• Deliver working software fast

• Course-correct often

• Spot problems early (before they go nuclear)

It’s the opposite of the old-school “build for a year and pray it works” model.

The Three Roles in Scrum (and Who Does What)

Scrum officially defines three roles. Here's what that means in practice:

1. Product Owner (PO)

Think: Vision-holder. They decide what the team builds and why. A product owner:

• Writes user stories (think of these as feature requests written from a user’s point of view)

• Prioritizes the work

• Clarifies what success looks like

• Says “yes” or “not yet” to features

2. Scrum Master (SM)

Think: Air-traffic controller meets therapist. They make sure the process works. The are master Facilitators, like between Dev and PO’s. A Scrum Master:

• Facilitates meetings

• Removes blockers (“Your AWS access is stuck? I’ll escalate it.”)

• Coaches the team on Scrum practices

• Doesn’t manage people – manages flow

3. Developers (YOU!)

Think: Builders. You write code, test it, ship it, fix it, and improve it. You also:

• Break down stories into tasks

• Pick up work from the team board (like Jira or Trello)

• Communicate progress

• Demo what you’ve built at the end of the sprint

You might also work with designers, testers, or DevOps folks – but within Scrum, you’re all “developers” building a product together.

The Scrum Rhythm: What a Sprint Actually Looks Like

Image Source: https://www.invensislearning.com/blog/what-are-scrum-ceremonies/

Understanding the Scrum Cycle

So, what does it actually look like when a team uses Scrum to build software?

Let’s walk through a full sprint – not just the buzzwords, but what really happens when a group of humans tries to plan, build, review, and improve together. Think of this as your backstage pass to the rhythm of modern teamwork.

📦 Step 1: Build and Refine the Product Backlog

Before any coding starts, the team needs to agree on what they might build – not just this week, but in the near future too.

That’s where the Product Backlog comes in. This is a big, running list of everything the product might need – features, bug fixes, improvements, ideas, and maybe a few wild dreams. It’s like the wishlist for the product, but more organized (ideally).

The Product Owner is responsible for maintaining and prioritizing this list. They decide what’s most important to work on based on customer needs, business goals, and feedback.

But the PO doesn’t do this in isolation. Enter the Backlog Refinement meeting.

In these sessions, the Scrum Team – that’s the PO, the Scrum Master (SM), and the Developers – come together to:

• Review the most important upcoming items

• Clarify any vague or confusing parts of each task

• Break big items down into smaller, buildable pieces called user stories

• Estimate effort (how much time or complexity is involved for each story)

This meeting makes sure the team isn’t caught off guard in the sprint – that they understand the work ahead and can actually start sprinting when the time comes.

🧭 Step 2: Sprint Planning – What Are We Building This Time?

Now that we’ve got a solid backlog, it’s time to pick what to build right now.

At the start of each sprint (which typically lasts 1 to 4 weeks), the team holds a Sprint Planning meeting. This meeting sets the stage for the entire sprint – it’s like the huddle before the big game.

In Sprint Planning, the team:

• Reviews the top items from the backlog

• Discusses what can realistically be completed based on their availability and capacity

• Chooses a handful of these stories to commit to

• Defines a Sprint Goal – a simple statement that captures the purpose of this sprint

For example, the Sprint Goal might be:
🎯 “Allow users to reset their passwords.”

Every user story chosen should contribute to that goal. The collection of these stories becomes the Sprint Backlog – basically, the to-do list for the sprint.

So when we say:

“The team selects an ordered list of user stories to comprise the Sprint Backlog for the next sprint, which will be achievable to satisfy the Sprint Goal...”

We’re really just saying:
👉 “Pick a realistic number of important tasks that, if completed, will help us hit our target for the sprint.”

Not too vague. Not too ambitious. Just achievable and focused.

☀️ Step 3: Daily Standups – Stay in Sync

Now the sprint is underway! But how does everyone stay aligned and avoid working in silos?

That’s where the Daily Standup comes in. Every day – usually in the morning – the team has a quick check-in (about 15 minutes) where each person answers three questions:

1. What did I do yesterday?

2. What am I working on today?

3. Is anything blocking me? (that is, am I stuck?)

Example:

“Yesterday I set up the login API integration. Today I’ll work on the UI validation. I’m blocked on getting access to the staging database — may need help.”

These standups keep the team in sync and surface blockers early so they can be addressed quickly. They’re not about micromanaging or showing off. They’re about visibility and support.

📉 What’s a Sprint Burndown Chart?

You might hear your team mention a “burndown chart.” No, this isn’t about things going down in flames (hopefully).

A Sprint Burndown Chart is a graph that shows how much work is left in the sprint – day by day.

• The y-axis is the amount of work remaining (often measured in story points or tasks)

• The x-axis is the number of days left in the sprint

The line should ideally trend downward as work gets completed – hence “burning down.” If it flattens out or slopes up, that’s a red flag that the team might be stuck, behind schedule, or not updating the board.

Think of it as a visual heartbeat of the sprint. You can learn more via a practical example in this video.

🖥️ Step 4: Sprint Review – Show What You’ve Built

At the end of the sprint, the team holds a Sprint Review (also called a “demo”). This is where you show what was actually built during the sprint.

• The Developers demo working features – live, not just screenshots

• The Product Owner reviews whether the Sprint Goal was achieved

• Stakeholders may ask questions, give feedback, or suggest tweaks

This meeting isn’t just for show – it’s a feedback loop. It helps the team validate that what they built is useful, usable, and meets expectations. If changes are needed, those get added to the backlog for future sprints.

🔍 Step 5: Sprint Retrospective – Look Back to Move Forward

Once the review is done, the team shifts focus from what they built to how they worked together.

Enter the Sprint Retrospective – a meeting to reflect on the process, not the product.

The team discusses:

• ✅ What went well

• ❌ What didn’t go so well

• 🔁 What could be improved next time

This isn’t about pointing fingers. It’s about learning, adapting, and continuously improving how the team collaborates.

The Scrum Master often facilitates this meeting and helps turn feedback into action items for the next sprint. For example:

“We underestimated testing time. Next sprint, let’s budget for QA earlier.”

The best teams take retros seriously. Why? Because even if your code is perfect, your process needs tuning too – and small process changes often lead to big gains.

♻️ Scrum Is a Loop

Here’s the rhythm:

1. Plan the sprint

2. Check in daily

3. Build and demo the product

4. Reflect and improve

Then do it all over again – with slightly better coordination and slightly more trust each time.

It’s not about being fast. It’s about being intentional, consistent, and collaborative.

Example Sprint

Let’s say, for example, that your team does 4-week sprints. (Keep in mind that Sprints can differ by team, nature of product, release cycles, and so on.)

Here’s the rough beat:

Scrum works in loops. Every 2-4 weeks (depending on your cadence and sprint cycle), your team should have working, demo-able software to show for it – even if it’s small.

And no, it’s not about “speed.” It’s about consistency, communication, and collaboration.

Who attends the Ceremonies:

Now lets dive deeper and understand practically how each of these ceremonies work:

Standups: Where You Talk Like a Human, Not a Robot

So how does the team actually stay connected day to day? That’s where standups come in.

Every morning, your team meets briefly – usually on Zoom or in a circle – and you answer 3 questions:

1. What did I work on yesterday?

2. What will I work on today?

3. What’s blocking me? Any impediments?

Example:

"Yesterday I cleaned up the signup validation logic. Today I’m working on the email verification flow. I’m stuck on SendGrid config – might need help setting up credentials."

It’s not about impressing anyone. It’s about keeping everyone in sync. Some days you’ll say, “I spent the whole day debugging a CSS bug that turned out to be a semicolon.” That’s okay.

How does it work?

The Scrum Master gathers everyone in a huddle room, the PO and Dev Team included, and opens the the Standup. They are the facilitator of the ceremony. Everyone gets a chance to answer the 3 questions above (usually about 2-5 minutes each). It’s not a full report – it’s quick. When one person is done, they pass it on to someone else.

This ensures there is team cohesion and transparency.

Here is a video example of a standup.

Sprint Planning

The goal of the planning meeting is to answer the questions “What are we going to work on, and how are we going to do it?” It is critical for the team to have a shared goal and a shared commitment to this goal before beginning this ceremony.

Participants should:

• Measure growth

• Sync with the Scrum Master

• Sync with the Product Owner

Sprint planning happens just before the sprint starts, and usually lasts for an hour or two. In this meeting, the team goes over a collection of user stories and discuss, plan, measure, and prioritize. This is where they decide what is going to be in scope for their upcoming sprint cycle.

The Product Owner will have a prioritized view of things in the backlog. They work with the team on each object or customer experience. Together, as a group they go through and make calculations, deciding to what they can commit.

What’s a User Story and Why Does It Sound Like a Children’s Book?

So you might be wondering: how do you know what to work on? What to build? So much work, so little time? Thats where user stories come in.

In Scrum, teams don’t just write vague tasks like “code the login.” Instead, they write user stories – short, human-centered feature descriptions that describe what the user needs, why they need it, and what success looks like.

Here’s an example:

As a user, I want to be able to reset my password, so I can access my account if I forget it.

User stories are the scaffolding of teamwork. They’re written with empathy, not just efficiency. And each one comes with acceptance criteria – a checklist that clarifies what “done” actually means:

• A “Forgot Password” link is visible

• Clicking it shows a form

• An email gets sent with a reset link

Once a story is agreed upon, developers break it down into tasks, like “build form,” “hook into backend,” or “handle email validation.” It’s collaborative, not prescriptive. And user stories have priority so you know what’s the most important and what’s the least.

A helpful rule of thumb many teams use is the Gherkin-style "Given–When–Then" format:

• Given some initial context

• When an event occurs

• Then a specific outcome should happen

This ensures that everyone – devs, testers, and product owners – shares the same understanding of behavior and expectations.

Here is a great video example thats outlines how to draft effective and powerful user stories.

What Counts as “Done”? Definition of Done and Why It’s Important

Now you might be wondering – how do I know when a task is done and can be closed out?

The Definition of Done is a type of documentation in the form of a team agreement. The Definition of Done identifies the conditions that need to be achieved in order for the product to be considered done (as in potentially shippable).

This is how we know that we "did the thing right". Meaning, we built the correct level of quality into the product. The Definition of Done is not the same as the acceptance criteria, which are written by the product owner to help us know we did the "right thing".

Every team has a Definition of Done – it’s not just “I pushed code.” It could mean:

• Code is written

• Reviewed by a peer

• Merged into main

• Tested on staging

• Possibly deployed

This clarity keeps teams honest and accountable. No “it works on my machine” energy here. The DoD sets a quality bar. It prevents ambiguity, rework, and “it works on my machine” moments. When every card on the board passes the same finish line, teams move faster – and trust each other more.

Everyone should know what done is in a team. Either its Done as per DoD standards or its not.

Here is a beautiful video highlighting the impotence of DoD.

Demos, Retros, and Saying the Hard Things

Once you’ve built the product, then comes demos (showcasing your work) and retros (analysis as a team on what when well and what areas to improve on).

In the retro, everyone’s encouraged to speak up:

• What went well?

• What didn’t?

• What should we try next time?

Example:

“We missed a lot of stories because we didn’t account for testing time. Maybe we buffer next sprint with fewer tasks.”

The goal is not to blame – it’s to improve. Over time, this feedback loop becomes gold. The Scrum Master usually facilitates, collects feedback (via tools like Parabol, Miro, or sticky notes), and helps turn insights into actionable experiments for the next sprint.

Over time, retros become the heartbeat of team evolution.

Here is a video highlighting the importance of a Retro and Sprint Review.

🧠 Why Retrospection Matters More Than You Think

The Sprint Retrospective is more than just another meeting. It’s a mirror for your team – a safe, structured space to pause, reflect, and improve together.

You discuss:

✅ what went well

❌ what did not go well

🔁 what could we do better next time

Great teams don't just deliver great software, they continually deliver better ways of working.

This is why many experienced Scrum practitioners consider the retro to be the most important event in Scrum. Code is deployed once, but process improvements grow exponentially, sprint after sprint.

Tools You Might Encounter

Scrum doesn’t require software, but real-world teams use a variety of tools:

• Jira – Tracks sprints, issues, velocity

• Trello – Simple board, good for small teams

• Slack – Where standups often happen if async

• Notion / Confluence – Docs, retros, notes

• GitHub Projects – Lightweight planning for devs

Don’t worry if you’re not fluent in these yet. They’re tools – you’ll learn them on the job.

If You’re Preparing for a Job, Here’s What You Can Do

• ✍️ Practice writing user stories from your side projects

• 🧪 Run a mini-sprint: Plan your weekend project, set goals, and “review” it at the end

• 🤝 Contribute to an open-source project that uses Scrum or Agile workflows

• 🧾 Write about what you learned – maybe as a tutorial (hint hint)

Final Thoughts

So to recap, Scrum is a simple yet powerful way for teams to work together, stay organized, and deliver results quickly. It runs in short cycles called sprints, where the team plans what to do, checks in daily, shows their progress at the end, and reflects on how to improve.

The four key ceremonies – Sprint Planning, Daily Scrum, Sprint Review, and Sprint Retrospective – help keep everyone aligned and focused. With clear roles and regular feedback, Scrum makes it easier to handle changes, solve problems early, and continuously get better as a team.

But scrum isn’t a magic spell. It’s just a way for humans to build complex things – together – without falling apart.

You don’t need to be a Scrum Master. You don’t need a certification. But if you understand how sprints work, what’s expected of you, and how to show up to meetings with clarity and candor, you’re 10 steps ahead of most.

Scrum helps teams talk, plan, build, and learn. And now? You can too.

If you liked this, please do share. You never know who it might help out.

Until then…keep learning, unlearning, and relearning!!!

We first built the list in 2020 using a data-driven approach that we have used each year, including 2025. You can find the methodology below.

Methodology

First, we identified the leading world university rankings. Since we were interested in computer science specifically, he looked at their latest computer science rankings.

For the 2025 update, Suparn, my colleague used the same sources and methodology to find the top 60 universities offering online computer science courses.

Here are the rankings used:

• QS: World University Ranking 2025 — Computer Science & IT

• Times Higher Education: World University Ranking 2025 — Computer Science

Then, we crawled and scraped each ranking.

Now that we had some data, we used Jupyter with Python to process it. We combined the two rankings into one by averaging the position of each university in each ranking. Then, we filtered out the universities that didn’t offer online courses, and limited the list to the top 60 institutions — the cream of the crop.

Combined ranking: top 10 universities for studying computer science in 2024

As you can see above, we found that the top three institutions are #1 MIT,  #2 Oxford , and #3 Stanford.

Finally, we used the Class Central database, with its 250K online courses, to find all the computer science courses offered by the universities in the ranking.

The end result is a list of 1000+ online courses offered by the 60 best universities in the world for studying computer science in 2025.

Stats

• Enrollments range from 10 to over 13 million, with 14 courses exceeding 1 million enrollments

• Altogether, they have over 81M enrollments, with an average of 222K enrollments

• 921 courses are in English, 114 Chinese, 5 Spanish, and 3 French

• Together, they account for more than 78K reviews at Class Central, with an average of 216 reviews

• Average rating: 4.37 out of 5.0

• All these courses are free or can be audited for free

• 104 courses are beginner level, 111 are intermediate level, and 49 are advanced level.

More Courses

The full list is split into subjects. With over 1000 courses to pick from, I hope you find something you like. But if these aren’t enough, check out Class Central’s catalog of 250K online courses or our thematic collections:

• 2000+ Free Developer and IT Certifications

• Harvard Computer Science Courses with Free Certificate

• 40+ Free Certificates from Wolfram U

• 800+ Free Certificates: Master Tech & Soft Skills with CodeSignal Learn

• 10,000+ Free Courses from Tech Giants: Learn from Google, Microsoft, Amazon, and More

• 8000+ OpenCourseWare Courses from Top Institutions.

Computer Science (727)

• Harvard CS50 – Full Computer Science University Course from Harvard University ★★★★★(1012)

• CS50's Introduction to Computer Science from Harvard University ★★★★★(204)

• Artificial Intelligence from Massachusetts Institute of Technology ★★★★★(182)

• Introduction to Electrical Engineering and Computer Science I from Massachusetts Institute of Technology ★★★★★(58)

• Advanced Algorithms - COMPSCI 224 from Harvard University ★★★★★(35)

• Introduction to Artificial Intelligence from Stanford University ★★★★☆(31)

• Machine Learning with Python: from Linear Models to Deep Learning. from Massachusetts Institute of Technology ★★★☆☆(30)

• CS50's Introduction to Artificial Intelligence with Python from Harvard University ★★★★★(29)

• Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues from Georgia Institute of Technology ★★★★★(28)

• Introduction to Algorithms from Massachusetts Institute of Technology ★★★★★(25)

• CS50’s Computer Science for Business from Harvard University ★★★★★(21)

• Intro to Machine Learning from Stanford University ★★★★☆(20)

• Automata Theory from Stanford University ★★★★☆(20)

• Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★★(20)

• Computer Science 101 from Stanford University ★★★★☆(19)

• Cryptocurrency Engineering and Design from Massachusetts Institute of Technology ★★★★★(19)

• Human-Computer Interaction I: Fundamentals & Design Principles from Georgia Institute of Technology ★★★★★(15)

• How to Code: Simple Data from The University of British Columbia ★★★★☆(15)

• Design of Computer Programs from Stanford University ★★★★☆(14)

• Unix Tools: Data, Software and Production Engineering from Delft University of Technology ★★★★★(13)

• AI for Clinical Trials and Precision Medicine - Ruishan Liu from Stanford University ★★★★★(11)

• Learning from Data (Introductory Machine Learning course) from California Institute of Technology ★★★★★(10)

• CS50's Computer Science for Lawyers from Harvard University ★★★★★(8)

• Information and Communication Technology (ICT) Accessibility from Georgia Institute of Technology ★★★★☆(8)

• Reinforcement Learning from Brown University ★★★☆☆(8)

• Machine Learning Fundamentals from University of California, San Diego ★★★★☆(8)

• Introduction to Computer Science and Programming in Python from Massachusetts Institute of Technology ★★★★★(8)

• Machine Learning from Georgia Institute of Technology ★★★★☆(7)

• Introduction to Computer Vision from Georgia Institute of Technology ★★★★★(7)

• Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps from Georgia Institute of Technology ★★★★★(7)

• Performance Engineering of Software Systems from Massachusetts Institute of Technology ★★★★★(7)

• Convolutional Neural Networks for Visual Recognition from Stanford University ★★★★☆(7)

• Advanced Operating Systems from Georgia Institute of Technology ★★★★★(6)

• Introduction to Computer Architecture from Carnegie Mellon University ★★★★★(6)

• Computer Networking from Georgia Institute of Technology ★★★★☆(6)

• Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms from Georgia Institute of Technology ★★★★★(6)

• Machine Learning for Healthcare from Massachusetts Institute of Technology ★★★★★(6)

• MIT 6.824 Distributed Systems - Spring 2020 from Massachusetts Institute of Technology ★★★★★(6)

• Data Structures: An Active Learning Approach from University of California, San Diego ★★★★★(5)

• How to Code: Complex Data from The University of British Columbia ★★★★★(5)

• Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms from Georgia Institute of Technology ★★★★★(5)

• Distributed Systems from University of Cambridge ★★★★★(5)

• Electrical and Computer Engineering - Systems Programming and Concurrency from University of Waterloo ★★★★☆(5)

• Human-Computer Interaction II: Cognition, Context & Culture from Georgia Institute of Technology ★★★★★(4)

• 6.S191: Introduction to Deep Learning from Massachusetts Institute of Technology ★★★★☆(4)

• Introduction to Computational Thinking and Data Science from Massachusetts Institute of Technology ★★★★★(4)

• AI and Education Summit: Transforming Learning and Workforce Readiness from Stanford University ★★★★★(4)

• Software Architecture & Design from Georgia Institute of Technology ★★★★★(3)

• Language, Proof and Logic from Stanford University ★★★★☆(3)

• Machine Learning: Unsupervised Learning from Brown University ★★★☆☆(3)

• Algorithmic Design and Techniques from University of California, San Diego ★★★★☆(3)

• Using Electronic Health Records for Better Care from Stanford University ★★★★★(3)

• Design & AI - Envisioning AI-Augmented Products from Stanford University ★★★★★(3)

• Stanford Seminar - Security and the Software Defined Network from Stanford University ★★★★☆(3)

• Stanford Seminar - Self-Driving Cars for Everyone from Stanford University ★★★★★(3)

• Stanford Seminar - Practical Blockchain Applications - Steven Pu from Stanford University ★★★★★(3)

• Stanford Webinar - IOT: From Smart Sensors to Smart Networks from Stanford University ★★★★★(3)

• Stanford Seminar - Gender Disparities in Software Engineering from Stanford University ★★★★☆(3)

• Introduction to Operating Systems from Georgia Institute of Technology ★★★★★(2)

• Human-Computer Interaction IV: Evaluation, Agile Methods & Beyond from Georgia Institute of Technology ★★★★★(2)

• Deep Learning for Natural Language Processing from University of Oxford ★★★★★(2)

• Software Engineering: Introduction from The University of British Columbia ★★★☆☆(2)

• Knowledge-Based AI: Cognitive Systems from Georgia Institute of Technology ★★★☆☆(2)

• The Quantum Internet and Quantum Computers: How Will They Change the World? from Delft University of Technology ★★★☆☆(2)

• 6.S094: Deep Learning for Self-Driving Cars from Massachusetts Institute of Technology ★★★★☆(2)

• Introduction to Algorithms (SMA 5503) from Massachusetts Institute of Technology ★★★★★(2)

• Introduction to Computer Science and Programming (Fall 2008) from Massachusetts Institute of Technology ★★★★★(2)

• Stanford - Human-Computer Interaction Seminar Series from Stanford University ★★★★★(2)

• How to Compute with Schrödinger's Cat - An Introduction to Quantum Computing from Stanford University ★★★★☆(2)

• Web3 Considered - Possible Futures for Decentralization and Digital Ownership from Stanford University ★★★★☆(2)

• Race, Technology, and Algorithmic Bias - Vision and Justice from Harvard University ★★★★★(2)

• Deep Learning Lecture Series from University College London ★★★★★(2)

• Towards Unsupervised Biomedical Image Segmentation Using Hyperbolic Representations - Jeffrey Gu from Stanford University ★★★★☆(2)

• Creating Human-Computer Partnerships - Stanford Seminar from Stanford University ★★★★★(2)

• Stanford Seminar - Decision Transformer: Reinforcement Learning via Sequence Modeling from Stanford University ★★★★★(2)

• Computability, Complexity & Algorithms from Georgia Institute of Technology ★★★★★(1)

• High Performance Computer Architecture from Georgia Institute of Technology ★★★★★(1)

• Health Informatics: A Current and Historical Perspective from Georgia Institute of Technology ★★★★★(1)

• Human-Computer Interaction III: Ethics, Needfinding & Prototyping from Georgia Institute of Technology ★★★★★(1)

• Compilers from Stanford University ★★★★★(1)

• Introduction to Graduate Algorithms from Georgia Institute of Technology ★★★★★(1)

• Software Construction: Object-Oriented Design from The University of British Columbia ★★☆☆☆(1)

• Introduction to Scientific Machine Learning from Purdue University ★★★★☆(1)

• Software Construction: Data Abstraction from The University of British Columbia ★★★☆☆(1)

• Machine Learning for Semiconductor Quantum Devices from Delft University of Technology ★★★★☆(1)

• Data Structures and Algorithm Design Part II | 数据结构与算法设计(下) from Tsinghua University ★★★★★(1)

• Introduction to Computational Science and Engineering from Massachusetts Institute of Technology ★★★★☆(1)

• Nature, in Code: Biology in JavaScript from École Polytechnique Fédérale de Lausanne ★★★☆☆(1)

• Introduction to Algorithms from Massachusetts Institute of Technology ★★★★☆(1)

• Mathematics for Computer Science from Massachusetts Institute of Technology ★★★★☆(1)

• Design and Analysis of Algorithms from Massachusetts Institute of Technology ★★★★★(1)

• Introduction to Computer Science and Programming (Spring 2011) from Massachusetts Institute of Technology ★★★★★(1)

• Boltzmann Law: Physics to Computing from Purdue University ★★★★☆(1)

• Deep Learning for Computer Vision from University of Michigan ★★★★★(1)

• Quantum Computer Systems Design III: Working with Noisy Systems from The University of Chicago ★★★★☆(1)

• Understanding the World Through Data from Massachusetts Institute of Technology ★★★★☆(1)

• Learning and Memory from Yale University ★★★★★(1)

• Beyond Cryptocurrency: Blockchain for the Real World from Stanford University ★★★★☆(1)

• Dive Into the World of Blockchain: Principles, Mechanics, and Tokens from University of Toronto ★★★☆☆(1)

• Rethinking the AI-UX Boundary for Designing Human-AI Experiences from Stanford University ★★★★★(1)

• How ChatGPT and Generative AI Will Shape the Future of Work from Stanford University ★☆☆☆☆(1)

• Stanford Seminar - How Not to Generate Random Numbers from Stanford University ★★★★★(1)

• Stanford Seminar - Building the Smartest and Open Virtual Assistant to Protect Privacy - Monica Lam from Stanford University ★★★★★(1)

• Tuning GPT-3 on a Single GPU via Zero-Shot Hyperparameter Transfer from Massachusetts Institute of Technology ★★★★★(1)

• MIT: Recurrent Neural Networks from Alexander Amini ★★★★★(1)

• Wireless Above 100GHz from New York University (NYU) ★★★☆☆(1)

• Generalization and Personalization in Federated Learning - Karan Singhal from Stanford University ★★★★★(1)

• MedAI- Graph-Based Modeling in Computational Pathology - Siyi Tang from Stanford University ★★★★★(1)

• Synthetic Data Sets for the Financial Industry from Stanford University ★★★★☆(1)

• Learning Controllers - From Engineering to AGI from Massachusetts Institute of Technology ★★★★☆(1)

• Stanford Seminar - Google's Multilingual Neural Machine Translation System from Stanford University ★★★★★(1)

• Deep Generative Modeling from Alexander Amini ★★★★★(1)

• Regulatory Evaluation of Image Processing Software Devices from Yale University ★★★★★(1)

• Edge Computing in Autonomous Vehicles - Panel Discussion from Stanford University ★★★★☆(1)

• Finding Fairness - Incorporating Societal Values in Computer Algorithms from Harvard University ★★★☆☆(1)

• Exploring the Implications of Machine Learning for Cognitive Disabilities from Stanford University ★★★★★(1)

• AI in Molecular Imaging from Yale University ★★★★★(1)

• Stanford Seminar - Toward Scalable Autonomy - Aleksandra Faust from Stanford University ★★★★☆(1)

• Pioneering the Science of Information - IBM's Role in Shaping Information Technology from University of Melbourne ★★★★★(1)

• The Soul of a New Machine - Rethinking the Computer from Stanford University ★★★★☆(1)

• Data Compression I - Lecture 2: Prefix Free Codes from Stanford University ★★★★★(1)

• Feedback Control from Pixels from Massachusetts Institute of Technology ★★★☆☆(1)

• Visualization for Machine Learning - Google Brain from Alexander Amini ★★★★★(1)

• The Information Age and The Age of Creation from Stanford University ★★★★★(1)

• Algorithms: Design and Analysis, Part 1 from Stanford University

• Cyber-Physical Systems Design & Analysis from Georgia Institute of Technology

• Artificial Intelligence from Georgia Institute of Technology

• Introduction to Quantum Computing from The University of British Columbia

• Human-Computer Interaction from Georgia Institute of Technology

• Hacker Tools from Massachusetts Institute of Technology

• High Performance Computing from Georgia Institute of Technology

• Programming & Data Structures from Columbia University

• AI for Teacher Assistance from Georgia Institute of Technology

• Algorithms: Design and Analysis, Part 2 from Stanford University

• Introduction to Blockchain Technology and Applications from University College London

• Machine Learning and AI with Python from Harvard University

• AI skills: Introduction to Unsupervised, Deep and Reinforcement Learning from Delft University of Technology

• Health Informatics: Data and Interoperability Standards from Georgia Institute of Technology

• Development and Applications of Germanium Quantum Technologies from Delft University of Technology

• Ethics in AI Design from Delft University of Technology

• Computer Applications of Artificial Intelligence and e-Construction from Purdue University

• AI skills for Engineers: Supervised Machine Learning from Delft University of Technology

• AI at the Edge on Arm: Understanding and Deploying LLMs for Mobile Devices from Arm Education

• Compilers: Theory and Practice from Georgia Institute of Technology

• Image Processing and Analysis for Life Scientists from École Polytechnique Fédérale de Lausanne

• Assessment Design with AI from Georgia Institute of Technology

• Introduction to Deep Learning from Purdue University

• Chatbots for Instruction from Georgia Institute of Technology

• Complex Problem Structuring: a Socio-Technical Perspective from Delft University of Technology

• Machine Learning from Georgia Institute of Technology

• Health Informatics: The Cutting Edge from Georgia Institute of Technology

• Computer Graphics II: Rendering from University of California, San Diego

• Computer Graphics from University of California, San Diego

• IoT Systems and Industrial Applications with Design Thinking from École Polytechnique Fédérale de Lausanne

• Introduction to Quantum Computing for Everyone from The University of Chicago

• Statistical Machine Learning from Carnegie Mellon University

• Fundamentals of Quantum Information from Delft University of Technology

• Applied Quantum Computing I: Fundamentals from Purdue University

• Accessible Gamification from Georgia Institute of Technology

• Applied Quantum Computing III: Algorithm and Software from Purdue University

• Data Structures Fundamentals from University of California, San Diego

• Computer Vision for Embedded Systems from Purdue University

• Graph Algorithms from University of California, San Diego

• Self-Driving Cars with Duckietown from ETH Zurich

• Machine Learning Use Cases in Finance from Université de Montréal

• Quantum Detectors and Sensors from Purdue University

• Fundamentals of TinyML from Harvard University

• Introduction to Quantum Computing for Everyone 2 from The University of Chicago

• Applications of Machine Learning in Plant Science from Cornell University

• Deep Learning Essentials from Université de Montréal

• Deploying TinyML from Harvard University

• Applied Quantum Computing II: Hardware from Purdue University

• MLOps for Scaling TinyML from Harvard University

• AI in Healthcare. Hype or Help? from KU Leuven University

• Quantum Communication and the Quantum Network Explorer from Delft University of Technology

• Quantum-safe Digital Infrastructures: Technical Challenges and Solutions from Delft University of Technology

• Applications of TinyML from Harvard University

• Quantum Computer Systems Design I: Intro to Quantum Computation and Programming from The University of Chicago

• Introduction à l'éthique de l’IA from Université de Montréal

• Bias and Discrimination in AI from Université de Montréal

• Explore Trading and Lending in Decentralized Finance from University of Toronto

• Quantum Computer Systems Design II: Principles of Quantum Architecture from The University of Chicago

• Data Structures and Algorithm Design Part I | 数据结构与算法设计(上) from Tsinghua University

• Quantum-safe Digital infrastructures: Challenges and Solutions for Governance from Delft University of Technology

• AI in Practice: Preparing for AI from Delft University of Technology

• LAFF-On Programming for High Performance from The University of Texas at Austin

• Modern Distributed Systems from Delft University of Technology

• GT - Refresher - Advanced OS from Georgia Institute of Technology

• Biais et discrimination en IA from Université de Montréal

• Techniques d’intelligence artificielle : des fondements aux applications from Université de Montréal

• The Society of Mind from Massachusetts Institute of Technology

• Introduction à l'apprentissage profond from Université de Montréal

• Advanced Data Structures from Massachusetts Institute of Technology

• NP-Complete Problems from University of California, San Diego

• String Processing and Pattern Matching Algorithms from University of California, San Diego

• Blockchain Technology and the Future of FinTech from University of Toronto

• Quantum Hardware and its Applications with Quantum Inspire from Delft University of Technology

• 计算几何 | Computational Geometry from Tsinghua University

• LAFF – On Programming for Correctness from The University of Texas at Austin

• L'essentiel de l'apprentissage profond from Université de Montréal

• Vision artificielle et exploitation intelligente des ressources naturelles from Université de Montréal

• Les coulisses des systèmes de recommandation from Université de Montréal

• Cryptocurrency Engineering and Design - Spring 2018 from Massachusetts Institute of Technology

• AI to Understand and Connect People from KU Leuven University

• AI in Practice: Applying AI from Delft University of Technology

• Big Data Machine Learning | 大数据机器学习 from Tsinghua University

• Introduction to Computers - Stanford University from Stanford University

• Navigating complex health data challenges from University of Cambridge

• Recommender Systems: Behind the Screen from Université de Montréal

• Navigating Decentralized Derivatives and Governance in Blockchain from University of Toronto

• How You Can Use ChatGPT to Increase Your Creative Output from Stanford University

• Optimization: principles and algorithms - Network and discrete optimization from École Polytechnique Fédérale de Lausanne

• Stanford CS229 - Machine Learning Full Course Taught by Andrew Ng - Autumn 2018 from Stanford University

• Deep Learning in Life Sciences - Spring 2021 from Massachusetts Institute of Technology

• 算法基础 | Fundamental Algorithms from Peking University

• Optimization: principles and algorithms - Unconstrained nonlinear optimization from École Polytechnique Fédérale de Lausanne

• Introduction to Computational Thinking from Massachusetts Institute of Technology

• Artificial Intelligence - Principles and Techniques - Autumn 2019 from Stanford University

• Introduction to EECS I from Massachusetts Institute of Technology

• The Battlecode Programming Competition from Massachusetts Institute of Technology

• Geometric Folding Algorithms: Linkages, Origami, Polyhedra from Massachusetts Institute of Technology

• AI and Law: Implications for Society and Legal Systems from Stanford University

• Artificial Intelligence for Business Leaders from Stanford University

• Programming for the Puzzled (January IAP 2018) from Massachusetts Institute of Technology

• Stanford Seminar - Software-Defined Networking at the Crossroads from Stanford University

• Information Technology as an Integrating Force in Manufacturing from Massachusetts Institute of Technology

• Multicore Programming Primer from Massachusetts Institute of Technology

• 理论计算机科学基础 | Introduction to Theoretical Computer Science from Peking University

• Artificial Intelligence - Current and Future Paradigms and Implications from Stanford University

• Theory of Computation, Fall 2020 from Massachusetts Institute of Technology

• Cryptocurrencies and Blockchains - The Science Behind the Technology from Stanford University

• Introduction to CS and Programming using Python from Massachusetts Institute of Technology

• Stanford Seminar - Deep Learning for Medical Diagnoses from Stanford University

• Stanford Seminar - Transformers in Language: The Development of GPT Models Including GPT-3 from Stanford University

• Introduction to the Theory of Computing - Stanford from Stanford University

• Stanford Seminar - The FATE of AI Ethics, Anna Bethke from Stanford University

• Stanford Seminar - Designing Assistive Technologies for Agency from Stanford University

• Algorithmic Lower Bounds: Fun with Hardness Proofs from Massachusetts Institute of Technology

• Software-Centric Visible Light Communication for the Internet of Things from Stanford University

• How to Use ChatGPT to Increase Your Creative Output - Part Two from Stanford University

• MIT Introduction to Deep Learning from Alexander Amini

• Stanford CS330: Deep Multi-Task and Meta Learning from Stanford University

• Machine Learning Course from California Institute of Technology

• Using Data for Increased Realism with Haptic Modeling and Devices from Stanford University

• Stanford Seminar - Deep Learning in Speech Recognition from Stanford University

• The Future of Blockchain and Cryptocurrencies - Dan Boneh from Stanford University

• AI in the Workplace: Rethinking Skill Development from Stanford University

• AI Bias and Fairness from Alexander Amini

• Stanford Seminar - Natural Language Processing for Conversational Interfaces from Stanford University

• Stanford Seminar - Bugs in Crypto Implementations from Stanford University

• The Artful Design of Interactive AI Systems from Stanford University

• Artificial Intelligence Planning from University of Edinburgh

• Deep Generative Modeling from Alexander Amini

• MIT: Reinforcement Learning from Alexander Amini

• How AI Is Turning Your Smartphone Into the Swiss Army Knife of Clinical Diagnostics from Yale University

• Stanford CS234: Reinforcement Learning - Winter 2019 from Stanford University

• Stanford Seminar - Representation Learning for Autonomous Robots, Anima Anandkumar from Stanford University

• Stanford Seminar - Creating Interfaces with Rich Physical Properties Through Digital Fabrication from Stanford University

• How Machine Learning is Changing Software from Stanford University

• AI in Healthcare from Alexander Amini

• Machine Learning with Graphs - Fall 2019 from Stanford University

• Stanford Seminar - Enabling NLP, Machine Learning, and Few-Shot Learning Using Associative Processing from Stanford University

• Smart Physical Systems from the Standpoint of an AI Company from Stanford University

• Stanford Seminar - Can the Brain Do Back-Propagation? from Stanford University

• Frontiers of Medical AI - Therapeutics and Workflows from Stanford University

• Agentic AI: A Progression of Language Model Usage from Stanford University

• Data Augmentation for Image-Based Reinforcement Learning from Massachusetts Institute of Technology

• MIT EI Seminar - Phillip Isola - Emergent Intelligence- Getting More Out of Agents Than You Bake In from Massachusetts Institute of Technology

• MIT 6.S191 - Automatic Speech Recognition from Alexander Amini

• Theory of Computation from Massachusetts Institute of Technology

• 算法设计与分析(高级) | Advanced Design and Analysis of Algorithms from Peking University

• Deep Maths - Machine Learning and Mathematics from University of Oxford

• Forecasting and Aligning AI - Jacob Steinhardt from Stanford University

• Deep Learning for Symbolic Mathematics - Guillaume Lample & Francois Charton from Stanford University

• Stanford Seminar - Neural Networks on Chip Design from the User Perspective from Stanford University

• Solana Larsen- Who Has Power Over AI? from Stanford University

• Stanford Seminar - Rebooting the Internet from Stanford University

• Federated Learning in Medicine - Breaking Down Silos to Advance Medical Research from Stanford University

• Large Language Models - Will They Keep Getting Bigger? from Massachusetts Institute of Technology

• Jacob Andreas - Natural Language Explanations of Deep Networks from Massachusetts Institute of Technology

• Diverse Data and Efficient Algorithms for Robot Learning from Massachusetts Institute of Technology

• Towards AI for 3D Content Creation from Alexander Amini

• 6.036 Introduction to Machine Learning from Massachusetts Institute of Technology

• The Creativity Code - Marcus du Sautoy from University of Oxford

• Stanford Seminar - Training Classifiers with Natural Language Explanations from Stanford University

• Stanford Seminar - New Golden Age for Computer Architecture - John Hennessy from Stanford University

• Stanford Seminar - Applying Theory to Practice and Practice to Theory from Stanford University

• Weakly-Supervised, Large-Scale Computational Pathology for Diagnosis and Prognosis - Max Lu from Stanford University

• Federated Hyperparameter Tuning - Challenges, Baselines, and Connections from Stanford University

• Stanford Seminar - What's Next for Blockchain in China? from Stanford University

• Artificial Intelligence - The Future of Medicine and Health Care Is Here from Stanford University

• Neurosymbolic AI from Alexander Amini

• Beyond Deep Learning - Learning+Reasoning from Alexander Amini

• Sequence Modeling with Neural Networks from Alexander Amini

• Stanford Seminar - Lenia- Biology of Artificial Life, Bert Wang-Chak Chan from Stanford University

• Stanford Seminar - Scalable Intelligent Systems Build and Deploy by 2025 from Stanford University

• Stanford Seminar - Erudite: Prototype System for Computational Intelligence from Stanford University

• Stanford Seminar - Safe Passwords Made Easy to Use from Stanford University

• Computational Memory - A Stepping-Stone to Non-Von Neumann Computing? from Stanford University

• Stanford Seminar - The Quest for Low Storage Latency Changes Everything from Stanford University

• Stanford Seminar - The REX Neo Architecture - An Energy Efficient New Processor Architecture from Stanford University

• Stanford Seminar - Citadel of One- Individuality and the Rise of the Machines, Suzanne Sadedin from Stanford University

• Dynamic Code Optimization and the NVIDIA Denver Processor from Stanford University

• Stanford Seminar - Instruction Sets Should Be Free- The Case for RISC-V from Stanford University

• Multimodal Opportunistic Risk Assessment for Ischemic Heart Disease - Juan Manuel from Stanford University

• Rethinking Architecture Design for Data Heterogeneity in FL - Liangqiong Qu from Stanford University

• Few-Shot Chest X-Ray Diagnosis Using Clinical and Literature Images from Stanford University

• Data-Efficient AI for Accelerating MRI Acquisition from Stanford University

• Hacking AI - Security & Privacy of Machine Learning Models from Stanford University

• Stanford CS229 - Machine Learning - Summer 2019 - Kernel Methods & Support Vector Machine from Stanford University

• Create a Better User Experience Through AI - Michael Bernstein from Stanford University

• NVIDIA GPU Computing - A Journey from PC Gaming to Deep Learning from Stanford University

• AI Transforms Health Care - The Future of Medicine and Health Care from Stanford University

• Computer Science and IT at the University of Melbourne - Faculty of Engineering and IT Overview from University of Melbourne

• CS50 2012 - Week 3 Continued: Search Algorithms and Sorting Techniques from Harvard University

• EI Seminar - Recent Papers in Embodied Intelligence from Massachusetts Institute of Technology

• Computational Principles Underlying the Learning of Sensorimotor Repertoires from Massachusetts Institute of Technology

• Intuitive Reasoning as Unsupervised Neural Generation from Massachusetts Institute of Technology

• The Lottery Ticket Hypothesis - Michael Carbin from Massachusetts Institute of Technology

• Recurrent Neural Networks and Transformers from Alexander Amini

• MIT: Introduction to Deep Learning from Alexander Amini

• Introduction to Deep Learning from Alexander Amini

• Advanced Topics in Cryptography from Massachusetts Institute of Technology

• Stanford Seminar - Failures & Where to Find Them: Considering Safety as a Function of Structure from Stanford University

• Denoising Diffusion Models for Denoising Diffusion MRI - Tiange Xiang from Stanford University

• Real-Time Seizure Detection Using EEG - Hyewon Jeong from Stanford University

• Deep Learning Methods for Electrocardiograms and Echocardiograms from Stanford University

• Towards Generalist Imaging Using Multimodal Self-Supervised Learning - Mars Huang from Stanford University

• The New Role of Physics Simulation in AI from Stanford University

• Stanford Seminar - Human-AI Interaction Under Societal Disagreement from Stanford University

• Inequality in Healthcare: Using AI and Data Science to Reduce Disparities from Stanford University

• Stanford Seminar - Taraxa.io: A Globalized Blockchain Protocol Startup from Stanford University

• Cybersecurity in the Modern Era - Zero Knowledge Proofs Explained from Stanford University

• Generative AI for Multimodal Biomedicine from Stanford University

• Building Embodied Autonomous Agents from Massachusetts Institute of Technology

• Machine Learning for Scent from Alexander Amini

• Introduction to Deep Learning from Alexander Amini

• Convolutional Neural Networks from Alexander Amini

• Convolutional Neural Networks from Alexander Amini

• Introduction to Deep Learning - MIT 2018 from Alexander Amini

• Improved Ultrasound Image Formation - Domain Adaptation With No Data from Yale University

• ChatGPT pour tous from Université de Montréal

• Stanford Seminar - Leela: A Semantic Intelligent Agent from Stanford University

• Stanford Seminar - An Architect's Point of View on Emerging Technologies from Stanford University

• Stanford Seminar - Dialog Markets from Stanford University

• Stanford Seminar - Time Traveling Hardware and Software Systems from Stanford University

• Stanford Seminar - Concepts and Questions as Programs from Stanford University

• Voices in the Code - A Story About People, Their Values, and the Algorithm They Made from Stanford University

• Against Ethical Robots with Ron Chrisley from Stanford University

• Stanford Seminar - Is Google Search Dying? from Stanford University

• The Race for Technological Supremacy - Emerging Technologies and Global Implications from Stanford University

• Stanford Seminar - Fastai: A Layered API for Deep Learning from Stanford University

• Deep Learning - Class Introduction and Logistics - Lecture 1 from Stanford University

• How is AI Evolving? A Look at the Startup Landscape in the U.S. and Asia from Stanford University

• Entrepreneurial Lessons from Self-Driving Cars from Stanford University

• Stanford Seminar - Transformers United: Neuroscience-Inspired Artificial Intelligence from Stanford University

• Overview of Transformers - Stanford CS25: Lecture 1 from Stanford University

• Language Models as Temporary Training Wheels to Facilitate Learning from Stanford University

• Can You Trust a Computer Algorithm - How and Why from University of Melbourne

• Recent Papers in Embodied Intelligence I from Massachusetts Institute of Technology

• Deep Learning New Frontiers from Alexander Amini

• Deep Learning Limitations and New Frontiers from Alexander Amini

• Computer Vision Meets Social Networks from Alexander Amini

• MIT: Deep Reinforcement Learning from Alexander Amini

• MIT 6.S191 - Deep Learning Limitations and New Frontiers from Alexander Amini

• Modeling Prior Information to Guide Image Reconstruction in Radiation Therapy from Yale University

• Ideas for a Complex World - Anna Seigal from University of Oxford

• GGP Course Videos from Stanford University

• Stanford Seminar - Persistent and Unforgeable Watermarks for Deep Neural Networks from Stanford University

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• CS50's Mobile App Development with React Native from Harvard University ★★★★★(2)

• Database Systems Concepts & Design from Georgia Institute of Technology ★★★★☆(1)

• Automated Software Testing: Model and State-based Testing from Delft University of Technology ★★★★★(1)

• Using JavaScript, JQuery, and JSON in Django from University of Michigan ★★★★★(1)

• Databases: Semistructured Data from Stanford University ★★★★★(1)

• Programación para todos (empezando con Python) from University of Michigan ★★★★☆(1)

• Seven Databases in Seven Weeks - Fall 2014 from Carnegie Mellon University ★★★★★(1)

• Advanced Database Systems - Spring 2019 from Carnegie Mellon University ★★★★★(1)

• Stanford Seminar - Optional Static Typing for Python from Stanford University ★★★★★(1)

• Advanced Database Systems - Spring 2017 from Carnegie Mellon University ★★★★☆(1)

• Rocket- Securing the Web at Compile-Time from Stanford University ★★★★★(1)

• Stanford Seminar - KUtrace from Stanford University ★★★★★(1)

• Stop User Errors - Mastering Usability Engineering in Medical Software Course from Yale University ★★★☆☆(1)

• Automated Software Testing: Unit Testing, Coverage Criteria and Design for Testability from Delft University of Technology

• Quantitative Methods for Biology from Harvard University

• CS 6340: Software Analysis & Testing from Georgia Institute of Technology

• Databases: Advanced Topics in SQL from Stanford University

• Database Design and Basic SQL in PostgreSQL from University of Michigan

• R Programming Fundamentals from Stanford University

• CS50's Introduction to Programming with R from Harvard University

• Creating Virtual Reality (VR) Apps from University of California, San Diego

• How Virtual Reality Works from University of California, San Diego

• Introduction to Object-Oriented Programming with Java III: Exceptions, Data Structures, Recursion, and GUIs from Georgia Institute of Technology

• Databases: Modeling and Theory from Stanford University

• Using JavaScript and JSON in Django from University of Michigan

• Mobile Application Experiences from Massachusetts Institute of Technology

• UML Class Diagrams for Software Engineering from KU Leuven University

• Worldbuilding for Video Games from The University of British Columbia

• Real-Time Audio Signal Processing in Faust from Stanford University

• JSON and Natural Language Processing in PostgreSQL from University of Michigan

• Global Software Development from Delft University of Technology

• Statistical Learning with Python from Stanford University

• Databases: OLAP and Recursion from Stanford University

• Introduction to Object-Oriented Programming with Java II: Object-Oriented Programming and Algorithms from Georgia Institute of Technology

• Introduction to Object-Oriented Programming with Java I: Foundations and Syntax Basics from Georgia Institute of Technology

• Intermediate PostgreSQL from University of Michigan

• Minecraft, Coding and Teaching from University of California, San Diego

• Programming Reactive Systems from École Polytechnique Fédérale de Lausanne

• Building Web Applications in Django from University of Michigan

• Database Architecture, Scale, and NoSQL with Elasticsearch from University of Michigan

• Web Application Technologies and Django from University of Michigan

• Introduction à la science des données sociales avec R from Université de Montréal

• Django Features and Libraries from University of Michigan

• MATLAB et Octave pour débutants from École Polytechnique Fédérale de Lausanne

• Estructuras de Datos con Python from University of Michigan

• C++ Programming | C++程序设计 from Peking University

• Java程序设计 | Java Programming from Peking University

• CS193p - Developing Apps for iOS from Stanford University

• Virtual Reality from University of Illinois at Urbana-Champaign

• How to Design Addictive Games from Stanford University

• iPhone Application Development Spring 2020 from Stanford University

• 程序设计基础 from Peking University

• Stanford Seminar - Programming Should Be More Than Coding from Stanford University

• Stanford Seminar - Stories from CoCoLab: Probabilistic Programs, Cognitive Modeling, & Smart Web Pages from Stanford University

• Ciencia de Datos: Fundamentos de R from Harvard University

• Extended Reality for Everybody - Michael Nebeling from Stanford University

• CS 241: System Programming from University of Illinois at Urbana-Champaign

• C++语言程序设计基础 from Tsinghua University

• Stanford Seminar - Accessible Virtual Reality for People with Limited Mobility from Stanford University

• Haskell: Lecture notes and assignments from University of Pennsylvania

• Learn Computer Science Online from University of Illinois at Urbana-Champaign

• Computer Language Engineering (SMA 5502) from Massachusetts Institute of Technology

• Advanced Database Systems - Spring 2016 from Carnegie Mellon University

• User Experience (UX) Design: Human Factors and Culture in Design from Tsinghua University

• Intro to Database Systems - Fall 2017 from Carnegie Mellon University

• Stanford Seminar - Mind Your State for Your State of Mind from Stanford University

• Making Teamwork an Objective Discipline - Sid Sijbrandij CEO & Chairman of GitLab from Stanford University

• Stanford Seminar - Concatenative Programming- From Ivory to Metal from Stanford University

• Programming Languages - The Fundamental Tools of the Computer Age from University of Melbourne

• Intro to Database Systems - Fall 2018 from Carnegie Mellon University

• Advanced Database Systems - Spring 2018 from Carnegie Mellon University

• Paul's Disciples - Pseudepigraphic Letters to Colossians and Ephesians from Yale University

• LaTeX course from University of Amsterdam

• Intro to Database Systems - Fall 2021 from Carnegie Mellon University

• Advanced Database Systems - Spring 2020 from Carnegie Mellon University

• Hardware Accelerated Database Lectures - Fall 2018 from Carnegie Mellon University

• 程序设计基础 from Tsinghua University

• 学做小程序——基础篇 from Tsinghua University

• 计算机程序设计基础 from Tsinghua University

• Stanford Seminar - The Future of Edge Computing from an International Perspective from Stanford University

• Missing Semester IAP 2020 from Massachusetts Institute of Technology

• Structure and Interpretation of Computer Programs from Massachusetts Institute of Technology

• Internet Technology in Local and Global Communities from Massachusetts Institute of Technology

• Intro to Database Systems - Fall 2019 from Carnegie Mellon University

• Time Series Database Lectures from Carnegie Mellon University

• The Databaseology Lectures - Fall 2015 from Carnegie Mellon University

• PACS & AI – From Integration to Cloud from Yale University

• Java程序设计 from Tsinghua University

• Designing the Interactive Paper from Stanford University

• Stanford Seminar - PyWren - Pushing Microservices to Teraflops from Stanford University

• Living in Information Everywhere from Stanford University

• Wearing a VR Headset While Driving to Improve Vehicle Safety from Stanford University

• Master Code Management - Essential Revision Control Systems for Medical Software Course from Yale University

• Software Testing Essentials - Finding Bugs and Ensuring Quality in Medical Software - 8.3 from Yale University

• C++语言程序设计进阶 from Tsinghua University

• 学做小程序——实战篇：树洞小程序 from Tsinghua University

• 学做小程序--云开发篇：近义词小程序 from Tsinghua University

• Jeremy Bailenson: Your Mind on the Metaverse from Stanford University

• Stanford Seminar - TechFail: From Intersectional Inaccessibility to Inclusive Design from Stanford University

• Understanding the Utility of Haptic Feedback in Telerobotic Devices from Stanford University

• Stanford Seminar - I Forgot, I Invented Hypertext - Ted Nelson from Stanford University

• Edge Computing and the Evolution of AR-VR - Panel Discussion from Stanford University

• Stanford Seminar - Virtual & Mixed Reality for Security of Critical City-Scale Cyber-Physical Systems from Stanford University

• Graph Analysis of Russian Twitter Trolls Using Neo4j from Stanford University

• An Alternative to the American Way of Innovation from Stanford University

• Integrating Interactive Devices with the User's Body from Stanford University

• Beyond Benchmarks – Building a Science of AI Measurement from Stanford University

• VR/AR in IR - Mixed Reality in Medicine from Yale University

• JAVA程序设计进阶 from Tsinghua University

• Web前端攻城狮 from Tsinghua University

• 基于R语言的社会统计分析 from Tsinghua University

• Making the Invisible Visible - Observing Complex Software Dynamics from Stanford University

• Stanford Seminar - Robosion: Software Platform for Lifelike Humanoids from Stanford University

• From Flat to Phantasmal: Enhancing User Experiences Through Spatial Computing Advancements from Stanford University

• Essential Medical Software Verification and Testing Strategies - 8.5 from Yale University

• VC++面向对象与可视化程序设计（上）：Windows编程基础 from Tsinghua University

• VC++面向对象与可视化程序设计（下）：MFC编程基础 from Tsinghua University

• 汇编语言程序设计 from Tsinghua University

• 基于Linux的C++ from Tsinghua University

• JAVA程序设计进阶 from Tsinghua University

• 面向对象程序设计（C++） from Tsinghua University

• 游戏程序设计 from Tsinghua University

• C++编程训练营 from Tsinghua University

• 低时延网络挑战赛：从5G和边缘计算到互联网 from Tsinghua University

• 界面设计导论 from Tsinghua University

• 高级数据库系统 from Tsinghua University

• 游戏分析与评测 from Tsinghua University

Data Science (110)

• Fundamentals of Qualitative Research Methods from Yale University ★★★★★(187)

• The Analytics Edge from Massachusetts Institute of Technology ★★★★★(80)

• Next in Data Visualization - Michelle Borkin - Radcliffe Institute from Harvard University ★★★★☆(61)

• Python for Data Science from University of California, San Diego ★★★★☆(48)

• Probability and Statistics in Data Science using Python from University of California, San Diego ★★☆☆☆(32)

• Introduction to Computational Thinking and Data Science from Massachusetts Institute of Technology ★★★★★(30)

• Mining Massive Datasets from Stanford University ★★★★★(24)

• Statistics and R from Harvard University ★★★★☆(20)

• DCO042 - Python For Informatics from University of Michigan ★★★★★(14)

• Data Science: R Basics from Harvard University ★★★★★(12)

• Foundations of Data Analysis - Part 1: Statistics Using R from The University of Texas at Austin ★★★★☆(8)

• Algorithms for Big Data from Harvard University ★★★★★(8)

• Data Analysis Essentials from Imperial College London ★☆☆☆☆(7)

• Data Analytics at the Exascale for Free Electron Lasers Project from Stanford University ★★★★★(7)

• Data Science: Visualization from Harvard University ★★★★★(6)

• Computing for Data Analysis from Georgia Institute of Technology ★★★☆☆(6)

• Introduction to Analytics Modeling from Georgia Institute of Technology ★★★★☆(5)

• STAT115 2020 from Harvard University ★★★★★(4)

• The Analytics Edge (Spring 2017) from Massachusetts Institute of Technology ★★★★★(4)

• Stanford Seminar - Secure Data Science on the Internet of Things from Stanford University ★★★★☆(4)

• Causal Diagrams: Draw Your Assumptions Before Your Conclusions from Harvard University ★★★★★(3)

• Data Science: Building Machine Learning Models from Harvard University ★★★★☆(3)

• High-Dimensional Data Analysis from Harvard University ★★★★☆(3)

• Introduction to Data Science with Python from Harvard University ★★★★☆(3)

• Data Analysis: Statistical Modeling and Computation in Applications from Massachusetts Institute of Technology ★★★★★(2)

• Data Science: Probability from Harvard University ★★★★☆(2)

• Data Science: Wrangling from Harvard University ★★★★★(2)

• AI Skills for Engineers: Data Engineering and Data Pipelines from Delft University of Technology ★★★★☆(2)

• Stanford Seminar - When DNA Meets AI from Stanford University ★★★★★(2)

• Stanford Seminar - New Platforms for Development Solutions from Stanford University ★★★★★(2)

• Data Analytics in Health – From Basics to Business from KU Leuven University ★★★★★(1)

• Data Science: Productivity Tools from Harvard University ★★★★★(1)

• Data Science: Inference and Modeling from Harvard University ★★★★☆(1)

• Policy Analysis Using Interrupted Time Series from The University of British Columbia ★★★★★(1)

• STAT115 2018 from Harvard University ★★★★★(1)

• Big Data Analytics from California Institute of Technology ★★★☆☆(1)

• MIT Deep Learning in Life Sciences Spring 2020 from Massachusetts Institute of Technology ★★★★★(1)

• MIT CompBio Course Projects Fall 2019 from Massachusetts Institute of Technology ★★★★☆(1)

• Soccermatics - Could a Premier League Team One Day Be Managed by a Mathematician? from University of Oxford ★★★★★(1)

• Data and Visual Analytics from Georgia Institute of Technology

• Principles, Statistical and Computational Tools for Reproducible Data Science from Harvard University

• Data Science: Capstone from Harvard University

• CSE 8803 Special Topics: Big Data from Georgia Institute of Technology

• Introduction to Genomic Data Science from University of California, San Diego

• Foundations of Data Analytics from The Hong Kong University of Science and Technology

• Statistical Computing with R - a gentle introduction from University College London

• Data Analytics for Business from Georgia Institute of Technology

• Dynamic Programming: Applications In Machine Learning and Genomics from University of California, San Diego

• Algorithms and Data Structures Capstone from University of California, San Diego

• Graph Algorithms in Genome Sequencing from University of California, San Diego

• Big Data for Reliability and Security from Purdue University

• Data Creation and Collection for Artificial Intelligence via Crowdsourcing from Delft University of Technology

• Big Data Computing with Spark from The Hong Kong University of Science and Technology

• Advanced Big Data Systems | 高级大数据系统 from Tsinghua University

• Data Mining and Knowledge Discovery from The Hong Kong University of Science and Technology

• Mathematical Methods for Data Analysis from The Hong Kong University of Science and Technology

• Data Analysis: Building Your Own Business Dashboard from Delft University of Technology

• Big Data Analytics Using Spark from University of California, San Diego

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Big Data Technology Capstone Project from The Hong Kong University of Science and Technology

• Fondamentaux de la science des données from Université de Montréal

• Learning Time Series with Interventions from Massachusetts Institute of Technology

• Knowledge Inference and Structure Discovery for Education from University of Pennsylvania

• STAT115 from Harvard University

• Science des données et santé from Université de Montréal

• The impact of big data on healthcare from University of Cambridge

• Stanford Seminar - Theories of Inference for Visual Analysis from Stanford University

• Mining Online Data Across Social Networks from Stanford University

• Introduction to R and Geographic Information Systems (GIS) from Massachusetts Institute of Technology

• How to Analyze Research Data - Kristin Sainani from Stanford University

• Next in Data Visualization - Interactive Systems for Intelligence Augmentation from Harvard University

• Data Mining - The Tool of the Information Age from Stanford University

• Análisis de datos: Diseño y Visualización de Tableros from Delft University of Technology

• Stanford Seminar - Towards Theories of Single-Trial High Dimensional Neural Data Analysis from Stanford University

• Stanford Seminar - Big Data Is -At Least- Four Different Problems from Stanford University

• Stanford Seminar - TSAR - Anirudh Todi of Twitter from Stanford University

• Mathematics of Big Data and Machine Learning from Massachusetts Institute of Technology

• Data Analysis for Social Scientists from Massachusetts Institute of Technology

• Data Analysis for Social Scientists from Massachusetts Institute of Technology

• Stanford Seminar - Developing Design Spaces for Visualization - Tamara Munzner from Stanford University

• Stanford Seminar - Forecasting and Predicting the Future from Stanford University

• The End of Privacy from Stanford University

• Análisis de datos: Llévalo al MAX() from Delft University of Technology

• Jupyter Notebooks and Academic Publication from Stanford University

• Quantitative Biology Workshop from Massachusetts Institute of Technology

• Method of Detection - The Critical Missing Link in U.S. Cancer Registries from Yale University

• Stanford Seminar - Algorithmic Extremism- Examining YouTube's Rabbit Hole of Radicalization from Stanford University

• AI for Academic Search （人工智能助力学术搜索） from Tsinghua University

• Big Data's Big Deal - Viktor Mayer-Schonberger from University of Oxford

• Stanford Seminar - Mobilizing the Future from Stanford University

• Data for the People - Andreas Weigend of Social Data Lab from Stanford University

• Big Data, Big Impact - Preventing Drug Interactions, Understanding Behavior, and Climate Change from Stanford University

• Understanding the World Through Data from Massachusetts Institute of Technology

• 大数据技术与应用 from Tsinghua University

• Communicating Complex Statistical Ideas to the Public - Lessons from the Pandemic - D. Spiegelhalter from University of Oxford

• Grid Modernization and the Integration of Distributed Resources from Stanford University

• Stanford Seminar - Zhang Lin on Mobile Urban Sensing in Beijing from Stanford University

• Human-Machine Symbiosis in Data Visualization from Stanford University

• Harnessing Data for Social Impact - Empowering Communities through Visualization and Social Computing from Stanford University

• 大数据系统基础 from Tsinghua University

• 疫情防控，大数据分析大显身手 from Tsinghua University

• 清华大数据应用实践：快速搭建基于数据的应用 from Tsinghua University

• 数据与智能技术应用 from Tsinghua University

• 基于数据驱动的网络技术应用 from Tsinghua University

• 提升检索（Search）力就是提升研究（Research）力 from Tsinghua University

• 清华大学计算机科学与技术系六十周年系庆学术报告（四）大数据 from Tsinghua University

• “清华数为”大数据智能软件栈 from Tsinghua University

• 大数据可视化 from Zhejiang University

Information Security (InfoSec) (55)

• Web Security from Stanford University ★★★★★(87)

• Computer Systems Security from Massachusetts Institute of Technology ★★★★★(52)

• Web Security Fundamentals from KU Leuven University ★★★★☆(22)

• Engineering Cyber Resiliency - A Pragmatic Approach from Stanford University ★★★★★(22)

• Hash, Hack, Code - Emerging Trends in Cyber Security from Stanford University ★★★★★(12)

• CS50's Introduction to Cybersecurity from Harvard University ★★★★★(6)

• Intro to Information Security from Georgia Institute of Technology ★★☆☆☆(2)

• Network Security from Georgia Institute of Technology ★★★★★(1)

• Cyber Security Economics from Delft University of Technology ★★☆☆☆(1)

• Stanford Seminar - The Current State of Cybersecurity from Stanford University ★★★★★(1)

• Big Breaches - What We Learned From the World's Most Disruptive Cybersecurity Attacks from Stanford University ★★★★☆(1)

• Solving Cybersecurity as an Economic Problem from Stanford University ★★★★☆(1)

• Stanford Seminar - Computer Security- The Mess We're In, How We Got Here, and What to Do About It from Stanford University ★★★★★(1)

• Stanford Seminar - Preventing Successful Cyberattacks Using Strongly-Typed Actors from Stanford University ★★★★☆(1)

• Cyber-Physical Systems Security from Georgia Institute of Technology

• Mobile Payment Security from New York University (NYU)

• La cybersécurité en milieu universitaire from Université de Montréal

• Building Your Shield - Mapping the Cybersecurity Market from Stanford University

• Securing the World Around Us - Cyber Security for the Physical Economy from Stanford University

• The Growing Threat and Impact of Web-Based Malware - Stanford Computer Security from Stanford University

• Cybersecurity for Critical Urban Infrastructure from Massachusetts Institute of Technology

• Security Challenges in 5G Wireless and Beyond from New York University (NYU)

• Cybersecurity in Corporate Law: Key Issues for Practitioners and Policy-makers from New York University (NYU)

• Anatomy of an Attack - Understanding and Protecting Against Cyber Threats from New York University (NYU)

• Stanford Seminar - Online Political Ad Transparency from Stanford University

• Taking Memory Forensics to the Next Level from New York University (NYU)

• Tales from the Risks Forum from Stanford University

• Cybersecurity Career Insights - From Tandon Cyber Fellow to VP at U.S. Bank from New York University (NYU)

• Cyber Security: Building Compromise Resilient Systems and Key Management - Dr. Justin Cappos from New York University (NYU)

• Too Big to Fail: Cybersecurity Issues in Financial Services from New York University (NYU)

• angr: Binary Analysis Framework - Demonstration and Analysis from New York University (NYU)

• Stanford Webinar - To Attribute or Not Attribute, Is That the Question? from Stanford University

• Industry Insights: Cyber Security - Trends, Impacts, and Career Opportunities from University of Melbourne

• Cybersecurity: Keeping the Lights On - Practical Challenges in Utilities - Keynote from New York University (NYU)

• Locking the Web Open - A Call for a New, Decentralized Web from Stanford University

• Stanford Seminar - Thunderclap & CHERI - Capability Hardware-Enhanced RISC Instructions from Stanford University

• Bulletproofs - Short Proofs for Confidential Transactions and More from Stanford University

• How Can Privacy Exist in a Data-Driven World? - Stanford Seminar from Stanford University

• Privacy and the Power of Unknowing from Stanford University

• Hacking Reality: Technology for Detecting Deep Fakes from New York University (NYU)

• Firing Rounds at the Analysis Shooting Gallery - CSAW'16 Security Workshop from New York University (NYU)

• Lessons from Mirai and the Current State of IoT Security from Stanford University

• Exploiting Modern Microarchitectures - Meltdown, Spectre, & Other Hardware Attacks from Stanford University

• RowHammer, RowPress and Beyond: Can We Be Free of Bitflips Soon? - Stanford Seminar from Stanford University

• Proving Confidentiality and Its Preservation for Mixed-Sensitivity Concurrent Programs from University of Melbourne

• Protect Your Patients - Cybersecurity Essentials for Medical Software Course from Yale University

• Medical Software's Lifespan - Maintenance and Safe Retirement Strategy from Yale University

• 网络安全博弈之路 from Tsinghua University

• Trading Privacy for Convenience in the Age of Technology - Part 2 from New York University (NYU)

• IC Layout Security - Hardening Integrated Circuit Designs Against Adversaries from New York University (NYU)

• Empirical Measurement of Security and Privacy in Technology Systems - Conversations with Dr. Damon McCoy from New York University (NYU)

• Dispelling the Top 10 Myths in Cybersecurity - AIG Cybersecurity Lecture from New York University (NYU)

• Democracy Confronts Cyber Insecurity - Lecture 9 from New York University (NYU)

• Web安全实践 from University of Science and Technology of China

• 高级计算机网络 from University of Science and Technology of China

In 2024, the most popular online course out of 5,300 launched across the top MOOC platforms is Google's AI Essentials on Coursera. With over 900,000 enrollments, this single course had more enrollments than the combined total of all new courses launched on edX, FutureLearn, and Swayam in the same year.

More than a decade has passed since a few Stanford professors decided to offer their courses online for free. These courses later became known as MOOCs (Massive Open Online Courses), and 2012 was dubbed the "Year of the MOOC."

What's unique about this edition is that the majority of the courses are from companies, not universities—a significant shift from when MOOCs were first popularized.

Methodology

I built this list following a well defined methodology:

First, I went through the Class Central database and made a list of all the courses that were offered for the first time ever in 2024, on the four platforms below. There were 5300 such courses.

Then, I wrote scrapers to collect enrollment numbers across four major online course platforms: Coursera, edX, FutureLearn, and Swayam. These platforms show how many students are enrolled in each of their courses.

Finally, I sorted the courses according to their enrollment number and truncated the list to keep only the 100 most popular courses. To maintain a balanced comparison, the number of courses considered from Coursera was limited, as they are significantly larger than other platforms, and the gap in scale continues to widen.

Combined, the platforms considered accrued over 5.7M enrollments for courses launched in 2024. The 100 most popular courses account for a whopping 2.7M of them.

If a Coursera course grabs your attention, you might want to read our guide: How to Sign up for Coursera Courses for Free.

Most Popular Online Courses (2025 Edition)

Google AI Essentials Google via Coursera

Operating Systems: Overview, Administration, and Security IBM via Coursera

Cybersecurity Compliance Framework, Standards & Regulations IBM via Coursera

Penetration Testing, Threat Hunting, and Cryptography IBM via Coursera

Incident Response and Digital Forensics IBM via Coursera

Introduction to Large Language Models Google Cloud via Coursera

Extract, Transform and Load Data in Power BI Microsoft via Coursera

Product Management: An Introduction IBM via Coursera

Business Analysis Fundamentals Microsoft via Coursera

AI Infrastructure and Operations Fundamentals Nvidia via Coursera

Data Warehouse Fundamentals IBM via Coursera

Introduction to Computers Microsoft via Coursera

Workday Basics Series Workday via Coursera

Data Analyst Career Guide and Interview Preparation IBM via Coursera

Project Management Essentials Howard University via Coursera

Project Management Fundamentals Microsoft via Coursera

Generative AI for Executives and Business Leaders - Part 1 IBM via Coursera

Fundamentals of Immunology: Complement, MHC I and II, T Cells, and Cytokines Rice University via Coursera

Business Intelligence (BI) Essentials IBM via Coursera

Generative AI Essentials: Overview and Impact University of Michigan via Coursera

The Principles of Real Estate Keller Williams via Coursera

Introduction to Generative AI for Software Development DeepLearning.AI via Coursera

The Art of Storytelling IESE Business School via Coursera

Comprendre la France, Advanced French Language & Culture École Polytechnique via Coursera

Statistical Learning with Python Stanford University via edX

Basic Mathematics Birla Institute Of Technology And Science–Pilani (BITS–Pilani) via Coursera

Fundamentals of Immunology: Inflammation, Tolerance, Autoimmunity Rice University via Coursera

Accelerate Your Learning with ChatGPT Deep Teaching Solutions via Coursera

Software Developer Career Guide and Interview Preparation IBM via Coursera

Supply Chain Management and Analytics Unilever via Coursera

Introduction to Data Engineering DeepLearning.AI via Coursera

Introduction to Prompt Engineering IBM via edX

English Intermediate B1.2 University of Naples Federico II via Coursera

Google Prompting Essentials Google via Coursera

OpenAI GPTs: Creating Your Own Custom AI Assistants Vanderbilt University via Coursera

Data Science Using Python Aligarh Muslim University via Swayam

Data Science Using Python Aligarh Muslim University via Swayam

Child Development_Education CEC via Swayam

Microsoft 365 Copilot: Personal Productivity for All Vanderbilt University via Coursera

ChatGPT for Project Management: Execution, Tracking, Success Vanderbilt University via Coursera

Introduction to Security Principles in Cloud Computing Google Cloud via Coursera

Generative AI: Elevate Your Data Science Career IBM via Coursera

Advanced Digital Transformation (Part 1) Indian Institute of Management Ahmedabad via Coursera

Rocket Science for Everyone Yale University via Coursera

Fundamentals of UI/UX Design Microsoft via Coursera

Generative AI: Elevate your Software Development Career IBM via Coursera

Customer Understanding and Digital Marketing Channels Unilever via Coursera

Graphic Design Essentials LCI Education via edX

ChatGPT - Usos y Estrategias Tecnológico de Monterrey via Coursera

Introduction to Cosmetic and Skincare Science Olay via Coursera

CompTIA a+ Cyber CompTIA via Coursera

Strategy and Game Theory for Management Indian Institute of Management Ahmedabad via Coursera

5G Wireless Standard Design Indian Institute of Technology Kanpur via Swayam

Introduction to Generative AI Duke University via Coursera

JavaScript Programming Essentials IBM via Coursera

Building Generative AI-Powered Applications with Python IBM via Coursera

CompTIA a+ Network CompTIA via Coursera

Python Data Structures Coursera Project Network via Coursera

Responsible & Safe AI Systems NPTEL via Swayam

Design Fundamentals Adobe via Coursera

Advanced R Programming for Data Analytics in Business Indian Institute of Technology Kanpur via Swayam

Responsible AI: Applying AI Principles with Google Cloud Google Cloud via Coursera

Data for Business Analysts Using Microsoft Excel Microsoft via Coursera

Generative AI: Enhance your Data Analytics Career IBM via Coursera

MBA Essentials University of Glasgow via Coursera

Introduction to Data Analytics in Google Cloud Google Cloud via Coursera

Introduction to Game Design Epic Games via Coursera

AI in Human Resource Management NPTEL via Swayam

Generative AI for University Leaders Vanderbilt University via Coursera

GPT Vision: Seeing the World through Generative AI Vanderbilt University via Coursera

Mastering Laravel Framework and PHP Board Infinity via Coursera

Business Analysis: Key Definitions & Strategy Analysis Starweaver via Coursera

Inteligencia Artificial (IA): Interacciones y prompts Universidad de Palermo via Coursera

Web Technology Uttarakhand Open University, Haldwani via Swayam

Practical Cyber Security for Cyber Security Practitioners Indian Institute of Technology Kanpur via Swayam

CS50's Introduction to Programming with R Harvard University via edX

Artificial Intelligence and Machine Learning in Materials Engineering Indian Institute of Technology Kanpur via Swayam

A Basic Course in Machine Learning for All Indian Institute of Management Bangalore via Swayam

Electronic Systems Design: Hands-on Circuits and PCB Design with CAD Software Indian Institute of Technology Delhi via Swayam

3D Printing and Design for Educators NITTTR via Swayam

The Living Amazon: Science, Cultures and Sustainability in Practice SDG Academy via edX

Introduction to Large Language Models (LLMs) NPTEL via Swayam

Digital Marketing Strategy Banaras Hindu University via Swayam

Introduction to Portuguese / Iniciação à Língua Portuguesa Fundação para a Ciência e a Tecnologia via edX

Artificial Intelligence for Economics Indian Institute of Technology, Kharagpur via Swayam

Introduction to Illustrator LCI Education via edX

Computer Architecture Essentials on Arm Arm Education via edX

Introduction to ChatGPT. Boost your productivity to the maximum using artificial intelligence! Universitat Politècnica de València via edX

Introduction to Robotic Programming RWTH Aachen University via edX

Quickbooks Level 1 Intuit via edX

Financial Markets and Products State Bank of India via edX

Interpreting Genomic Variation: Overcoming Challenges in Diverse Populations via FutureLearn

Fundamentals of Economics University of California, Berkeley via edX

Structure and Function of Argument: Introduction to Critical Thinking Harvard University via edX

Inclusive Education: Exploring SEND Practice and Strategies University of Leeds via FutureLearn

Interior Design Essentials LCI Education via edX

An Introduction to Programming Using Python University of Leeds via FutureLearn

How to Get Into AI University of Leeds via FutureLearn

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And yes this is the full book – for free – right here on this page of freeCodeCamp.

Also, I've recorded a FREE full-length audiobook version of this book, which I published as episode #100 of The freeCodeCamp Podcast. You can search for it in your favorite podcast player. Be sure to subscribe. I've also embedded it below for your convenience.

A few years back, one of the Big 5 book publishers from New York City reached out to me about a book deal. I met with them, but didn't have time to write a book.

Well, I finally had time. And I decided to just publish this book for free, right here on freeCodeCamp.

Information wants to be free, right? 🙂

It will take you a few hours to read all this. But this is it. My insights into learning to code and getting a developer job.

I learned all of this while:

• learning to code in my 30s
• then working as a software engineer
• then running freeCodeCamp.org for the past 8 years. Today, more than a million people visit this website each day to learn about math, programming, and computer science.

I was an English teacher who had never programmed before. And I was able to learn enough coding to get my first software development job in just one year.

All without spending money on books or courses.

(I did spend money to travel to nearby cities and participate in tech events. And as you'll see later in the book, this was money well spent.)

After working as a software engineer for a few years, I felt ready. I wanted to teach other people how to make this career transition, too.

I built several technology education tools that nobody was interested in using. But then one weekend, I built freeCodeCamp.org. A vibrant community quickly gathered around it.

Along the way, we all helped each other. And today, people all around the world have used freeCodeCamp to prepare for their first job in tech.

You may be thinking: I don't know if I have time to read this entire book.

No worries. You can bookmark it. You can come back to it and read it across as many sittings as you need to.

And you can share it on social media. Sharing: "check out this book I'm reading" and linking to it is a surprisingly effective way to convince yourself to finish reading a book.

I say this because I'm not trying to sell you this book. You already "bought" this book when you opened this webpage. Now my goal is to reassure you that it will be worth investing your time to finish reading this book. 😉

I promise to be respectful of your time. There's no hype or fluff here – just blunt, actionable tips.

I'm going to jam as much insight as I can into every chapter of this book.

Which reminds me: where's the table of contents?

Ah. Here it is:

Table of Contents

1. Preface: Who is this book for?
2. 500 Word Executive Summary
3. Chapter 1: How to Build Your Skills
4. Chapter 2: How to Build Your Network
5. Chapter 3: How to Build Your Reputation
6. Chapter 4: How to Get Paid to Code – Freelance Clients and the Job Search
7. Chapter 5: How to Succeed in Your First Developer Job
8. Epilogue: You Can Do This

Preface: Who is This Book For?

This book is for anyone who is considering a career in software development.

If you're looking for a career that's flexible, high-paying, and involves a lot of creative problem solving, software development may be for you.

Of course, each of us approaches our own coding journey with certain resources: time, money, and opportunity.

You may be older, and may have kids or elderly relatives you're taking care of. So you may have less time.

Or you may be younger, and may have had less time to build up any savings, or acquire skills that boost your income. So you may have less money.

And you may live far away from the major tech cities like San Francisco, Berlin, Tokyo, or Bengaluru.

Or you may live with disabilities, physical or mental. Agism, racism, and sexism are real. Immigration status can complicate the job search. So can a criminal record.

So you may have less opportunity.

Learning to code and getting a developer job is going to be harder for some people than it will be for others. Everyone approaches this challenge from their own starting point, with whatever resources they happen to have on hand.

But wherever you may be starting out from – in terms of time, money, and opportunity – I'll do my best to give you actionable advice.

In other words: don't worry – you are in the right place.

A Quick Note on Terminology

Whenever I use new terms, I'll do my best to define them.

But there are a few terms I'll be saying all the time.

I'll use the words "programming" and "coding" interchangeably.

I'll use the word "app" as it was intended – as shorthand for any sort of application, regardless of whether it runs on a phone, laptop, game console, or refrigerator. (Sorry, Steve Jobs. iPhone does not have a monopoly on the word app.)

I will also use the words "software engineer" and "software developer" interchangeably.

You may encounter people in tech who take issue with this. As though software engineering is some fancy-pants field with a multi-century legacy, like mechanical engineering or civil engineering are. Who knows – maybe that will be true for your grandkids. But we are still very much in the early days of software development as a field.

I'll just drop this quote here for you, in case you feel awkward calling yourself a software engineer:

"If builders built buildings the way programmers wrote programs, then the first woodpecker that came along would destroy civilization." – Gerald Weinberg, Programmer, Author, and University Professor

Can Anyone Learn to Code?

Yes. I believe that any sufficiently motivated person can learn to code. At the end of the day, learning to code is a motivational challenge – not a question of aptitude.

On the savannas of Africa – where early humans lived for thousands of years before spreading to Europe, Asia, and the Americas – were there computers?

Programming skills were never something that was selected for over the millennia. Computers as we know them (desktops, laptops, smartphones) emerged in the 80s, 90s, and 00s.

Yes – I do believe that aptitude plays a part. But at the end of the day, anyone who wants to become a professional developer will need to put in time at the keyboard.

A vast majority of people who try to learn to code will get frustrated and give up.

I sure did. I got frustrated and gave up. Several times.

But like other people who eventually succeeded, I kept coming back after a few days, and tried again.

I say all this because I want to acknowledge: learning to code and getting a developer job is hard. And it's even harder for some people than others, due to circumstance.

I'm not going to pretend to have faced true adversity in learning to code. Yes, I was in my 30s, and I had no formal background in programming or computers science. But consider this:

I grew up middle class in the United States – a 4th-generation American from an English-speaking home. I went to university. My father went to university. And his father went to university. (His parents before him were farmers from Sweden.)

I benefitted from a sort of intergenerational privilege. A momentum that some families are able to pick up over time when they are not torn apart by war, famine, or slavery.

So that is my giant caveat to you: I am not some motivational figure to pump you up to overcome adversity.

If you need inspiration, there are a ton of people in the developer community who have overcome real adversity. You can seek them out.

I'm not trying to elevate the field of software development. I'm not going to paint pictures of science fiction utopias that can come about if everyone learns to code.

Instead, I'm just going to give you practical tips for how you can acquire these skills. And how you can go get a good job, so you can provide for your family.

There's nothing wrong with learning to code because you want a good, stable job.

There's nothing wrong with learning to code so you can start a business.

You may encounter people who say that you must be so passionate about coding that you dream about it. That you clock out of your full-time job, then spend all weekend contributing to open source projects.

I do know people who are that passionate about coding. But I also know plenty of people who, after finishing a hard week's work, just want to go spend time in nature, or play board games with friends.

People generally enjoy doing things they're good at doing. And you can develop a reasonable level of passion for coding just by getting better at coding.

So in short: who is this book for? Anyone who wants to get better at coding, and get a job as a developer. That's it.

You don't need to be a self-proclaimed "geek", an introvert, or an ideologically-driven activist. Or any of those stereotypes.

It's fine if you are. But you don't need to be.

So if that's you – if you're serious about learning to code well enough to get paid to code – this book is for you.

And you should start by reading this quick summary of the book. And then reading the rest of it.

500 Word Executive Summary

Learning to code is hard. Getting a job as a software developer is even harder. But for many people, it's worth the effort.

Coding is a high-paying, intellectually challenging, creatively rewarding field. There is a clear career progression ahead of you: senior developer, tech lead, engineering manager, CTO, and perhaps even CEO.

You can find work in just about any industry. About two thirds of developer jobs are outside of what we traditionally call "tech" – in agriculture, manufacturing, government, and service industries like banking and healthcare.

If you're worried your job might be automated before you reach retirement, consider this: coding is the act of automating things. Thus it is by definition the last career that will be completely automated.

Automation will impact coding. It already has. For decades.

Generative AI tools like GPT-4 and Copilot can help us move from Imperative Programming – where you tell computers exactly what to do – closer to Declarative Programming – where you give computers higher-level objectives. In other words: Star Trek-style programming.

You should still learn math even though we now have calculators. And you should still learn programming even though we now have AI tools that can write code.

Have I sold you on coding as a career for you?

Good. Here's how to break into the field.

Build your skills.

You need to learn:

• Front End Development: HTML, CSS, JavaScript
• Back End Development: SQL, Git, Linux, and Web Servers
• Scientific Computing: Python and its many libraries

These are all mature, 20+ year old technologies. Whichever company you work for, you will almost certainly use most of these tools.

The best way to learn these tools is to build projects. Try to code at least some every day. If you do the freeCodeCamp curriculum from top to bottom, you'll learn all of this and build dozens of projects.

Some of the certifications in the freeCodeCamp core curriculum.

Build your network.

So much of getting a job is who you know.

It's OK to be an introvert, but you do need to push your boundaries.

Create GitHub, Twitter, LinkedIn, and Discord accounts.

Go to tech meetups and conferences. Travel if you have to. (Most of your "learn to code" budget should go toward travel and event tickets – not books and courses.)

Greet people who are standing by themselves. Let others do most of the talking, and really listen. Remember people's names.

Add people on LinkedIn, follow them on Twitter, and go to after-parties.

Build your reputation.

Share short video demos of your projects.

Keep applying to speak at bigger and bigger conferences.

Hang out at hackerspaces and help people who are even newer to coding than you.

Contribute to open source. The work is similar to professional software development.

Build your Skills, Network, and Reputation at the same time. Don't let yourself procrastinate the scariest parts.

Instead of applying for jobs through the "front door" use your network to land job interviews through the "side door." Recruiters can help, too.

Keep interviewing until you start getting job offers. You don't need to accept the first offer you get, though. Be patient.

Your first developer job will be the hardest. Try to stay there for at least 2 years, and essentially get paid to learn.

The real learning begins once you're on-the-job, working alongside a team, and with large legacy codebases.

Most importantly, sleep and exercise.

Any sufficiently-motivated person can learn to code well enough to get a job as a developer.

It's just a question of how badly you want it, and how persistent you can be in the job search.

Remember: you can do this.

This Book is Dedicated to the Global freeCodeCamp Community.

Thank you to all of you who have supported our charity and our mission over the past 9 years.

It is through your volunteerism and through your philanthropy that we've been able to help so many people learn to code and get their first developer job.

The community has grown so much from the humble open source project I first deployed in 2014. I am now just a small part of this global community.

It is a privilege to still be here, working alongside you all. Together, we face the fundamental problems of our time. Access to information. Access to education. And access to the tools that are shaping the future.

These are still early days. I have no illusion that everyone will know how to code within my lifetime. But just like the Gutenberg Bible accelerated literacy in 1455, we can continue to accelerate technology literacy through free, open learning resources.

Again, thank you all.

And special thanks to Abbey Rennemeyer for her editorial feedback, and to Estefania Cassingena Navone for designing the book cover.

And now, the book.

Chapter 1: How to Build Your Skills

"Every artist was first an amateur." ― Ralph Waldo Emerson

The road to knowing how to code is a long one.

For me, it was an ambiguous one.

But it doesn't have to be like that for you.

In this chapter, I'm going to share some strategies for learning to code as smoothly as possible.

First, allow me to walk you through how I learned to code back in 2011.

Then I'll share what I learned from this process.

I'll show you how to learn much more efficiently than I did.

Story Time: How Did a Teacher in His 30s Teach Himself to Code?

I was a teacher running an English school. We had about 100 adult-aged students who had traveled to California from all around the world. They were learning advanced English so they could get into grad school.

Most of our school's teachers loved teaching. They loved hanging out with students around town, and helping them improve their conversational English.

What these teachers didn't love was paperwork: Attendance reports. Grade reports. Immigration paperwork.

I wanted our teachers to be able to spend more time with students. And less time chained to their desks doing paperwork.

But what did I know about computers?

Programming? Didn't you have to be smart to do that? I could barely configure a WiFi router. And I sucked at math.

Well one day I just pushed all that aside and thought "You know what: I'm going to give it a try. What do I have to lose?"

I started googling questions like "how to automatically click through websites." And "how to import data from websites into Excel."

I didn't realize it at the time, but I was learning how to automate workflows.

And the learning began. First with Excel macros. Then with a tool called AutoHotKey where you can program your mouse to move to certain coordinates of a screen, click around, copy text, then move to different coordinates and paste it.

After a few weeks of grasping in the dark, I figured out how to automate a few tasks. I could open an Excel spreadsheet and a website, run my script, then come back 10 minutes later and the spreadsheet would be fully populated.

It was the work of an amateur. What developers might call a "dirty hack". But it got the job done.

I used my newfound automation skills to continue streamlining the school.

Soon teachers barely had to touch a computer. I was doing the work of several teachers, just with my rudimentary skills.

This had a visible impact on the school. So much of our time had been tied up with rote work on the computer. And now we were free.

The teachers were happier. They spent more time with students.

The students were happier. They told all their friends back in their home country "you've got to check out this school."

Soon we were one of the most successful schools in the entire school system.

This further emboldened me. I remember thinking to myself: "Maybe I can learn to code."

I knew some software engineers from my board game night. They had traditional backgrounds, with degrees from Cal Tech, Harvey Mudd, and other famous Computer Science programs.

At the time, it was far less common for people in their 30s to learn to code.

I worked up the courage to share my dreams with some of these friends.

I wanted to learn to how program properly. I wanted to be able to write code for a living like they did. And to maybe even write software that could power schools.

I would share these dreams up with my developer friends. "I want to do what you do."

But they would sort of shrug. Then they'd say something like:

"I mean, you could try. But you're going to have to drink an entire ocean of knowledge."

And: "It's a pretty competitive field. How are you going to hang with people who grew up coding from an early age?"

And: "You're already doing fine as a teacher. Why don't you just stick with what you're good at?"

And that would knock me off course for a few weeks. I would go on long, soul-searching walks at night. I would ponder my future under the stars. Were these people right? I mean – they would know, right?

But every morning I'd be back at my desk. Watching my scripts run. Watching my reports compile themselves at superhuman speeds. Watching as my computer did my bidding.

A thought did occur to me: maybe these friends were just trying to save me from heartache. Maybe they just don't know anyone who learned to code in their 30s. So they don't think it's possible.

It's like... for years doctors thought that it would be impossible for someone to run a mile in 4 minutes. They thought your heart would explode from running so fast.

But then somebody managed to do it. And his heart did not explode.

Once Roger Bannister – a 25-year old Oxford student – broke that psychological barrier – a ton of other people did it, too. To date, more than 1,000 people have run a sub-4 minute mile.

Roger Bannister running like a a champ. (Image: Britannica)

And it's not like I was doing something as bold and unprecedented as running a 4 minute mile here. Plenty of famous developers have managed to teach themselves coding over the years.

Heck, Ada Lovelace taught herself programming in the 1840s. And she didn't even have a working computer. She just had an understanding of how her friend Charles Babbage's computer would work in theory.

She wrote several of the first computer algorithms. And she's widely regarded as the world's first computer programmer. Nobody taught her. Because there was nobody to teach her. Whatever self doubt she may have had, she clearly overcame it.

Now, I was no Ada Lovelace. I was just some teacher who already had a working computer, a decent internet connection, and the ability to search through billions of webpages with Google.

I cracked my knuckles and narrowed my gaze. I was going to do this.

Stuck in Tutorial Hell

"If you work for 10 years, do you get 10 years of experience or do you get 1 year of experience 10 times? You have to reflect on your activities to get true experience. If you make learning a continuous commitment, you’ll get experience. If you don’t, you won’t, no matter how many years you have under your belt." – Steve McConnell, Software Engineer

I spent the next few weeks googling around, and doing random tutorials that I encountered online.

Oh look, a Ruby tutorial.

Uh-oh, it's starting to get hard. I'm getting error messages not mentioned in the tutorial. Hm... what's going on here...

Oh look, a Python tutorial.

Human psychology is a funny thing. The moment something starts to get hard, we ask: am I doing this right?

Maybe this tutorial is out of date. Maybe its author didn't know what they were talking about. Does anybody even still use this programming language?

When you're facing ambiguous error messages hours into a coding session, the grass on the other side starts to look a lot greener.

It was easy to pretend I'd made progress. Time to go grab lunch.

I'd see a friend at the café. "How's your coding going?" they'd ask.

"It's going great. I already coded 4 hours today."

"Awesome. I'd love to see what you're building sometime."

"Sure thing," I'd say, knowing that I'd built nothing. "Soon."

Maybe I'd go to the library and check out a new JavaScript book.

There's that old saying that buying books gives you the best feeling in the world. Because it also feels like you're buying the time to read them.

And this is precisely where I found myself a few weeks into learning to code.

I had read the first 100 pages of several programming books, but finished none.

I had written the first 100 lines of code from several programming tutorials, but finished none.

I didn't know it, but I was trapped in place that developers lovingly call "tutorial hell."

Tutorial hell is where you jump from one tutorial to the next, learning and then relearning the same basic things. But never really going beyond the fundamentals.

Because going beyond the fundamentals? Well, that requires some real work.

It Takes a Village to Raise a Coder

Learning to code was absorbing all of my free time. But I wasn't making much progress. I could now type the { and * characters without looking at the keyboard. But that was about it.

I knew I needed help. Perhaps some Yoda-like mentor, who could teach me the ways. Yes – if such a person existed, surely that would make all the difference.

I found out about a nearby place called a "hackerspace." When I first heard the name, I was a bit apprehensive. Don't hackers do illegal things? I was an English teacher who liked playing board games. I was not looking for trouble.

Well I called the number listed and talked with a guy named Steve. I nervously asked: "You all don't do anything illegal, do you?" And Steve laughed.

It turns out the word "hack" is what he called an overloaded term. Yes – "to hack" can mean to maliciously break into a software system. But "to hack" can also mean something more mundane: to write computer code.

Something can be "hacky" meaning it's not an elegant solution. And yet you can have "a clever hack" – an ingenious trick to make your code work more efficiently.

In short: don't be scared of the term "hack."

Facebook's corporate campus has the word "hack" written in giant letter on the concrete. (Image: Bloomberg)

I, for one, scarcely use the term because it's so confusing. And I think recently a lot of hackerspaces have picked up on the ambiguity. Many of them now call themselves "makerspaces" instead.

Because that's what a hackerspace is all about – making things.

Steve invited me to visit the hackerspace on Saturday afternoon. He said several developers from the area would be there.

The first time I walked through the doors of the Santa Barbara Hackerspace, I was blown away.

The place smelled like an electric fire. Its makeshift tables were lined with soldering irons, strips of LED lights, hobbyist Arduino circuit boards, and piles of Roomba vacuum robots.

The same Steve I'd spoken to on the phone was there, and he greeted me. He had glasses, slicked back hair, and a goatee beard. He was always smiling. And when you asked him a question, instead of responding quickly, he would nod and think for a few seconds first.

Steve was a passionate programmer who had studied math and philosophy at the University of California – Santa Barbara. He was still passionate about those subjects. But his real passion was Python.

Steve turned on the projector and gave an informal "lightning talk." He was demoing an app he'd written that would recognize QR codes in a video and replace them with images.

Someone in the audience pulled up a QR code on their laptop and held it in front of the camera. Steve's app then replaced the QR code with a picture of a pizza.

Somebody in the audience shouted, "Can you make the pizza spin?"

Steve opened up his code in a code editor, called Emacs, and started making changes to it in real time. He effortlessly tabbed between his code editor, his command line, and the browser the app was running in, "hot loading" updates to the code.

For me, this was sorcery. I couldn't believe Steve had just busted out that app over the course of few hours. And now he was adding new features on the fly, as the audience requested them.

I thought: "This guy is a genius."

And that evening, after the event ended, he and I stayed after and I told him so.

We ate sandwiches together. And I said to him: "I could code for my entire career and not be as good as you. I would be thrilled if after 10 years I could code even half as well as you."

But Steve pushed back. He said, "I'm nothing special. Don't limit yourself. If you stick with coding, you could easily surpass me."

Now, I didn't for a second believe the words he said to me. But just the fact that he said it gave me butterflies.

Here he was: a developer who believed in me. He saw me – some random teacher – the very definition of a "script kiddie" – and thought I could make it.

Steve and I talked late into the night. He showed me his $200 netbook computer, which even by 2011 standards was woefully underpowered.

"You don't need a powerful computer to build software," Steve told me. "Today's hardware is incredibly powerful. Computers are only slow because the bloated software they run makes them slow. Get an off-the-shelf laptop, wipe the hard drive, install Linux on it, and start coding."

I took note of the model of laptop he had and ordered the exact same one when I got home that night.

After a few days of debugging my new computer with Stack Overflow, I successfully installed Ubuntu. I started learning how to use the Emacs code editor. By the following Saturday, I knew a few commands, and was quick to show them off.

Steve nodded in approval. He said, "Awesome. But what are you building?"

I didn't understand what he meant. "I'm learning how to use Emacs. Check it out. I memorized..."

But Steve looked pensive. "That's cool and all. But you need a project. Always have a project. Then learn what you need to learn en route to finishing that project."

Other than a few scripts I'd written for to help the teachers at my school, I had never finished anything. But I started to see what he was saying.

And it started to dawn on me. All this time I had been trapped in tutorial hell, going in circles, finishing nothing.

Steve said, "I want you to build a project using HTML5. And next Saturday, I want you to present it at the hackerspace."

I was mortified at his words. But I stood up straight and said. "Sounds like a plan. I'm on it."

Nobody Can Make You a Developer But You

"I'm trying to free your mind, Neo. But I can only show you the door. You're the one that has to walk through it." – Morpheus in the 1999 film The Matrix

The next morning, I woke up extra early before work and googled something like "HTML5 tutorial." I already knew a lot of this from my previous time in tutorial hell. But instead of skipping ahead, I just slowed my roll and followed along exactly, typing every single command.

Usually once I finished a tutorial I would just go find another tutorial. But instead, I started playing with the tutorial's code. I had a simple idea for a project. I was going to make an HTML5 documentation page. And I was going to code it purely in HTML5.

Let me explain HTML5 real quick. It's just a newer version of HTML, which has existed since the first webpages back in the 1990s.

If a website was a body, HTML would be the bones. Everything else rests on top of those bones. (You can think of JavaScript as the muscles and CSS as the skin. But let's get back to the story.)

I knew that in HTML, you could link to different parts of the same webpage by using ID properties. So I thought: what if I put a table of contents along the left hand side? Then clicking the different items on the left would scroll down the page on the right to show those items.

Within half an hour, I had coded a rough prototype.

But it was time to report for work at the school. The entire day, all I could think about was my project, and how I should best go about finishing it.

I raced home, opened up my laptop, and spent the entire evening coding.

I copied the official (and creative commons-licensed) HTML documentation directly into my page, "hard coding" it into the HTML.

Then I spent about an hour on the CSS, getting everything to look right, and using absolute positioning to keep the sidebar in place.

I made a point to make use of as many of HTML5's new "semantic" tags as I could.

And boom – project finished.

A wave of accomplishment washed over me. I jogged to a nearby football field and ran laps around the field, celebrating. I did it. I finished a project.

And I decided right then and there: from here on out, everything I do is going to be a project. I'm going to be working toward some finished product.

The next evening I walked up to the podium, plugged in my laptop, and presented my HTML5 webpage. I answered questions from the developers there about HTML5.

Sometimes I'd get something wrong, and someone in the audience would say, "that doesn't sound right – let me check the documentation."

People weren't afraid to correct me. But they were polite and supportive. It didn't even feel like they were correcting me – it felt like they were correcting the public record – lest someone walk away with incorrect information.

I didn't feel any of the anxiety that I might have felt giving a talk at a teacher in-service meeting.

Instead I almost felt like I was part of the audience, learning alongside them.

After all, these tools were new and emerging. We were all trying to understand how to use them together.

After my talk, Steve came up to me and said, "Not bad."

I smiled for an awkwardly long time, not saying anything, just happy with myself.

Then Steve squinted and pursed his lips. He said: "Start your next project tonight."

Lessons from my Coding Journey

We'll check in on younger Quincy's coding journey in each of the following chapters. But now I want to break down some of the lessons here. And I want to answer some of the questions you may have.

Why is Learning to Code so Hard?

Learning any new skill is hard. Whether it's dribbling a soccer ball, changing the oil on a car, or speaking a new language.

Learning to code is hard for a few particular reasons. And some of these are unique to coding.

The first one is that most people don't understand exactly what coding is. Well, I'm going to tell you.

What is coding?

Coding is telling a computer what to do, in a way the computer can understand.

That's it. That's all coding really is.

Now, make no mistake. Communicating with computers is hard. They are "dumb" by human standards. They will do exactly what you tell them to do. But unless you're good at coding, they are probably not going to do what you want them to do.

You may be thinking: what about servers? What about databases? What about networks?

At the end of the day, these are all controlled by layers of software. Code. It's code all the way down. Eventually you reach the physical hardware, which is moving electrons around circuit boards.

For the first few decades of computing, developers wrote code that was "close to the metal" – often operating on the hardware directly, flipping bits from 0 to 1 and back.

But contemporary software development involves so many "layers of abstraction" – programs running on top of programs – that just a few lines of JavaScript code can do some really powerful things.

In the 1960s, a "bug" could be an insect crawling around inside a room-sized computer, and getting fried in one of the circuits.

The first computer bug, discovered in 1945, was a moth that got trapped in the panels of a room-sized calculator computer at Harvard. (Image: Public Domain)

Today, we're writing code so many layers of abstraction above the physical hardware.

That is coding. It's vastly easier than it has ever been in the past. And it is getting easier to do every year.

I am not exaggerating when I say that in a few decades, coding will be so easy and so common that most younger people will know how to do it.

Why is learning to code still so hard after all these years?

There are three big reasons why learning to code is so hard, even today:

1. The tools are still primitive.
2. Most people aren't good at handling ambiguity, and learning to code is ambiguous. People get lost.
3. Most people aren't good at handling constant negative feedback. And learning to code is one brutal error message after another. People get frustrated.

Now I'll discuss each of these difficulties in more detail. And I'll give you some practical strategies for overcoming each of them.

The Tools are Still Primitive

A Possessed Barclay from Star Trek: The Next Generation, programming on the Holodeck.

"Computer. Begin new program. Create as follows. Work station chair. Now create a standard alphanumeric console positioned to the left hand. Now an iconic display console for the right hand. Tie both consoles into the Enterprise main computer core, utilizing neuralscan interface." - Barclay from Star Trek: The Next Generation, Season 4 Episode 19: "The Nth Degree"

This is how people might program in the future. It's an example from my favorite science fiction TV show, Star Trek: The Next Generation.

Every character in Star Trek can code. Doctors, security officers, pilots. Even little Wesley Crusher (played by child actor Wil Wheaton) can get the ship's computer to do his bidding.

Sure – one of the reasons everyone can code is that they live in a post-scarcity 24th-century society, with access to free high quality education.

Another reason is that in the future, coding will be much, much easier. You just tell a computer precisely what to do, and – if you're precise enough – the computer does it.

What if programming was as easy as just saying instructions to a computer in plain English?

Well, we've already made significant progress toward this goal. Think of our grandmothers, running between room-sized mainframe computers with stacks of punchcards.

Working with a punchcard-based computer in the 1950s (Image: NASA)

It used to be that programming even a simple application would require meticulous instructions.

Here are two examples of a "Cesar Cypher", the classic computer science homework project.

This is also known as "ROT-13" because you ROTate the letters by 13 positions. For example, A becomes N (13 letters after A), and B becomes O (13 letters after B).

I'm going to show you two examples of this program.

First, here's the program in x86 Assembly:

format     ELF     executable 3
entry     start

segment    readable writeable
buf    rb    1

segment    readable executable
start:    mov    eax, 3        ; syscall "read"
    mov    ebx, 0        ; stdin
    mov    ecx, buf    ; buffer for read byte
    mov    edx, 1        ; len (read one byte)
    int    80h

    cmp    eax, 0        ; EOF?
    jz    exit

    xor     eax, eax    ; load read char to eax
    mov    al, [buf]
    cmp    eax, "A"    ; see if it is in ascii a-z or A-Z
    jl    print
    cmp    eax, "z"
    jg    print
    cmp    eax, "Z"
    jle    rotup
    cmp    eax, "a"
    jge    rotlow
    jmp    print

rotup:    sub    eax, "A"-13    ; do rot 13 for A-Z
    cdq
    mov    ebx, 26
    div    ebx
    add    edx, "A"
    jmp    rotend

rotlow:    sub    eax, "a"-13    ; do rot 13 for a-z
    cdq
    mov    ebx, 26
    div    ebx
    add    edx, "a"

rotend:    mov    [buf], dl

print:     mov    eax, 4        ; syscall write
    mov    ebx, 1        ; stdout
    mov    ecx, buf    ; *char
    mov    edx, 1        ; string length
    int    80h

    jmp    start

exit:     mov     eax,1        ; syscall exit
    xor     ebx,ebx        ; exit code
    int     80h

This x86 Assembly example comes from the Creative Commons-licensed Rosetta Code project.

And here's the same program, written in Python:

def rot13(text):
    result = []

    for char in text:
        ascii_value = ord(char)

        if 'A' <= char <= 'Z':
            result.append(chr((ascii_value - ord('A') + 13) % 26 + ord('A')))
        elif 'a' <= char <= 'z':
            result.append(chr((ascii_value - ord('a') + 13) % 26 + ord('a')))
        else:
            result.append(char)

    return ''.join(result)

if __name__ == "__main__":
    input_text = input("Enter text to be encoded/decoded with ROT-13: ")
    print("Encoded/Decoded text:", rot13(input_text))

This is quite a bit simpler and easier to read, right?

This Python example comes straight from GPT-4. I prompted it the same way Captain Picard would prompt the ship's computer in Star Trek.

Here's exactly what I said to it: "Computer. New program. Take each letter of the word I say and replace it with the letter that appears 13 positions later in the English alphabet. Then read the result back to me. The word is Banana."

GPT-4 produced this Python code, and then read the result back to me: "Onanan."

What we're doing here is called Declarative Programming. We're declaring "computer, you should do this." And the computer is smart enough to understand our instructions and execute them.

Now, the style of coding most developers use today is Imperative Programming. We're telling the computer exactly what to do, step-by-step. Because historically, computers have been pretty dumb. So we've had to help them put one foot in front of the other.

The field of software development just isn't mature yet.

But just like early human tools advanced – from stone to bronze to iron – the same is happening with software tools. And much faster.

We're probably still in the programming equivalent of the Bronze Age right now. But we may reach the Iron Age in our lifetime. Generative AI tools like GPT are quickly becoming more powerful and more reliable.

The developer community is still divided on how useful tools like GPT will be for software development.

On one side, you have the "become your own boss" entrepreneur influencers who say things like: "You don't need to learn to code anymore. ChatGPT can write all your code for you. You just need an app idea."

And on the other side of the spectrum, you have "old guard" developers with decades of programming experience – many of whom are skeptical that tools like GPT are really all that useful for producing production-grade code.

As with most things, the real answer is probably somewhere in between.

You don't have to look hard to find YouTube videos of people who start with an app idea, then prompt ChatGPT for the code they need. Some people can even take that code and wire it together into an app that works.

Large Language Models like GPT-4 are impressive, and the speed at which they're improving is even more impressive.

Still, many developers are skeptical about how useful these tools will ultimately become. They question whether we'll be able to get AIs to stop "hallucinating" false information.

This is the fundamental problem of "Interpretability." It could be decades before we truly understand what's going on inside of a black box AI like GPT-4. And until we do, we should double check everything it says, and assume there will be lots of bugs and security flaws in the code that it gives us.

There's a big difference from being able to get a computer to do something for you, and actually understanding how the computer is doing it.

Many people can operate a car. But far fewer can repair a car – let alone design a new car from the ground up.

If you want to be able to develop powerful software systems that solve new problems – and you want those systems to be fast and secure – you're still going to need to learn how to code properly.

And that means feeling your way through a lot of ambiguity.

Learning to Code is an Ambiguous Process

When you're learning to code, you constantly ask yourself: "Am I spending my time wisely? Am I learning the right tools? Do these book authors / course creators even know what they're talking about?"

Ambiguity fogs your every study session. "Did my test case fail because the tutorial is out of date, and there have been breaking changes to the framework I'm using? Or am I just doing it wrong?"

As I mentioned earlier with Tutorial Hell, you also have to cope with "grass is greener on the other side" disease.

This is compounded by the fact that some developers think it's clever to answer questions with "RTFM" which means "Read the Freaking Manual." Not super helpful. Which manual? Which section?

Another problem is: you don't know what you don't know. Often you can't even articulate the question you're trying to ask.

And if you can't even ask the right question, you're going to thrash.

This is extra hard with coding because it's possible no one has attempted to build quite the same app that you're building.

And thus some of the problems you encounter may be unprecedented. There may be no one to turn to.

15% of the queries people type into Google every day have never ever been searched before. That's bad news if you're the person typing one of those.

My theory is that most developers will figure out how to solve a problem and simply move on, without ever documenting it anywhere. So you may be one of dozens of developers who has had to invent their own solution to the same exact problem.

And then, of course, there are the old forum threads and StackOverflow pages.

Comic by XKCD

How Not to Get Lost When Learning to Code

The good news is: both competence and confidence come with practice.

Soon you'll know exactly what to google. You'll get a second sense for how documentation is usually structured, and where to look for what. And you'll know where to ask which questions.

I wish there were a simpler solution to the ambiguity problem. But you just need to accept it. Learning to code is an ambiguous process. And even experienced developers grapple with ambiguity.

After all, coding is the rare profession where you can just infinitely reuse solutions to problems you've previously encountered.

Thus as a developer, you are always doing something you've never done before.

People think software development is about typing code into a computer. But it's really about learning.

You're going to spend a huge portion of your career just thinking really hard. Or blindly inputting commands into a prompt trying to understand how a system works.

And you're going to spend a lot of time in meetings with other people: managers, customers, fellow devs. Learning about the problem that needs to be solved, so you can build a solution to it.

Get comfortable with ambiguity and you will go far.

Learning to Code is One Error Message After Another

A lot of people who are learning to code feel like they hit a wall. Progress does not come as fast as they expect.

One huge reason for this: in programming, the feedback loop is much tighter than in other fields.

In most schools, your teacher will give you assignments, then grade those assignments and give them back to you. Over the course of a semester, you may only have a dozen instances where you get feedback.

"Oh no, I really bombed that exam," you might say to yourself. "I need to study harder for the midterm."

Maybe your teacher will leave notes in red ink on your paper to help you improve your work.

Getting a bad grade on an exam or paper can really ruin your day.

And that's how we generally think about feedback as humans.

If you've spent much time coding, you know that computers are quite fast. They can execute your code within a few milliseconds.

Most of the time your code will crash.

If you're lucky, you'll get an error message.

And if you're really lucky, you'll get a "stack trace" – everything the computer was trying to do when it encountered the error – along with a line number for the piece of code that caused the program to crash.

Now this in-your-face negative feedback from a computer. Not everyone can handle it seeing this over and over all day long.

Imagine if every time you handed your teacher your term paper, they handed it back with big red "F" written on it. And imagine they did this before you could even blink. Over and over.

That's what coding can feel like sometimes. You want to grab the computer and shout at it, "why don't you just understand what I'm trying to do?"

How Not to Get Frustrated

The key, again, is practice.

Over time, you will develop a tolerance for vague error messages and screen-length stack traces.

Coding will never be harder than it is when you're just starting out.

Not only do you not know what you're doing, but you're not used to receiving such impersonal, rapid-fire, negative feedback.

So here are some tips:

Tip #1: Know that you are not uniquely bad at this.

Everyone who learns to code struggles with the frustration of trying to Vulcan Mind Meld with a computer, and get it to understand you. (That's another Star Trek reference.)

Of course, some people started programming when they were just kids. They may act like they've always been good at programming. But they most likely struggled just like we adults do, and over time have simply forgotten the hours of frustration.

Think of the computer as your friend, not your adversary. It's just asking you to clarify your instructions.

Tip #2: Breathe.

Many people's natural reaction when they get an error message is to gnash their teeth. Then go back into their code editor and start blindly changing code, hoping to somehow luck into getting past it.

This does not work. And I'll tell you why.

The universe is complex. Software is complex. You are unlikely to just Forest Gump your way into anything good.

Forest Gump doing what he does and getting improbably lucky catching shrimp.

You may have heard of the Infinite Monkey Theorem. It's a thought experiment where you imagine chimpanzees typing on typewriters.

If you had a newsroom full of chimpanzees doing this, how long would it take before one of them typed out the phrase "to be or not to be" by random chance?

Let's say each chimp types one random character per second. It would likely take 1 quintillion years for one of them to type "to be or not to be." That's 10 to the 18th power. A billion billion.

Even assuming the chimps remain in good health and the typewriters are regularly serviced – the galaxy would be a cold, dark void by the time one of them managed to type "to be or not to be."

Why do I tell you all of this? Because you don't want to be one of those chimps.

In that time, you could almost certainly figure out a way to teach those chimps how to type English words. They could probably manage to type out all of Hamlet – not just its most famous line.

Even if you somehow do get lucky, and get past the bug, what will you have learned?

So instead of thrashing, you want to take some time. Understand the code. Understand what's happening. And then fix the error.

Always take time to understand the failing code. Don't be a quintillionarian chimp. (I think that means someone who is 1 quintillion years old, though according to Google, nobody has ever typed that word before.)

Instead of blindly trying things, hoping to get past the error message, slow down.

Take a deep breath. Stretch. Get up to grab a hot beverage.

Your future self will be grateful that you took this as a teachable moment.

Tip #3: Use Rubber Duck Debugging

Get a rubber ducky and set it next to your computer. Every time you hit an error message, try to explain what you think is happening to your rubber duck.

Of course, this is silly. How could this possibly be helpful?

Except it is.

Rubber Duck Debugging is a great tool for slowing down and talking through the problem at hand.

You don't have to use a rubber duck, of course. You could explain your Python app to your pet cactus. Your SQL query to the cat that keeps jumping onto your keyboard.

The very act of explaining your thinking out loud seems to help you process the situation better.

How do Most People Learn to Code?

Now let's talk about traditional pathways to a first developer job.

Why should you care what everyone else does? Spoiler alert: you don't really need to.

You do you.

This said, you may doubt yourself and the decisions you've made about your learning. You may yearn for the path not taken.

My goal with this section is to calm any anxieties you may have.

The Importance of Computer Science Degrees

University degrees are still the gold standard for preparing for a career in software development. Especially bachelor's degrees in Computer Science.

Before you start saying "But I don't have a computer science degree" – no worries. You don't need a Computer Science degree to become a developer.

But their usefulness is undeniable. And I'll explain why.

First, you may wonder: why should developers study computer science? After all, one of the most prominent developers of all time had this to say about the field:

"Computer science education cannot make anybody an expert programmer any more than studying brushes and pigment can make somebody an expert painter." – Eric Raymond, Developer, Computer Scientist, and Author

Computer Science departments were traditionally part of the math department. Universities back in the 1960s and 1970s didn't know quite where to put this whole computer thing.

At other universities, Computer Science was considered an extension of Electrical Engineering. And until recently, even University of California – Berkeley – one of the greatest public universities in the world – only provided Computer Science degrees as sort of a double-major with Electrical Engineering.

But most universities have now come to understand the importance of Computer Science as a field of study.

As of writing this, Computer Science is the highest paying degree you can get. Higher even than fields focused on money, such as Finance and Economics.

According to Glassdoor, the average US-based Computer Science major makes more money at their first job than any other major. US $70,000. That's a lot of money for someone who just graduated from college.

More than Nursing majors ($59,000), Finance majors ($55,000) and Architecture majors ($50,000).

OK – so getting a Computer Science degree can help you land a high-paying entry-level job. That is probably news to no one. But why is that?

How Employers Think About Bachelor's Degrees

You may have heard some big employers in tech say things like, "we no longer require job candidates to have a bachelor's degree."

Google said this. Apple said this.

And I believe them. That they no longer require bachelor's degrees.

We've had lots of freeCodeCamp alumni get jobs at these companies, some of whom did not have a bachelor's degrees.

But those freeCodeCamp alumni who landed those jobs probably had to be extra strong candidates to overcome the fact that they didn't have bachelor's degrees.

You can look at these job openings as having a variety of criteria they judge candidates on:

1. Work experience
2. Education
3. Portfolio and projects
4. Do they have a recommendation from someone who already works at the company? (We'll discuss building your network in depth in Chapter 2)
5. Other reputation considerations (we'll discuss building your reputation in Chapter 3)

For these employers who do not require a bachelor's degree, education is just one of several considerations. If you are stronger in other areas, they may opt to interview you – regardless of whether you've ever even set foot inside a university classroom.

Just note that having a bachelor's degree will make it easier for you to get an interview, even at these "degree-optional" employers.

Why do so Many Developer Jobs Require a Computer Science Degree Specifically?

A bachelor's is a bachelor's, I often tell people. Because for most intents and purposes, it is.

Want to enter the US military as an officer, rather than an enlisted service member? You'll need a bachelor's degree, but any major will do.

Want to get a work visa to work abroad? You'll probably need a bachelor's degree, but any major will do.

And for so many job openings that say "bachelor's degree required" – any major will do.

Why is this? Doesn't the subject you study in university matter at all?

Well, here's my theory on this: what you learn in university is less important than whether you finished university.

Employers are trying to select for people who can figure out a way to get through this rite of passage.

It is certainly true that you can be at the bottom of your class, repeating courses you failed, and being on academic probation for half the time. But a degree is a degree.

You know what they call the student who finished last in their class at medical school? "Doctor."

And for most employers, the same holds true.

In many cases, HR folks are just checking a box on their job application filtering software. They're filtering out applicants who don't have a degree. In those cases, they may never even look at job applications from people without degrees.

Again, not every employer is like this. But many of them are. Here in the US, and perhaps even more so in other countries.

It sucks, but it's how the labor market works right now. It may change over the next few decades. It may not.

This is why I always encourage people who are in their teens and 20s to seriously considering getting a bachelor's degree.

Not because of any of the things universities market themselves as:

• The education itself. (You can take courses from some of the best universities online for free, so this alone does not justify the high cost of tuition.)
• The "college experience" of living in a dorm, making new friends, and self discovery. (Most US University students never live on campus so they don't really get this anyway.)
• General education courses that help you become a "well rounded individual" (Ever hear of the Freshman 15? This is a joke of course. But a lot of university freshman do gain weight due to the stress of the experience.)

Again, the real value of getting a bachelor's degree – the real reason Americans pay $100,000 or more for 4 years of university – is because many employers require degrees.

Of course, there are other benefits of having a bachelor's degree, such as the ones I mentioned: expanded military career options, and greater ease getting work visas.

One of these is: if you want to become a doctor, dentist, lawyer, or professor, you will first need a bachelor's degree. You can then use that to get into grad school.

OK – this is a lot of background information. So allow me to answer your questions bluntly.

Do You Need a University Degree to Work as a Software Developer?

No. There are plenty of employers who will hire you without a bachelor's degree.

A bachelor's degree will make it much easier to get an interview at a lot of employers. And it may also help you command a higher salary.

What About Associate's Degrees? Are Those Valuable?

In theory, yes. There are some fields in tech where having an associates may be required. And I think it always does increase your chances of getting an interview.

This said, I would not recommend going to university with the specific goal of getting an associate's degree. I would 100% encourage you to stay in school until you get a bachelor's degree, which is vastly more useful.

According to the US Department of Education, over the course of your career, having a bachelor's degree will earn you 31% more than merely having an associate's degree.

And I'm confident that difference is much wider with a bachelor's in Computer Science.

Is it Worth Going to University to Get a Bachelor's Degree Later in Life, if You Don't Already Have One?

Let's say you're in your 30s. Maybe you attended some college or university courses. Maybe you completed the first two years and were able to get an associate's degree.

Does it make sense to go "back to school" in the formal sense?

Yes, it may make sense to do so.

But I don't think it ever makes sense to quit your job to go back to school full time.

The full-time student lifestyle is really designed with "traditional" students in mind. That is, people age 18 to 22 (or a bit older if they served in the military), who have not yet entered the workforce beyond high school / summer jobs.

Traditional universities cost a lot of money to attend, and the assumption is that students will pay through some combination of scholarships, family funds, and student loans.

As a working adult, you'll have less access to these funding sources. And just as importantly, you'll have less time on your hands than a recent high school graduate would.

But that doesn't mean you have to give up on the dream of getting a bachelor's degree.

Instead of attending a traditional university, I recommend that folks over 30 attend one of the online nonprofit universities. Two that have good reputations, and whose fees are quite reasonable, are Western Governor's University and University of the People.

You may also find a local community college or state university extension program that offers degrees. Many of these programs are online. And some of them are even self-paced, so that you can complete courses as your work schedule permits.

Do your research. If a school looks promising, I recommend finding one of its alumni on LinkedIn and reaching out to them. Ask them questions about their experience, and whether they think it was worth it.

I recommend not taking on any debt to finance your degree. It is much better to attend a cheaper school. After all, a degree is a degree. As long as it's from an accredited institution, it should be fine for most intents and purposes.

If You Already Have a Bachelor's Degree, Does it Make Sense to Go Back and Earn a Second Bachelor's in Computer Science?

No. Second bachelor's degrees are almost never worth the time and money.

If you have any bachelor's degree – even if it's in a non-STEM field – you have already gotten most of the value you will get out of university.

What About a Master's of Computer Science Degree?

These can be helpful for career advancement. But you should pursue them later, after you're already working as a developer.

Many employers will pay for their employee's continuing education.

One program a lot of my friends in tech have attended is Georgia Tech's Master's in Computer Science degree.

Georgia Tech's Computer Science department is among the best in the US. And this degree program is not only fully online – it's also quite affordable.

But I wouldn't recommend doing it now. First focus on getting a developer job. (We'll cover that in-depth later in this book).

Will Degrees Continue to Matter in the Future?

Yes, I believe that university degrees will continue to matter for decades – and possibly centuries – to come.

University degrees have existed for more than 1,000 years.

Many of the top universities in the US are older than the USA itself is. (Harvard is more than 400 years old.)

The death of the university degree is greatly exaggerated.

It has become popular in some circles to bash universities, and say that degrees don't matter anymore.

But if you look at the statistics, this is clearly not true. They do have an impact on lifetime earnings.

And just as importantly, they can open up careers that are safer, more stable, and ultimately more fulfilling.

Sure, you can make excellent money working as a deckhand offshore, servicing oil rigs.

But you can make similarly excellent money working as a developer in a climate-controlled office, servicing servers and patching codebases.

One of these jobs is dangerous, back-breaking work. The other is a job you could comfortably do for 40 years.

Many of the "thought leaders" out there who are bashing universities have themselves benefitted from a university education.

One reason why I think so many people think degrees are "useless" is: it's hard to untangle the learning from the status boost you get.

Is university just a form of class signaling – a way for the wealthy to continue to pass advantage on to their children? After all, you're 3 times as likely to find a rich kid at Harvard as you are a poor kid.

The fact is: life is fundamentally unfair. But that does not change how the labor market works.

You can choose easy mode, and finish a degree that will give you more options down the road.

Or you can go hard mode, potentially save time and money, and just be more selective about which employers you apply to.

I have plenty of friends who've used both approaches to great success.

What Alternatives are There to a University Degree?

I've worked in adult education for nearly two decades, and I have yet to see a convincing substitute for a university degree.

Sure – there are certification programs and bootcamps.

But these do not carry the same weight with employers. And they are rarely as rigorous.

Side note: when I say "certification programs" I mean a program where you attend a course, then earn a certification at the end. These are of limited value. But exam-based certifications from companies like Amazon and Microsoft are quite valuable. We'll discuss these in more depth later.

What I tell people is: to degree or not to degree – that is the question.

I meet lots of people who are auto mechanics, electricians, or who do some other sort of trade, who don't have a bachelor's. They can clearly learn a skillset, apply it, and hold down a job.

I meet lots of people who are bookkeepers, paralegals, and other "knowledge workers" who don't have a bachelor's. They can clearly learn a skillset, apply it, and hold down a job.

In many cases, these people can just learn to code on their own, using free learning resources and hanging out with likeminded people.

Some of these people have always had the personal goal of going back and finishing their bachelor's. That's a good reason to do it.

But it's not for everyone.

If you want formal education, go for the bachelor's degree. If you don't want formal education, don't do any program. Just self-teach.

The main thing bootcamps and other certification programs are going to give you is structure and a little bit of peer pressure. That's not a bad thing. But is it worth paying thousands of dollars for it?

How to Teach Yourself to Code

Most developers are self-taught. Even the developers who earned a Bachelor's of computer science still often report themselves as "self-taught" on industry surveys like Stack Overflow's annual survey.

Most working developers consider themselves to be "self-taught" (Image: Stack Overflow 2016 Survey)

This is because learning to code is a life-long process. There are constantly new tools to learn, new legacy codebases to map out, and new problems to solve.

So whether you pursue formal education or not, know this: you will need to get good at self-teaching.

What Does it Mean to be a "Self-Taught" Developer?

Not to be pedantic, but when I refer to self-teaching, I mean self-directed learning – learning outside of formal education.

Very few people are truly "self-taught" at anything. For example, Isaac Newton taught himself Calculus because there were no Calculus books. He had to figure it out and invent it as he went along.

Similarly, Ada Lovelace taught herself programming. Because before her there was no programming. She invented it.

Someone might tell you: "You're not really self taught because you learned from books or online courses. So you had teachers." And they are correct, but only in the most narrow sense.

If someone takes issue with you calling yourself self-taught, just say: "By your standards, no one who wasn't raised by wolves can claim to be self-taught at anything."

Point them to this section of this book and tell them: "Quincy anticipated your snobbery." And then move on with your life.

Because come on, life's too short, right?

You're self taught.

What is Self-Directed Learning?

As a self-learner, you are going to curate your own learning resources. You're going to choose what to learn, from where. That is the essence of "Self-Directed Learning."

But how do you know you're learning the right skills, and leveraging the right resources?

Well, that's where community comes in.

There are lots of communities of learners around the world, all helping one another expand their skills.

Community is a hard word to define. Is Tech Twitter a community? What about the freeCodeCamp forum? Or the many Discord groups and subreddits dedicated to specific coding skillsets?

I consider all of these communities. If there are people who regularly hang out there and help one another, I consider it a community.

What about in-person events? The monthly meetup of Ruby developers in Oakland? The New York City Startup community meetup? The Central Texas Linux User Group?

These communities can be online, in-person, or some mix of both.

We'll talk more about communities in the Build Your Network chapter. But the big takeaway is: the new friends you meet in these communities can help you narrow your options for what to learn, and which resources to learn from.

What Programming Language Should I Learn First?

The short answer is: it doesn't really matter. Once you've learned one programming language well, it is much easier to learn your second language.

There are different types of programming languages, but today most development is done using "high-level scripting languages" like JavaScript and Python. These languages trade away the raw efficiency you get from "low-level programming languages" like C. What they get in return: the benefit of being much easier to use.

Today's computers are billions of times faster than they were in the 1970s and 1980s, when people were writing most of their programs in languages like C. That power more than makes up for the relative inefficiency of scripting languages.

It's worth noting that both JavaScript and Python themselves are written in C, and they are both getting faster every year – thanks to their large communities of open source code contributors.

Python is a powerful language for scientific computing (Data Science and Machine Learning).

And JavaScript... well, JavaScript can do everything. It is the ultimate Swiss Army Knife programming language. JavaScript is the duct tape that holds the World Wide Web together.

"Any application that can be written in JavaScript, will eventually be written in JavaScript." – Atwood's Law (Jeff Atwood, founder of Stack Overflow and Discourse)

You could code your entire career in JavaScript and would never need to learn a second language. (This said, you'll want to learn Python later on, and maybe some other languages as well.)

So I recommend starting with JavaScript. Not only is it much easier to use than languages like Java and C++ – it's easier to learn, too. And there are far, far more job openings for people who know JavaScript.

A screenshot from job search engine Indeed. My search for "javascript" for the US yielded 68,838 job listings.

The other skills you'll want to focus on are HTML and CSS. If a webpage were a body, HTML would be the bones, and CSS would be the skin. (JavaScript would be the muscles, making it possible for the website to move around and be interactive.)

You can learn some HTML and CSS in a single afternoon. Like most of the tools I mention here, they are easy to learn, but difficult to master.

You'll also want to learn how to use Linux. Linux powers a vast majority of the world's servers, and you will spend much of your career running commands in the Linux command line.

If you have a Mac, MacOS has a terminal that accepts almost all the same commands as Linux. (MacOS and Linux have a common ancestor in Unix.)

But if you're on a Windows PC, you'll want to install WSL, which stands for Windows Subsystem for Linux. You will then be able to run Linux commands on your PC. And if you're feeling adventurous, you can even dual boot both the Windows and Linux operating systems on the same computer.

If you're going to install Linux on a computer, I recommend starting with Ubuntu. It is the most widely used (and widely documented) Linux distribution. So it should be the most forgiving.

Make no mistake – Linux is quite a bit harder to use than Windows and MacOS. But what you get in return for your efforts is an extremely fast, secure, and highly customizable operating system.

Also, you will never have to pay for an operating system license again. Unless you want to. Red Hat is a billion dollar company even though its software is open source, because companies pay for their help servicing and supporting Linux servers.

You'll also want to learn Git. This Version Control System is how teams of developers coordinate their changes to a codebase.

You may have heard of GitHub. It's a website that makes it easier for developers to collaborate on open source projects. And it further extends some of the features of Git. You'll learn more about GitHub in the How to Build Your Reputation chapter later.

You'll want to learn SQL and how relational databases work. These are the workhorses of the information economy.

You'll also hear a lot about NoSQL databases (Non-relational databases such as graph databases, document databases, and key-value stores.) You can learn more about these later. But focus on SQL first.

Finally, you'll want to learn how web servers work. You'll want to start with Node.js and Express.js.

When you hear the term "full stack development" it refers to tying together the front end (HTML, CSS, JavaScript) with the back end (Linux, SQL databases, and Node + Express).

There are lots of other tools you'll want to learn, like React, NGINX, Docker, and testing libraries. You can pick these up as you go.

But the key skills you should spend 90% of your pre-job learning time on are:

1. HTML
2. CSS
3. JavaScript
4. Linux
5. Git
6. SQL
7. Node.js
8. Express.js

If you learn these tools, you can build most major web and mobile apps. And you will be qualified for most entry-level developer jobs. (Of course, many job descriptions will include other tools, but we'll discuss these later in the book.)

So you may be thinking: great. How do I learn these?

Where do I learn how to code?

Funny you should ask. There's a full curriculum designed by experienced software engineers and teachers. It's designed with busy adults in mind. And it's completely free and self-paced.

That's right. I'm talking about the freeCodeCamp core curriculum. It will help you learn:

• Front End Development
• Back End Development
• Engineering Mathematics
• and Scientific Computing (with Python for Data Science and Machine Learning)

To date, thousands of people have gone through this core curriculum and gotten a developer job. They didn't need to quit their day job, take out loans, or really risk anything other than some of their nights and weekends.

In practice, freeCodeCamp has become the default path for most people who are learning to code on their own.

If nothing else, the freeCodeCamp core curriculum can be your "home base" for learning, and you can branch out from there. You can learn the core skills that most jobs require, and also dabble in technologies you're interested in.

There are decades worth of books and courses to learn from. Some are available at your public library, or through monthly subscription services. (And you may be able to access some of these subscription services for free through your library as well.)

Also, freeCodeCamp now has nearly 1,000 free full-length courses on everything from AWS certification prep to mobile app development to Kali Linux.

There has never been an easier time to teach yourself programming.

Building Your Skills is a Life-Long Endeavor

We've talked about why self-teaching is probably the best way to go, and how to go about it.

We've talked about the alternatives to self-teaching, such as getting a bachelor's degree in Computer Science, or getting a Master's degree.

And we've talked about which specific tools you should focus on learning first.

Now, let's shift gears and talk about how to build the second leg of your stool: your network.

Chapter 2: How to Build Your Network

"If you want to go fast, go alone. If you want to go far, go together." – African Proverb

"Networking." You may wince at the sound of that word.

Networking may bring to mind awkward job fairs in stuffy suits, desperately pushing your résumé into the hands of anyone who will accept it.

Networking may bring to mind alcohol-drenched watch parties – where you pretend to be interested in a sport you don't even follow.

Networking may bring to mind wishing "happy birthday" to people you barely know on LinkedIn, or liking their status updates hoping they'll notice you.

But networking does not have to be that way.

In this chapter, I'll tell you everything I've learned about meeting people. I'll show you how to earn their trust and be top of their mind when they're looking for help.

Because at the end of the day, that's what it's all about. Helping people solve their problems. Being of use to people.

I'll show you how to build a robust personal network that will support you for decades to come.

Story Time: How did a Teacher in his 30s Build a Network in Tech?

Last time on Story Time: Quincy learned some coding by reading books, watching free online courses, and hanging out with developers at the local Hackerspace. He had just finished building his first project and given his first tech talk...

OK – so I now had some rudimentary coding skills. I could now code my way out of the proverbial paper bag.

What was next? After all, I was a total tech outsider.

Well, even though I was new to tech, I wasn't new to working. I'd put food on the table for nearly a decade by working at schools and teaching English.

As a teacher, I got paid to sling knowledge. And as a developer, I'd get paid to sling code.

I already knew one very important truth about the nature of work: it's who you know.

I knew the power of networks. I knew that the path to opportunity goes right through the gatekeepers.

All that stood between me and a lucrative developer job was a hiring manager who could say: "Yes. This Quincy guy seems like someone worthy of joining our team."

Of course, being a tech outsider, I didn't know the culture.

Academic culture is much more formal.

You wear a suit.

You use fancy academic terminology to demonstrate you're part of the "in group."

You find ways to work into every conversation that you went to X university, or that you TA'd under Dr. Y, or that you got published in The Journal of Z.

Career progressions are different. Conferences are different. Power structures are different.

And I didn't immediately appreciate this fact.

The first few tech events I went to, I wore a suit.

I kept neatly-folded copies of my résumé in my pocket at all times.

I even carried business cards. I had ordered sheets of anodized aluminum, and used a laser cutter to etch in my name, email address, and even a quote from legendary educator John Dewey:

"Anyone who has begun to think places some portion of the world in jeopardy." – John Dewey

It's still my favorite quote to this day.

But talk about heavy-handed.

"Hi, I'm Quincy. Here's my red aluminum business card. Sorry in advance – it might set off the metal detector on your flight home."

I was trying too hard. And it was probably painfully apparent to everyone I talked to.

I went on Meetup.com and RSVP'd for every developer event I could find. Santa Barbara is a small town, but it's near Los Angeles. So I made the drive for events there, too.

I quickly wised up, and traded my suit for jeans and a hoody. And I noticed that no one else gave out business cards. So I stopped carrying them.

I took cues from the devs I met at the hackerspace: Be passionate, but understated. Keep some of your enthusiasm in reserve.

And I read lots of books to better understand developer culture.

The Coders at Work is a good book from the 1980s.

Hackers: Heroes of the Revolution is a good book from the 1990s.

For a more contemporary cultural resource, check out the TV series Mr. Robot. Its characters are a bit extreme, but they do a good job of capturing the mindset and mannerisms of many developers.

Soon, I was talking less like a teacher and more like a developer. I didn't stick out quite as awkwardly.

Several times a week I attended local tech-related events. My favorite event wasn't even a developer event. It was the Santa Barbara Startup Night. Once every few weeks, they'd have an event where developers would pitch their prototypes. Some of the devs demoing their code were even able to secure funding from angels – rich people who invest in early-stage companies.

The guy who ran the event was named Mike. He must have known every developer and entrepreneur in Santa Barbara.

When I finally got the nerve to introduce myself to Mike, I was star-struck. He was an ultra-marathoner with a resting heartbeat in the low 40s. Perfectly cropped hair and beard. To me he was the coolest guy on the planet. Always polished. Always respectful.

Mike was "non-technical". He worked as a product manager. And though he knew a lot about technology and user experience design, he didn't know how to code.

Sometimes devs would write non-technical people off. "He's just a business guy," they'd say. Or: "She's a suit." But I never heard anyone say that about Mike. He had the respect of everyone.

I made a point to watch the way Mike interacted with developers. After all, I wasn't that far removed from "non-technical" myself. I'd only been coding for a few months.

Often my old habits would creep in. During conversations I'd have the temptation to show off what I'd learned or what I'd built.

Many developers are modest about their skills or accomplishments. They might say: "I dabble in Python." And little 'ol insecure me would open his big mouth and say something like, "Oh yeah. I've coded so many algorithms in Python. I write Python in my sleep."

And then I'd go home and google that developer's name, and realize they were a core contributor to a major Python library. And I'd kick myself.

I quickly learned not to boast of my accomplishments or my skills. There's a good chance a person you're talking to can code circles around you. But most of them would never volunteer this fact.

There's nothing worse than confidently pulling out your laptop, showing off your code, and then having someone ask you a bunch of questions that you're wholly unprepared to answer.

My first few months of attending events was a humbling experience. But these events energized me to keep pushing forward with my skills.

Soon people around southern California would start to recognize me. They'd say: "I keep running to you at these events. What's your name again?"

One night a dev said, "Let's follow each other on Twitter." I had grudgingly set up a Twitter account a few days earlier, thinking it was a gimmicky website. How much could you really convey with just 140 characters? I had barely tweeted anything. But I did have a Twitter account ready, and she did follow me.

That inspired me to spend more time refining my online presence. I made my LinkedIn less formal and more friendly. I looked at how other devs in the community presented themselves online.

Within a few months, I knew people from so many fields:

• experienced developers
• non-technical or semi-technical people who worked at tech companies
• hiring managers and recruiters
• and most importantly, my peers who were also mid-career and trying to break into tech

Why were peers the most important? Surely they would be the least able to help me get a job, right?

Well, let me tell you a secret: let's say a hiring manager brings on a new dev, trains them, and they turn out to be really good at their job. That hiring manager is going to ask: where can I find more people like you?

Your peers are one of the most important pieces of your network. So many of my freelance opportunities and job interview opportunities came from people who started learning to code around the same time as I did.

We came up together. We were brothers and sisters in arms. Those bonds are the tightest.

Anyway, all this networking over the months would ultimately come to fruition one night when I walked into the bar of a fancy downtown hotel for a developer event.

But more on that in the next chapter. Now let's talk more about the art and science of building your network.

Is it Really Who You Know?

You may have heard the expression that success is "less about what you know, and more about who you know."

In practice, it's about both.

Yes – your connections may help you land your dream job. But if you're out of your depth, and lack the skills to succeed, you will not fare well in that role.

But let's assume that you are proactively building your skills. You've followed my advice from Chapter 1. When is the right time to start building your network?

The best time to start building your network is yesterday.

But you don't need a time machine to do this. Because you already have a network. It's probably much smaller than you'd like it to be, but you do know people.

They may be friends from your home town, or the colleagues of your parents. Any person you know from your past – however marginally – may be of help.

So step one is to take full inventory of the people you know. Don't worry – I am not asking you to reach out to anyone yet, or tax your personal relationships.

Think before you move. Formulate a strategy.

First, let's inventory all the people you know.

How to Build a Personal Network Board

You want to start by creating a list of people you know.

You could do this with a spreadsheet, or a Customer Relationship Management tool (CRM) like sales people use. But that's probably overkill for what we're doing here.

I recommend using a Kanban board tool like Trello, which is free.

You're going to create 5 columns: "to evaluate", "to contact", "waiting for reply", "recently in contact", and "don't contact yet".

Then you're going to want to create labels, so you can classify people by how you know them. Here are some label ideas for you: "Childhood friend", "Friend of the family", "Former colleague", "Classmate", "Friends from Tech Events".

Now you can start creating cards. Each card can just be their name, and if you have time you can add a photo to the card.

Here is the Trello board I created to give you an idea of what this Personal Network Board might look like. I used characters from my favorite childhood movie, the 1989 classic Teenage Mutant Ninja Turtles.

My Personal Network Board with my friends from my side job fighting crime.

You can go through your social media accounts – even your old school year books if you have them – and start adding people.

Many of these people are not going to be of any help. But I recommend adding them for the sake of being comprehensive. You never know when you'll remember: "oh – so and so got a job at XYZ corp. I should reach out to them."

This process may take a day or two. But know that this is an investment. You'll be able to use this board for the rest of your career.

You may think "I don't need to do this – I already have a LinkedIn account." That might work OK, but LinkedIn is a blunt instrument. You want to maximize signal and minimize noise here. That's why I'm encouraging you to create this dedicated personal network board.

As you add people to your board, you can label them. Take a moment to research each of these people. What are they up to these days? Do they have a job? Run a company?

You can add notes to each card, as you discover new facts about them. Did they recently run a fundraiser 5K run? Did their grandma recently celebrate her 90th birthday? These facts may seem extraneous. But if the person is sharing them on social media, it means these facts are important to them.

Make an effort to be interested in people. Their daily lives. Their aspirations. By understanding their motivations and goals, you will have deeper insight into how you can help them.

And as I said earlier, the best way to forge alliances is to help people. We'll talk about this at length in a little bit.

For each of the people you add to your Personal Network Board, consider whether they might be worth reaching out to. Then either put them into the "to contact" or "don't contact yet" column.

You may be wondering: why is the column called "don't contact yet"? Because you never know when it might be helpful to know someone. Never take any friendship or acquaintanceship for granted.

Once you've filled up your board, labeled everyone, and sorted them into columns, you're ready to start reaching out.

How to Prepare for Network Outreach

The main thing to keep in mind when reaching out and trying to make an impression: keep yourself simple.

People are busy, and they can only remember so many facts about you. You want to boil down who you are to the fundamentals. And the best way to do this is to write a personal bio.

How to Write a Personal Bio for Social Media

You want your presence to be consistent across all of your social media accounts.

Here's how I introduce myself:

"I'm Quincy. I'm a teacher at freeCodeCamp. I live in Dallas, Texas. I can help you learn to code."

Go ahead and write yours. See if you can get it down to 100 characters or less. Try to avoid using fancy words or jargon.

It may be hard to distill your identity down to a few words. But this is an important process.

Remember: people are busy. They don't need to know your life story. As you get to know these people better, you can gradually fill in the details of who you are as a person. As they ask questions, they can get to know you better over time.

And on that note, you need a good photo of your smiling face.

How to Make a Social Media Headshot

If you have the money, just find a local photographer and pay them to take some professional headshots.

You may even have a friend who's into photography, who can take them for free.

I took my headshot myself, using Photobooth, which comes pre-installed on MacOS. My friend spent about 10 minutes fixing some background and shading in Photoshop. He may have made my teeth slightly whiter. Here's what it looks like:

My headshot. I use this same photo everywhere.

Be sure to smile with your eyes, so you don't look robotic. Or better yet, think of something really funny, like I did here. Then the smile will be genuine.

Take a lot of shots from different angles, and just use whichever one looks best on you.

I recommend using a headshot that looks like how you look on any given day. Not a heavily photoshopped photo that tries to maximize your attractiveness. You want people at events to recognize you from your photo. And you don't want to intimidate people with your beauty. You want to put them at ease.

Speaking of putting people at ease: do not wear sunglasses, or try too hard to look cool. You want to look friendly and approachable. A good acid test for this is: look at your photo. If you were lost, and saw this person on the street, would you be brave enough to ask them for directions?

Once you have chosen your headshot photo, use that same photo everywhere. Put it on all of your social media accounts.

Use it on your personal website. Even add the profile photo to your email account.

I recommend using that same photo for years. Every time you change it, you run the risk that some people won't immediately recognize you. Even subtle changes in lighting, angle, or background can throw off people's familiarity.

Be sure to keep a high-definition version of the photo. That way people can use it to promote your talk at their conference, or your guest appearance on their podcast. (Don't worry – in time, you will get there.)

How to Reach Out to People from your Past

Now that you've got your bio and photos sorted out, you're ready to start talking with people.

15 years ago, I would say you should call people on the phone instead of messaging them. But culture has changed a lot with the introduction of smart phones. Most people will not respond well to a phone call.

Similarly, I don't recommend asking people out to coffee or lunch until much later in the conversation. People are busy, and may view the request as awkward.

You need to get to the point, and do so quickly.

So what is that point you need to get to?

Essentially:

1. I know you
2. I like you
3. and I respect the work you're doing.

That's it.

People like to be known. They like to be liked. They like for the work they do and the lives they live to be noticed.

Most of us get recognition on our birthdays. People from our past might send "happy birthday" text messages, social media posts, or even call us.

But what about the other 364 days of the year? People like to be recognized on those other days, too.

Well, here's a simple way you can recognize people.

Step 1: Research the person. Google them. Read through their most recent social media posts. Read through their LinkedIn. If they post family photos, actually take time to look at them.

Step 2: Think about something you could say that might make their day a bit brighter.

Step 3: Choose a social media platform they've been recently active on. Send them a direct message.

I'm going to share a template, but never use any templates verbatim, because if the recipient plugs your message into Google, they'll discover it's a template, and all your goodwill will be squandered.

If I were messaging someone I hadn't talked to in a few months or years out of the blue, I would say something like this:

"Hey [name], I hope your [new year / spring / week] is off to a fun start. Congrats on [new job / promotion / new baby / completed project]. It's inspiring to see you out there getting things done."

Something short and to the point like that. Greeting + congratulations + compliment. That is the basic formula.

Don't just say it. Mean it.

Really want this person to feel recognized. Really want to brighten their day. Really want to encourage them to keep progressing toward their goals.

Humans are very good at detecting insincerity. Don't try to over-sell it. Don't give them any reason to think "this person wants something from me."

That's why the most important thing about this is: be brief. Be respectful of people's time. Nobody wants a long letter that they'll feel obligated to respond to at length.

Because – say it with me again – people are busy.

How to Build Even Deeper Connections

Because people are so busy, they're often tempted to see strangers more for what those strangers can do for them:

• This person drives the bus that gets me to work.
• This person makes my beverage just the way I like it.
• This person in HR answers my questions about time off.
• This person put together a bangin' acid jazz playlist for me to listen to while I code.
• This person sends me helpful emails each week with free coding resources.

To some extent, you are what you do for people.

I know, I know. That might sound overly reductive. Cynical even. And that is 100% not true for the close friends and family in your life.

But for people who barely know you – who just encounter you while going about their day – this is likely how they see you.

You have to give people a reason to care about you. You have to inspire them to learn more about you.

Before you can become somebody's close friend – someone they truly care about, and think about when you're not around – you need to start off as someone who is helpful to them.

And that's what we're going to do here. We're going to build even deeper relationships by offering to help people.

This will be a long process. And you should start it well in advance of your job search. The last thing you want is for someone to think "Oh – you're just reaching out because you need something from me."

On the contrary – you're reaching out because you have something to offer them.

You are, after all, in possession of one of the most powerful skillsets a person can acquire. The ability to bend machines to your will. You are a programmer.

This is what being good at coding feels like.

Or, at least, you're on the road to becoming one.

So you already have a good pretext to reach out to people.

You may have heard the term "cold call". This is where you call someone knowing almost nothing about them, and trying to sell them something. This is not easy, and a vast majority of cold calls end with the other party hanging up.

But the more information you know about the other person, the warmer the call gets, and the more likely you are to succeed.

Now, you're not selling anything here. And as I mentioned earlier, you're not calling them either. You're sending them a direct message.

Maybe this is through Twitter, LinkedIn, Discord, Reddit – wherever. But you are reaching out to them with a single paragraph of text.

As I said, the strongest opening move – the approach that's most likely to get a response – is to casually offer help.

If I were doing this, here's a simple template I'd use. Remember not to use this template verbatim. Rewrite it in your own voice, how you would say it to a friend:

"Hey [name], congrats on the [new job / promotion / new baby]. I've been learning some programming, and am building my portfolio. You immediately came to mind as someone who gets a lot of things done. Is there any sort of tool or app that would make your life easier? I may be able to code it up for you, for practice."

This is a strong approach, because it is personalized and doesn't come across as automated. People get so many automated messages these days that they are quick to disregard anything that even resembles an automated message.

This is why I send all my messages manually, and don't rely on automation. It's better to slowly compose messages one-by-one than it is try and save time with a script or a mail-merge.

The fastest way to get blocked is to message someone with "Hi , how's it going?" where there's clearly a first name missing – evidence that the message is a template.

Sometimes I get a message using my last name instead of my first name. "Hey Larson." What, am I in military school now?

And a lot of people on LinkedIn have started putting an emoji at the beginning of their name. This makes it easy to detect automated messages, because nobody would include that emoji in their direct message.

When a message starts with: "Hi 🍜Sarah, are you looking for a new job?" Then you know it's a bulk message.

Also note that my above template does not say "we went to school together" or something like that. Unless you just met someone a few days ago, you shouldn't specify how you two know one another.

Why? Because the very act of reminding people how you know one another will prompt some people to step back and think: "Gee, I barely know this person."

How to Keep the Conversation Going

Again, your goal is to get a response from them, so you can start a back-and-forth conversation.

These messaging platforms have a casual feel to them. Keep it casual.

Don't send a single, multi-paragraph message. Keep your messages short and snappy. You don't want for it to feel like a chore to reply to you.

Once you've got them replying to you, start making notes on your Personal Network Board so you can remember these facts later.

Maybe they do have some app idea or tool idea. Great. Ask them questions about it. See if you can build it for them.

Start by sketching out a simple mockup of the user interface. Use graphing paper if you want to look extra sophisticated. Snap a photo of it and send it to them. "Something like this?"

This will establish that you're serious about helping them. And I'd be willing to bet for most people, this would be a new experience.

"You're helping me? You're creating this app for me?" It will be flattering, and they will be likely to remember it. Even if the app itself doesn't go anywhere.

From there, you can just go with the flow of conversation. Maybe it fizzles out. No worries. Let it. You can find a reason to pick the conversation back up a few weeks later.

The great thing about these social media direct messages is the entire message log is there. The next time you message them, they can just scroll up and see "oh – this is that person who offered to build that app for me." There are no more "who are you again?" head tilts that you might get during in-person conversations.

Again, keep everything casual and upbeat. If it feels like the conversation is going slow, that's no problem. Because you're going to have dozens of other conversations going. Other irons in the fire. You're going to be a busy bee building your network.

How to Meet New People and Expand Your Personal Network

We've talked about how to reach out to people you already know. Those connections are still there, even if they've atrophied a bit over the years.

But how do you make brand new connections?

This is no easy task. But I have some tips that will make this process a bit less daunting.

First of all, meeting people for the first time in person is so much more powerful than meeting them online.

When you meet someone in person, your memory has so much more information to latch onto:

• How the person looks, their posture, and how they move through the space
• The sound of their voice and the way they speak
• The lights, sounds, aromas, temperature, and the general feel of the venue
• And so many other little details that get baked into your memory

Spending 10 minutes talking with someone in person can build a deeper connection than dozens of messages back and forth, across weeks of correspondence.

This is why I strongly recommend: get out there and meet people at local events.

How to Meet People at Local Events Around Town

Which events? If you live in a densely-populated city, you may have a ton of options at your disposal. You may be able to go to tech events several nights each week, with minimal commuting.

If you live in a small town, you may have to stick with meeting people at local gatherings. Book fairs, ice cream socials, sporting events.

If you go to church, mosque, or temple, get to know people there, too.

And yes, I realize this may sound ridiculous. "That person standing in the bleachers next to me at the soccer game? They're somehow going to help me get a developer job?"

Maybe. Maybe not. But don't write people off.

That person may run a small business.

They may have gone to school with a friend who's a VP of Engineering at a Fortune 500 company.

And maybe – just maybe – they're a software engineer, too. After all, there are millions of us software engineers out there. And we don't all live in Silicon Valley. 😉

When you do meet a new person, you don't want to immediately pull out your phone and say "Can I add you to my LinkedIn professional network?"

Instead, you want to play it cool. Introduce yourself.

Remember their name. Names are integral to building a relationship. If you are bad with names, practice remembering them. You can practice by just trying to remember the name of every character – no matter how minor they are – when you're watching TV shows or movies.

If you forget someone's name, don't guess. Just say "what's your name again" and be sure to remember it the second time.

Shake their hand or fist bump. Talk with them about whatever feels natural. If the conversation peters out, no worries. Let it.

You build relationships over time. It's not about total time spent with someone – it's about the number of times you meet that person over a longer span of time.

There's a good chance you will see the person again in the future. Maybe at that same exact location a few weeks later. And that is when you make your move:

"Hi [name] how's the [thing you talked about the previous time] going?"

Pick the conversation up where it left off. If they seem like someone who would be a helpful addition to your Personal Network Board, ask them "hey what are you doing next [day of week]? Do you want to come with me to [other upcoming local event]?"

Always have your upcoming week of events in mind, so you can invite people to join you.

This is a great way to get people to hang out with you in a safe, public space. And you're providing something of value – giving them awareness of an upcoming event.

If they seem interested, you can say "Awesome. What's the best way for me to message with you, and get you the event details?"

Boom – you now have their email or social media or phone number, and your relationship can unfold from there.

This may sound like a slow burn approach. Why be so cautious?

Again, people are busy. Smart people are defensive of their time, and of their personal information.

There are too many vampires out there who want to take advantage of people – trying to sell them something, scam them, get them into their multi-level marketing scheme, or in some other way proselytize them.

The best way to help other people get past this reflexive defensiveness is to already be on their radar from previous encounters as a reasonable person.

How to Leverage Your Network

We'll talk more about how to leverage your network in Chapter 4. For now, look at your network purely as an investment of time and energy.

I like to think of my network as an orchard. I am planting relationships. Tending to them, and making sure they're healthy.

Who knows when those relationships will grow into trees and bear fruit. The goal is to keep planting trees, and at some point in the future, those trees will help sustain you.

Keep sending out positive energy. Keep offering to help people using your skills, and even your own network. (It is rarely a bad move to make a polite introduction between two people you know.)

Be a kind, thoughtful, helpful person.

Don't ever feel impatient with how slow a job search may be going.

Don't ever let yourself feel slighted or snubbed.

Don't ever let yourself feel jealous of someone else's success.

What goes around comes around. You will one day reap what you sow. And if you're sowing positive energy, you're setting yourself up for one bountiful harvest.

Chapter 3: How to Build Your Reputation

"The way to gain a good reputation is to endeavor to be what you desire to appear." – Socrates

Now that you've started building your skills and your network, you're ready to start building your reputation.

You may be starting from scratch – a total newcomer to tech. Or you may already have some credibility you can bring with you from your other job.

In this chapter, I'll share practical tips for how you can build a sterling reputation among your peers. This will be the key to getting freelance clients, a first job, and advancing in your career.

But first, here's how I built my reputation.

Story Time: How Did a Teacher in His 30s Build a Reputation as a Developer?

Last time on Story Time: Quincy started building his network of developers, entrepreneurs, and hiring managers in tech. He was frequenting hackerspaces and tech events around the city. But he had yet to climb into the arena and test his might...

I was already several months into my coding journey when I finally worked up the courage to go to my first hackathon.

One day I encountered a particularly nasty bug, and I wasn't sure how to fix it. So I did what a lot of people would do in that situation: I procrastinated by browsing the web. And that's when I saw it. Startup Weekend EDU.

Startup Weekend is a 54-hour competition that involves building an app, then pitching it to a panel of judges. These events reward your knowledge of coding, design, and entrepreneurship as well.

This particular event – held in the heart of Silicon Valley – had a panel of educators and education entrepreneurs as its judges. With my background in adult education, this seemed like an ideal first hackathon for me.

I told Steve about the event. And then I said the magic words: "I'll do the driving." Which was good, because Steve didn't have a driver's license.

With Steve onboard, we rounded out our team with a couple of devs from the Santa Barbara Hackerspace.

I spent weeks preparing for the event by researching the judges and the companies they worked for. I researched the sponsors. And of course, I practiced coding like a Shaolin monk.

Finally, after a month of preparation, it was the big weekend. We piled into my 2003 Toyota Corolla with the peeling clear coat, put on some high energy music, and started our 5-hour drive.

On the way up, we discussed what we should build. It would be education-focused, of course. Preferably catering to high school students, since those were the grade levels the judge's companies focused on.

But what should the app do? How was it going to make people's lives easier?

I thought back to my own time in high school. I didn't have much to go on, since I'd dropped out after just one year. (I did manage to study for and pass the GED – Good Enough Degree as we called it – while working at Taco Bell, before eventually going to college. But that's another story.)

But one pain point I did remember from high school, which still rang out after all these years: English papers.

Now I loved writing. But I didn't love writing in MLA format, with its rigid citation rules. I used to dread preparing a Work Cited page. My teacher would always dock me points for not formatting my citations correctly.

After listening to a lot of OK ideas from the other passengers in the car, I piped up. I said: "I have an idea. We should code an app that creates citations for you."

And someone laughed and said: "Out of sight."

And Steve said, "Hey that's a good name. We could call it Out of Cite with a 'C'."

We all laughed and felt clever. Then we started discussing the implementation details.

When we arrived at the venue, there were about 100 other devs there. It was an open-plan office space, with low-rise cubicles flanked by whiteboards.

I heard whispers about one of those developers. "Hey, it's that guy who won the event last year," I heard people say. They gestured in the direction of a cocky-looking dev surrounded by fans. "Maybe he'll let me be on his team."

The event started with pitches. Anyone could go up to the front of the room, grab the mic, and deliver a 60 second pitch for the app they wanted to build.

I was so nervous it felt like an alien was about to burst out of my chest. So naturally, I was first in line. Rip the band-aid off, right?

I was sweating and gesticulating wildly as I raced through my pitch. I said something like this: "Citations suck. I mean, they don't suck. They're necessary. And you need to add them to your papers. But preparing citations sucks. Let's build an app that will fill out your Work Cited page for you. Who's with me?"

The room was quiet. Then people realized I was finished talking, and they gave me an obligatory round of applause. The MC took the mic out of my hand and gave it to the next person, and I pranced back to my seat.

After pitches, it was time to form teams. Our Santa Barbara contingent looked at each other and said "I guess we're a team."

We figured out the wifi password and grabbed the choicest of workspaces: a corner office that had a door you could actually close.

I started scrawling UI mockups on whiteboard. I said, "We want something that's always a click away. Right in your browser's menu bar."

"Like a browser plugin," Steve said.

"Yeah. Let's build a browser plugin."

I showed them examples of the three formats that essays might require: MLA, APA, and Chicago.

"Could we generate all three of these at once, so they can just copy-paste them?" I asked.

"We can do better than that," Steve said. "We can have a button for each of them that puts the citation directly into their clipboard."

We worked fast, creating a simple MVP (Minimum Viable Product) by the end of Friday night. All it did was grab the current website's metadata and structure it as a citation. But it worked.

Since it was my first hackathon, I didn't want the stress of staying in a hostel. So I'd splurged to get a hotel room. We had two twin beds, so each night we'd rotate which of us had to sleep on the floor.

Saturday morning, our ambitions grew. I walked to the whiteboard and said to the team: "Citing websites is great and all. But a lot of the things students cite are in books or academic papers. We need to be able to generate citations for those, too."

We found an API that we could use to get citation information based on ISBN (a serial number used for books). And we hacked together a script that could search for academic papers based on their DOI (a serial number used for academic papers), then scrape data from the result page.

By Saturday night, the code for our browser plugin was really coming together. So I sat down and started preparing the presentation slides. I left a lot of the final coding to my teammates while I rehearsed my pitch over and over again for hours.

Even though it was my turn to sleep in a bed, I could barely get any shut-eye due to the jitters. Here I was, right in the heart of the tech ecosystem. Silicon Valley.

As a teacher, I would routinely give talks in front of my peers – sometimes dozens of them. But this was different.

In a few hours, I'd be presenting to a room full of ambitious developers. And judges. People with Ph.D.s, some of whom had founded their own tech companies. They were going to be evaluating our work. I was terrified I'd somehow blow it.

Unable to sleep, I opened my email. The Startup Weekend staff had sent out an email, which included a PDF of a book. It was an unofficial mash-up of the tech startup classics 4 Steps to the Epiphany and The Lean Startup.

Now, I had already read these books, because they were required reading for anyone who wanted to build software products in the early 2010s. But I had also read dozens of other startup books. And a lot of their insights sort of ran together into a slurry of advice.

It was 4 a.m., and I couldn't sleep. So I just started reading. One thing these books really hit on is building something that people will pay for. The ultimate form of customer validation.

That's when I realized: you know what would really push my presentation over the finish line? Proof of product-market fit. Proof that the app we were building solved a real problem people had. So much so that they'd open up their wallets.

This gave me an idea. I should take our app on the road and sell it to people.

But it was Sunday morning. Where was I going to find potential customers? Well, our hotel just happened to be located near the main campus of Stanford University.

I drove my team to the event venue, waved goodbye and said: "I'll come back when I have cold, hard cash from customers."

My teammates chuckled. I'm not sure if they thought I was serious. They said, "Just don't be late for the pitch."

But I was serious. I had a prototype of the app running on my laptop. I punched Stanford into my GPS and embarked on my mission.

Now, I studied at a really inexpensive state university in Oklahoma. So I felt really out of my depth when I rolled up to one of the premier universities in the world.

Stanford costs $50,000 per year to attend. And I pulled into their parking lot driving a car worth 1/10th of that.

The campus was a ghost town this time of the week. But a palatial ghost town, nonetheless. Bronze statues. Iconic arches everywhere.

I asked myself: where are the most high-achieving, hard-core students this time of day? The ones who don't have time to waste on manually creating their Work Cited pages?

I walked into the main library, right past the security desk and a sign that said "no soliciting."

I strode around the stacks, finding a small handful of people studying. This one kid was studiously taking notes as he read through a thick textbook. Bingo.

I slid into the seat next to him. "Psst. Hey. Do you like citations?"

"What?"

"Citations. You know, like, work cited pages."

"Um..."

"You know, the last page of your paper, where you have to list all the..."

"I know what a work cited page is."

"OK. Well check this out." I pulled my jacket to the side like a drug dealer, and whipped out my $200 netbook. He humored me for a moment while I delivered my awkward sales pitch.

I said: "Here. I've got this browser plugin. I go to any website, click the button, and voilà. It will create a citation for me."

The kid raised his eyebrows. "Can it do MLA?"

I bit back my excitement and said, "MLA, APA, and even Chicago. Watch." I clicked the button and three citations appeared – each with its own copy-to-clipboard button.

The kid nodded, seeming somewhat impressed. So I attempted to close the sale.

"What if I told you that I was about to launch this app with a yearly subscription. But if you sign up now, I'll get unlimited access not for a year, but for a lifetime."

The kid thought for a moment.

I had heard that silence was the salesperson's best friend. So I sat there for an uncomfortably long time in total silence, staring him down.

Finally he said: "Cool I'm in."

"Awesome. That'll be twenty bucks."

The kid recoiled. "What? That's expensive."

This was of course the era of venture capital-subsidized startups, where Uber and Lyft were losing money on every ride in a race for market share. So the kid's reaction was not totally surprising.

But I thought fast. "Well, how much cash do you have on you?"

He fumbled with his wallet, then said, "five bucks."

I looked at the crumpled bill and shrugged. "Sold."

He smiled, and I sent him an email with instructions for how to install it. Then I said, "One more thing. Let's take a picture together."

I put my phone on selfie mode. He started to smile, and I said, "Here. Hold up the five dollar bill."

I spent another hour pitching people in the library, and managed to get another paying customer as well. Then I raced back to the event venue to finalize our prototype with the team.

That afternoon, I gave what I still think is the best presentation of my life. We live-demoed the working app – which worked perfectly.

We ended the presentation with the photos I'd taken, posing with Stanford students who were now our paying customers. When I held up the cash we earned, the audience burst into applause.

Overall, it was one of the most exhilarating experiences of my life. We came in second place, and won some API credit from one of the companies who sponsored the event.

At the after party, I chipmunked some pizza, so I'd have more time to network with everyone I could. I connected on LinkedIn. I followed on Twitter. I snapped selfies together with people and used the heck out of the event's hashtag.

This was a watershed moment in my coding journey. I had proven to the people in that room that I could help design, code, and even sell an app. And more importantly, I'd proven it to myself.

Riding the Hackathon Circuit

From that moment on, I was hooked on hackathons. That year, I participated in dozens of them. I became a road warrior, railing up and down the coast, attending every competition I could.

It would be much harder from here on out. I didn't have a team anymore. I was on my own.

I'd arrive, meet as many people as I could, then go up and pitch an idea I thought might win over the judges.

Sometimes people joined my team. Sometimes I joined other people's teams.

I didn't merely want to design apps – I wanted to code them, too. And my reach often exceeded my grasp.

There were many hackathons where I would still be trying to fix bugs down to the final minutes before going on stage. Sometimes my apps would crash during live demos.

One hackathon in Las Vegas, I managed to screw up the codebase so badly that we just had to use a slideshow. I sat in the audience with my head in my hands, watching helplessly as my team member demonstrated how our app would hypothetically work – if I could have gotten it to work. We didn't fare well with the judges.

But I kept grinding. Kept arriving in new towns, checking into the hostel, and hitting the venue, and eating as much free pizza as I could.

My teams had come in second or third so many times I could barely keep count. But we'd never managed to outright win a hackathon.

Breaking Through

That was until an event in San Diego. I'll never forget the feeling of building something that won over the audience and judges to the extent that our victory felt like a foregone conclusion.

After they announced us as the winner, I remember sneaking out the back door to a parking lot and calling my grandparents. I told them that I'd finally done it. I'd helped build an app and craft a pitch that had won a hackathon.

I don't know how much my grandparents understood about software development, or about hackathons. But they said they were proud of me.

With them gone now, I often think back to this conversation. I cherish their encouragement. Their faith in a 30-something teacher grandson could try like crazy and become a developer.

I kept going to hackathons after that. I kept forming new teams and learning new tools along the way. You never forget the first time you get an API to work. Or when you finally grok how some Git command works. And you never forget the people hustling alongside you, trying to get the app to hold together through the demo.

The TechCrunch Disrupt hackathon. The DeveloperWeek hackathon. The ProgrammableWeb hackathon. The $1 Million Prize Salesforce Hackathon. So many big hackathons and so much learning. This was the crucible where my developer chops were forged.

Not only did I manage to build my skills and my network along the way – I now had a reputation as someone who could actually win a hackathon.

I could ship.

This made me a known quantity.