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Learning Paths.

Curated, ordered sequences of our best content, designed to take you from concept to competence on the topics that matter.

Advanced

7 pieces · ~2.2h

Post-Training Modern LLMs

Pretraining produces a model that predicts text. Post-training is what turns it into something you can ship. This path walks the levers in the order you would actually reach for them: supervised fine-tuning and adapters, preference optimization without a reward model, the reinforcement-learning map from RLHF to verifiable rewards, RL against a verifier that cannot be talked out of its answer, and finally the inference-time compute that picks up where training leaves off. Every step names the ceiling it runs into.

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Intermediate

4 pieces · ~1.2h

Evaluating LLMs Honestly

A leaderboard number is a hypothesis, not a result. This path builds the habit of asking what a benchmark measured before quoting what it reported, starting with contamination and judge bias, moving through a case where the advertised figure and the measured one diverge, then to agents where a single passing run tells you almost nothing, and ending where the eval harness itself turns into attack surface.

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Advanced

5 pieces · ~1.3h

Attention Beyond Quadratic

Every long-context system is a bet about which parts of attention you can afford to skip. This path starts with what a context window actually delivers under measurement, then walks the ladder of answers in order: sparse attention that keeps the mechanism and prunes the work, the hybrid stacks shipping that idea at frontier scale, linear attention that removes the quadratic term from most layers, and the state space models that go fully recurrent and run straight into the recall gap. Every step names what it gives up.

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Intermediate

5 pieces · ~1.4h

Building Production AI Agents

Go from an agent demo to a system that holds up in production. This path moves through the four decisions that determine whether an agent ships: the deployment reality gap, tool and data access with MCP, persistent memory, and evaluation that measures reliability rather than luck.

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Intermediate

5 pieces · ~1.4h

LLM Inference in Production

A practical route through the serving stack. Start with the map of where inference cost and latency actually come from, take the quantization lever apart, speed up decoding with speculative drafts and measure it yourself, confront what long context really delivers, and finish at the 2026 state of the art. Every step names its tradeoffs.

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Intermediate

6 pieces · ~2h

Understanding Modern LLM Architectures

A guided journey from transformer fundamentals to the cutting edge of LLM engineering. You will build an intuition for how modern language models are designed, scaled, optimized, and deployed, by covering attention mechanisms, Mixture of Experts, architectural innovations like DeepSeek's MLA, reasoning capabilities, inference optimization, and the open-source ecosystem reshaping AI.

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