A curated roadmap for CS students and new developers entering an industry where AI writes half the code. Not doom, not hype. What actually changed, what still matters, and how to be ready for your first job.
You are a computer science student in your final year, or a new developer in your first two years on the job, and you are trying to answer some version of:
- What should I actually be learning right now?
- If AI can write the code, what does an entry-level engineer even do?
- How do professional teams actually use AI tools day-to-day?
- How do I interview for jobs where the interviewer knows I can use ChatGPT?
- Is my degree still worth it?
This roadmap collects the best writing, videos, tools, and communities that answer those questions honestly. Every link is here because a working engineer would send it to a friend, not because it was on the first page of Google.
- Experienced senior engineers looking for advanced material. This is foundational.
- People looking for get-rich-quick "AI dev influencer" content. There is none of that here.
- Anyone hoping for a "AI will/won't take your job" polemic. This roadmap is about how the job actually works now.
| # | Stage | What you'll figure out | Deeper read |
|---|---|---|---|
| 1 | What actually changed | An honest look at the industry — hiring, tools, workflows — over the past three years | stages/1 |
| 2 | Foundations that still matter | The CS skills AI hasn't touched, and probably won't | stages/2 |
| 3 | Skills for the AI era | Prompting for code, evaluating LLM output, agentic workflows, using Cursor/Copilot/Claude Code | stages/3 |
| 4 | How real teams ship code now | Case studies from real engineering orgs on their AI-augmented workflows | stages/4 |
| 5 | Getting hired | Portfolios that impress, interviews that companies actually run, projects that stand out | stages/5 |
| 6 | Your first year | What junior work looks like when AI does the boilerplate, and how to grow past it | stages/6 |
| 7 | Staying relevant | Newsletters, podcasts, communities, and books to keep up with the field | stages/7 |
The main README below is the roadmap at a glance — curated links per stage. Each row also links to a deeper stage file in stages/ with reading order, concrete exercises, and a "you're done with this stage when…" checkpoint.
Plus a Career Q&A at the bottom for the specific questions people email about.
Before deciding what to learn, understand the ground you're standing on. These sources are honest, data-driven, and free of vendor hype.
- Stack Overflow Developer Survey — Annual survey of what tools and languages engineers actually use, salaries, and satisfaction. Read the last two years back-to-back to see the AI adoption curve.
- GitHub Octoverse — Yearly report on what's happening on GitHub — language trends, most-starred repos, AI-assisted development stats.
- The Pragmatic Engineer — State of the Startup Engineering Job Market — Gergely Orosz writes the most-cited analyses of tech hiring cycles.
- Levels.fyi Trends — Real compensation data across companies, roles, and years.
- Simon Willison's blog — The clearest, least-hyped writing on what LLMs actually can and can't do. Start with anything tagged
llms. - Ethan Mollick — One Useful Thing — A professor of management writing weekly about how AI actually gets used in real work. Pragmatic, evidence-based.
- Latent Space podcast — Interviews with the engineers actually building AI tools. Reveals how professional workflows are changing.
- The Pragmatic Engineer — AI Tooling for Software Engineers in 2026 — Detailed survey of how real engineering teams have integrated AI tools.
Contribution wanted: a well-sourced "before/after 2022" article specifically about entry-level hiring changes. Open a PR if you know one.
AI is very good at writing syntax. It is mediocre at systems thinking, at debugging problems it did not create, and at translating a vague business need into a working system. Those are the skills that a company still pays a person for. Invest here.
- CS50 (Harvard) — Still the best free introduction to computer science, covering C, Python, algorithms, SQL, and web basics. Watch the lectures even if you already have a degree.
- Missing Semester of Your CS Education (MIT) — The shell, git, editors, debugging, version control — the practical skills a CS degree usually skips. Free lectures.
- Teach Yourself Computer Science — A curated syllabus of the classic textbooks and courses if you want to fill in a specific gap (OS, networking, distributed systems, etc.).
- Structure and Interpretation of Computer Programs (free) — SICP. Still the book that most permanently changes how you think about code.
- Designing Data-Intensive Applications — Martin Kleppmann — The single most-recommended book for backend engineers. Systems, storage, distributed computing, all rigorously explained.
- System Design Interview — Alex Xu (Vol 1 & 2) — Concrete case studies of designing large systems from scratch. Both interview prep AND real skill-building.
- Julia Evans's zines and blog — Debugging, networking, systems — explained in a way that finally sticks. Read everything.
- Site Reliability Engineering (free book, Google) — How Google runs production systems. Long, but the mental models transfer to any team.
- The Pragmatic Programmer, 20th Anniversary Ed. — Hunt & Thomas — The habits of a working engineer. Timeless.
- On Writing Well — William Zinsser — Learning to write clearly makes you a better engineer. This is the book. Not tech-specific and that's a feature.
- Google's Technical Writing Courses (free) — Two short courses. Do them in an afternoon.
These are new skills. Most CS programs don't teach them yet. Every one of them is directly hire-able.
- Anthropic — Prompt Engineering Guide — The canonical guide to writing effective prompts. Written by the model makers.
- Simon Willison — Using LLMs for code — Practical, honest write-ups of what works and what doesn't when pairing with an LLM on real code.
- Cursor documentation — Cursor is currently the most-used AI-native code editor. Learn the shortcuts and the mental model.
- GitHub Copilot best practices — Official guide from GitHub on getting real leverage from Copilot.
- Claude Code documentation — Anthropic's agentic CLI for coding. Different mental model than Cursor — worth learning both.
Knowing when to trust the model is more valuable than knowing how to prompt it.
- Hamel Husain — Your AI Product Needs Evals — The most-shared essay on why LLM evaluation matters and how to actually do it.
- Chip Huyen — Building LLM Applications for Production — Long, thorough, still relevant.
- Eugene Yan — Patterns for Building LLM-based Systems — Practical patterns from production experience.
- LangSmith and Braintrust — Two of the popular evaluation platforms. Learn the concepts even if your team uses something else.
- Anthropic — Building effective agents — The clearest, most-cited framework for when to use agents (and when not to).
- LangGraph — Currently the most flexible agent-orchestration framework in Python.
- Building an Agent from Scratch — Simon Willison — Read from-scratch implementations before reaching for frameworks. Understand what's happening under the hood.
- The Rise of the AI Engineer — Latent Space — The essay that named the emerging job title. Worth reading if you're considering that direction.
Case studies from working engineering orgs. Read these when you want to know "how does this actually work at a real company?"
- Shopify Engineering — AI tooling posts — Shopify has been publicly aggressive about AI-augmented dev workflows. Their blog is one of the most concrete sources on what changed and what didn't.
- GitHub Blog — engineering — How the company that ships Copilot uses AI internally.
- Vercel Blog — v0 team writes candidly about building AI-native products.
- Anthropic's engineering posts — The engineering behind Claude — evals, tooling, safety.
- Sourcegraph Blog — Their whole product is code intelligence + AI; posts often reveal patterns.
- Team Topologies — Skelton & Pais — How healthy modern engineering teams are shaped. Not AI-specific but foundational for understanding where you fit.
- Accelerate — Forsgren, Humble, Kim — The four DORA metrics. How high-performing engineering teams measurably ship faster.
- The Pragmatic Engineer — inside real companies — Long-form pieces on how specific companies (Uber, Meta, Stripe, Amazon) actually organize engineering work.
- Google's Engineering Practices — Code Review — The standard reference. AI-augmented code review still needs a human who understands these principles.
- Steve Yegge — Cheating Is All You Need — Widely-shared essay on how AI changed his workflow at Sourcegraph.
The hiring process changed. Take-home coding tests are increasingly replaced with live pairing sessions where interviewers watch you work with AI tools. Portfolios matter more than ever because interviewers now assume everyone can generate a to-do app.
- Cracking the Coding Interview — Gayle McDowell — Still the standard reference. Skip this at your peril.
- NeetCode — Best free structured LeetCode practice for the patterns that come up 90% of the time.
- System Design Primer (GitHub) — 200k+ stars for a reason. Read it once end-to-end.
- Grokking Modern System Design Interview (Educative) — Paid but often included with student discounts.
- Interviewing.io blog — Anonymous mock-interview data on what actually happens in real interviews.
- How to build a project that gets you hired — Josh Comeau — Not AI-specific but the principles apply.
- Every developer's Github should have this project — Nikita Sharov — Search this on dev.to; the argument is that a from-scratch clone of a well-known tool teaches more than a novel project.
- A published npm/PyPI/Maven package — Even a small utility, shipped to a real package registry, signals professional-grade work. Retrylens is one example — build your own.
- A blog with technical writeups — Companies read them. Write about the hardest bug you've fixed.
- SimplifyJobs / New-Grad-Positions — Community-maintained list of new-grad SWE roles in the US.
- Jobright-ai / 2026-Software-Engineer-New-Grad — Similar, updated daily.
- speedyapply / 2027-AI-College-Jobs — AI/ML internship & new-grad job list, updated daily.
- YC Work at a Startup — Y Combinator startups hiring, often willing to interview inexperienced candidates.
- LinkedIn — Yes, still.
Junior engineering work looked one way in 2015 and a different way in 2026. If AI writes the boilerplate, what do juniors do? Reading these will keep you from being blindsided.
- Julia Evans — What I learned working at Stripe — Every post tagged
careerorlearning. Foundational. - Charity Majors on being an engineer — Especially the posts on the engineer/manager pendulum and staff-plus paths.
- Rachel by the Bay — Battle-worn writing about production, on-call, incidents. Read for pattern recognition.
- The Manager's Path — Camille Fournier — Read chapters 1–3 (Intern, Tech Lead) as a junior. The rest waits for later.
- Kellan Elliott-McCrea — Being Glue — The invisible work that makes teams function. Especially important when AI can do the visible work.
- Will Larson — An Elegant Puzzle — Engineering management wisdom that also explains what junior roles should look like in a healthy team.
- James Hawkins — Growing as a Software Engineer — Practical career advice from a working engineering leader.
- Simon Willison — LLMs and the future of programming — Yearly retrospectives that are honest about what's changed.
- Search dev.to and hackernews for recent essays. Add the best ones via PR.
Contribution wanted: 3-5 essays specifically on what junior/entry engineering work looks like at real companies in 2026. Open a PR.
You will never be "done learning." The trick is being efficient about what you consume.
- The Pragmatic Engineer — Weekly. The single best paid newsletter on the software industry.
- Bytes — Weekly frontend/JS ecosystem, funny, short.
- TLDR Newsletter — Free daily 5-minute tech news skim.
- Simon Willison's Weblog — Subscribe via RSS. Posts multiple times a day; skim, don't binge.
- Latent Space — AI engineering, deep interviews.
- Software Engineering Radio — Long-form interviews on engineering topics, weekly.
- The Changelog — Open source and software culture.
- Latent Space — AI engineering.
- Practical AI — Applied AI, less hype than most.
- Hacker News — Read, don't comment for the first year. The best essays surface.
- Lobsters — Smaller, higher signal-to-noise than HN. Invite-only for posting.
- r/ExperiencedDevs on Reddit — Career-focused, actual senior engineers.
- r/cscareerquestions on Reddit — More entry-level focus. Take advice with salt.
- Rands Leadership Slack — Free Slack community of ~15k tech leaders. Excellent for questions about "how does this actually work at real companies."
- Local meetups — Nothing beats meeting engineers in person. Search your city for language-specific groups.
- The Pragmatic Programmer — Hunt & Thomas
- Designing Data-Intensive Applications — Kleppmann
- The Manager's Path — Fournier (first 3 chapters)
- A Philosophy of Software Design — John Ousterhout
- Accelerate — Forsgren, Humble, Kim
- The Staff Engineer's Path — Reilly (aspirational read)
"Should I still learn to code manually if AI can do it?" Yes. You have to be able to read code fluently to review what an AI generates, and you have to have built things by hand to understand what's actually happening when you debug. AI dependency without foundations is a career-limiting move.
"Should I focus on AI/ML or on general software engineering?" For your first job — general software engineering. The demand is 100× higher, the pay is comparable, and you can specialize into ML later once you know how production systems actually work. Very few teams hire junior ML engineers; almost every team hires junior SWEs.
"Which programming language should I learn?" For a first job, the honest answer is: it doesn't matter that much. Pick one modern language (Python, TypeScript, Go, Rust, Kotlin, Swift, Java) and get good at it. Fluency in one beats surface knowledge of five.
"Is a CS degree still worth it?" Yes, and probably more than ever. The market is tightening at the junior level, and a degree is the most common filter recruiters use. It's not required (many people succeed without one), but if you have the option, take it.
"Should I go to grad school?" Only if you specifically want to do research (PhD) or need it as a visa/credential path (some Master's programs). Not required for industry SWE work.
"How important is my GitHub profile?" Somewhat, especially for your first job. A handful of well-written repos with good READMEs and a clear commit history beats 50 empty ones. Contributing a real PR to a well-known project is worth more than any solo project.
This roadmap is a community effort. If you know a resource that belongs here — or a resource that's here and shouldn't be — open a PR or an issue.
Contribution rules are in CONTRIBUTING.md. Short version: no marketing links, no low-signal content, no "AI tool of the week" spam. Every resource must be one you would actually recommend to a friend.
The list itself is CC0 — copy it, fork it, use it any way you like.
- roadmap.sh — General developer roadmaps by role. Complementary to this one.
- Teach Yourself CS — Deep-dive on CS fundamentals.
- Awesome Lists — The canonical index of curated resource lists.
Started by bibekmhj. Kept alive by everyone who has opened a PR.