🧭 Start Here
Where should you begin?
This site has 120+ long-form articles across more than a dozen series. Pick the track that fits you and read in order — each step builds on the last, and every series article ends with a link to the next one.
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I'm new to AIUnderstand how modern models work and who's building them. No math required.
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I want to do the researchBuild the math, the deep-learning foundations, and a working language model of your own.
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I'm building or investingWhere frontier AI is headed, where the gaps are, and how to build a company in them.
🌱 Understand how it works
For curious readers, students and anyone switching in. About 6 hours of reading.
- 1Build a GPT from scratch, explained plainlyThe interactive course in Layman mode: what a language model actually is, how it learns, and how it's trained — no jargon.
- 2How a transformer works, end to endFollow one sentence through a GPT-style model step by step, with interactive demos and code you can run — including how training works.
- 3Reasoning models & test-time computeWhy the newest models "think" before answering, and why that became the second scaling law.
- 4Multimodality & the great consolidationHow text, image, audio and video models are merging into one.
- 5AI agents & the enterprise agent economyWhat changes when models stop answering questions and start doing tasks.
- 6AI safety, alignment & interpretabilityThe open problems that decide whether any of this goes well.
- 7The GPT lineage, GPT-1 to GPT-6One model family's full history — the clearest way to see how the field actually progressed. Other labs' lineages follow via the Next links.
- 8The research frontier mapWhat every major lab is betting on right now, side by side.
🔬 Build the skills to do the research
For students and engineers aiming at research or research-engineering roles. Weeks, not hours — work the exercises.
- 1Self-assessment: find your gaps firstCheck what you already know so you can skip it — then fill only the gaps.
- 2The mathematical foundationsThe linear algebra, probability and optimization you'll actually use.
- 3Core deep learning conceptsThen continue through the Foundations curriculum — RL, interpretability, alignment, world models, systems — using the Next links.
- 4Build a language model from scratch (Stanford CS336)A lecture-by-lecture companion with runnable code: tokenizer, resource accounting, architecture, and on through training.
- 5How pretraining works, end to endFrom raw web text to a foundation model: data filtering, deduplication, the training recipe and the systems that scale it — with a pipeline you can run.
- 6How fine-tuning works: full fine-tuning vs LoRATurning a base model into an assistant or specialist, with a runnable experiment comparing the two main methods.
- 7How post-training works: SFT, RLHF, DPO and RLAIFHow a base model becomes an aligned assistant, with an experiment comparing SFT, DPO and RLHF on the same model.
- 8How inference works: serving a model at scalePrefill and decode, the KV cache, batching, quantization and speculative decoding, with a mini engine you can run.
- 9Scaling laws, GPUs and distributed trainingWhy bigger models work, what GPUs are fast at, and how one model is split across thousands of them, with a mini scaling study you can run.
- 10How to read a paper like a researcherA repeatable method for getting through the literature without drowning in it.
- 11Day one at a frontier labThe engineering practicum: production training code, distributed debugging, kernels, on-call.
- 12Recursive self-improvement: a primerOne live research frontier, end to end — then the CS329A self-improving agents handbook.
- 13The researcher's field guide: getting hiredPapers, labs, people and the hiring process — plus the study stack and PhD programs guides.
🚀 Find the opportunity and build in it
For founders, operators and investors. About 6 hours, then the 12-part startup series.
- 1The six real levers of frontier AIThe framework the rest of this track uses: what actually moves capability forward.
- 2The AGI capability stack, mappedWhere today's frontier models stand on each layer, and what closing each gap takes.
- 3The levers nobody's pulled yetWhere a new lab could realistically win against incumbents.
- 4Compute frontier: why clusters keep growingThe cost structure underneath everything: training runs, mega-clusters, hardware.
- 5Frontier model pricingWhat the models cost to use, and what that means for product margins.
- 6AGI startups to watchWho's already building in the open lanes.
- 7The three startup lanesFrontier scale-first, the applied & agentic layer, and the narrow research bet — strategy and team for each.
- 8Founding an AGI startup: the 12-part seriesFrom finding the idea through legal, team, seed, product, go-to-market and Series A.