Acing AI — AI education, tutorials, research and datasets for data scientists
From RLHF to RLVR: How RL Post-Training Actually Evolved
RL post-training mapped: RLHF and PPO through Constitutional AI, DPO, and GRPO to RLVR and rubric rewards. Every jump changed the reward, not the optimizer.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
Synthetic Data for Post-Training: When It Helps and When It Collapses
Synthetic data for post-training: when distillation helps, when self-generated data triggers model collapse, and how to detect the narrowing before it ships.
Test-Time Compute: Where More Thinking Stops Paying
Test-time compute scaling explained: best-of-N, self-consistency, and verifier-guided search, where each saturates, and when more inference compute is wasted.
TutorialPreference Optimization After DPO: IPO, KTO, and SimPO Compared
ArticleSelf-Hosting a Frontier Open MoE: The Real GPU Bill for a 1.6-2.8T Open-Weight Model
ArticlePagedAttention Is Gone — What vLLM's Model Runner V2 Replaced It With
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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.
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.
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.