Acing AI — AI education, tutorials, research and datasets for data scientists
Agent Skills Are a Procedure, Not Knowledge
A controlled study of agent skills finds they work by procedural anchoring, not knowledge injection. What that means for your SKILL.md and your skill library.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
LLM Capacity Planning Has a Half-Life: Why Your Serving Numbers Expire
LLM capacity planning has a half-life: the VRAM bill and throughput baseline you wrote at model release decay as vLLM and SGLang change defaults beneath them.
Evolution Strategies Fine-Tuning: What Dropping Backprop Buys, and Where the Bill Moves
Evolution strategies fine-tuning updates every LLM weight with no backprop: what the 2025-2026 papers measured, what they skipped, and when ES beats GRPO.
ArticleVision Encoder Disaggregation: When Your Encoder Should Be Its Own Service
TutorialHow Much Does an Image Cost? Measuring Image Token Cost on a 27B Multimodal Model
ArticleNobody Broke the Sandbox: Agent Evaluation Scope Has to Live Outside the Model
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Step-by-step guides from neural network basics to advanced LLM fine-tuning.
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Peer-reviewed insights and white papers defining the frontier of artificial intelligence.
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High-fidelity training sets for natural language processing and computer vision.
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Guided sequences through our best content — structured to build understanding from the ground up.
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.
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.