Acing AI — AI education, tutorials, research and datasets for data scientists
Verified Code Generation: When the Model Has to Prove It
Verified code generation with LLMs, Dafny, and Lean. How formal verification turns passing tests into proof, and why writing the spec is the hard part.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
Quantization of Ethics: Mathematical Constraints for AI Fairness
The mathematical frameworks behind AI fairness: demographic parity, equalized odds, impossibility theorems, and practical bias mitigation for ML teams.
Diffusion Models Beyond Images: Audio, Video, and 3D in 2026
Diffusion models beyond images in 2026: audio, video, and 3D. How diffusion transformers work, the sampling-step latency tax, and where autoregression wins.
ArticleMultimodal LLMs in Production: What Native Vision Actually Costs
ArticleSparse Attention in 2026: Why It Finally Had to Be Native
ArticleState Space Models in 2026: The Recall Gap, and What Finally Closed It
Browse by Type
Tutorials
Step-by-step guides from neural network basics to advanced LLM fine-tuning.
Research Papers
Peer-reviewed insights and white papers defining the frontier of artificial intelligence.
Datasets
High-fidelity training sets for natural language processing and computer vision.
Start Learning
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.