Pick a goal. Follow the path.
Each path threads the handbooks, system designs, algorithm games, runnable challenges and tools into one ordered course — pointed at a single goal, with your progress tracked as you go. 24 paths, all free, no sign-up.
Prefer to browse by subject? Explore topics →Forward Deployed Engineer Academy
Start with a diagnostic, follow a college-to-job curriculum, ship a simulated customer engagement and finish with a complete mock interview loop.
- 3
- entry routes
- 7
- phases
- 1
- capstone
AI Engineering
Become an AI Engineer
For developers building real features on top of LLMs.
From prompting to retrieval to agents to evaluation and serving — the full arc of shipping LLM-powered features, threaded through handbooks, system designs, runnable challenges and tools.
Adapt to the AI Era
For engineers who want to get more valuable as AI makes code cheap.
The role-agnostic adaptation arc: understand the shift, learn to direct and review AI, build the new stack (spec-driven development, evals, reviewing AI code), then present and price the judgment that becomes your moat. A 90-day plan you can actually track.
Ace the AI Engineer Interview
For AI/ML engineer interview loops.
The AI interview handbooks paired with the runnable challenges behind the questions — so you can explain softmax, embeddings and evals and then implement them on the spot.
Ship an AI Product
For indie hackers and founders shipping AI products solo.
The build → ship → grow arc for a one-person AI startup: the on-site lessons for the engineering, woven together with open-source tools — Inkwell, VolfPack, PromptPlay — for prototyping, finding your first users, and marketing a launch without a team.
Master LLM Evaluation
For engineers who need to prove their AI actually works.
Shipping an LLM feature is easy; knowing whether it is good is the hard part. Build the vocabulary of evals, design a judge rubric, implement a metric by hand, then evaluate agents and a real production system.
ML Engineering Foundations
For developers moving from app code into machine learning.
The core math and primitives behind modern ML, made runnable: feel how models learn, implement the functions every model relies on, then assemble them into a recommender and a retrieval pipeline.
Master Context Engineering
For engineers whose agents forget, ramble, or blow the window.
The discipline that replaced prompt-tweaking, learned end to end: tokens and budgets, the four operations (write, select, compress, isolate), compaction and memory, cache-aware prompts — threaded through the handbook, labs, tools, papers and system designs.
The Papers Bootcamp
For engineers who want to read the canonical deep-learning papers in the right order.
The papers that built modern AI, sequenced so each one earns the next — from word2vec and the Transformer through scaling laws, RLHF, chain-of-thought, RAG, LoRA and MoE, out to vision and diffusion. Every entry is a plain-English breakdown, not the raw PDF.
AI for Product Managers
For PMs shipping AI features who want to reason about them without writing code.
A concept-first path for product managers: the AI stack, the build decisions (RAG vs fine-tuning, big vs small model), the cost and quality levers, and the risk surface — paired with interactive calculators so you can pressure-test a plan in a meeting, not just nod along.
Land Your First Dev Job in the AI Era
For new and bootcamp grads facing "AI is killing junior roles — what do I even learn?"
The honest, ordered path for a new grad in 2026: understand what actually changed for juniors, learn the fundamentals AND the new core skill (directing and reviewing AI), build a portfolio of judgment, and walk into the 2026 interview ready to work with AI in the room.
Switch Careers into Tech and AI
For career switchers coming from a non-tech field into tech/AI.
The realistic AI-era path for a career switcher: use AI’s build leverage to demonstrate real ability, anchor your switch to the domain knowledge from your old career, and aim above the fully-automatable entry rung — threaded through the on-site handbooks, tools, and challenges.
Systems & Backend
Pass the System Design Interview
For engineers prepping the system-design loop.
Build the vocabulary and the trade-off intuition first, practice the back-of-envelope numbers, then rehearse a dozen canonical designs from a URL shortener to a payment system.
Level Up as a Backend Engineer
For backend engineers going deeper on distributed systems.
The distributed-systems fundamentals, then the systems that put them to work — a key-value store, a cache, a message queue, an ID generator — with tools to build the intuition.
Reliability & Scale (SRE)
For engineers who keep systems up as traffic grows.
The reliability engineer’s toolkit: turn nines into real downtime, size capacity before it bites, then study the patterns — quorums, rate limiting, caching, queues — that absorb load and keep services alive under pressure.
Become a Forward Deployed Engineer
For engineers who want to own the whole arc — problem to production, at the customer.
The complete FDE track, in order: understand the role, learn to discover and scope, prototype fast, integrate with real systems, deploy in the customer’s world, demo, harden, and hand off — threaded through deep handbooks and hands-on Python/TypeScript challenges you actually solve.
System Design in 30 Days
For engineers who want to design real systems, not memorize diagrams.
A structured month through the canonical system designs — from a URL shortener to Uber — each built on the fundamentals it needs: capacity math, caching, partitioning, consistency. Design the system, then implement the primitive underneath it.
Distributed Systems Deep Dive
For engineers who want consensus, replication and consistency in their bones.
The hard core of distributed systems, threaded from the CAP theorem through Raft, quorums, vector clocks and CRDTs to real designs. Each protocol is paired with a simulator to drive it and a challenge to implement one step of it yourself.
From Sysadmin to DevOps / SRE
For system administrators moving into DevOps, SRE, or platform engineering.
A persona-framed re-sequence that starts from the Linux and operations you already know, adds containers, Git, and infrastructure-as-code thinking, and threads through the DevOps, SRE, and platform-engineering roadmaps — the honest route from keeping servers alive to engineering the platform.
From DBA to Data Platform Engineer
For database administrators moving into data engineering / data platform work.
A persona-framed path that starts from the database internals you already know, sharpens the SQL you own, and threads through the data-engineering and platform roadmaps plus the AI-era data shift — the honest route from administering databases to engineering the data platform.
Foundations
Crack the Coding Interview
For the algorithms half of the loop.
Follow the DSA roadmap, play the patterns as interactive games, then prove you can implement them by solving the matching challenges in your browser.
Advanced Algorithms & Data Structures
For when the interview basics aren’t enough.
The specialized machinery behind real systems — range-query trees, string automata, network flow, and probabilistic sketches — each played as an interactive visualization, in a coherent order.
Build an LLM From Scratch
For engineers who want to understand a language model down to the matrix multiply.
No black boxes. Start at raw bytes and end at an aligned assistant — tokenization, embeddings, attention, the full forward pass, the training loss, and RLHF — each idea paired with a runnable challenge or lab that makes you build the piece by hand.
The 4-Week Interview Sprint
For engineers with a loop on the calendar and four weeks to prepare.
A dense, ordered sprint: week one drills the coding patterns, week two the harder graph and DP problems, week three switches to system design, and week four handles the behavioral round, resume and offer. Every coding step is runnable.
From Manual Tester to SDET
For manual QA testers moving into software development engineer in test (SDET) / automation.
A persona-framed path that starts from the test design you already know, adds your first code and automation, and threads through the QA/SDET roadmap and the AI-era QA shift — the honest route from clicking through test cases to engineering the systems that test.