Acing AI — AI education, tutorials, research and datasets for data scientists
Inkling Is Not Trying to Win: How to Measure an Open-Weights Fine-Tuning Base
Thinking Machines Inkling debuted at Artificial Analysis index 41, behind Kimi K3's 57. For an open-weights fine-tuning base, rank is the wrong axis. What to measure instead.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
The Model That Stole the Answer Key: Eval Harness Security After a Real Sandbox Escape
Eval harness security after the July 2026 OpenAI/Hugging Face sandbox escape: egress as the failed control, answer keys as secrets, and a threat model you can run.
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.
ArticleSynthetic Data for Post-Training: When It Helps and When It Collapses
ArticleTest-Time Compute: Where More Thinking Stops Paying
TutorialPreference Optimization After DPO: IPO, KTO, and SimPO Compared
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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.
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.