Acing AI — AI education, tutorials, research and datasets for data scientists
Sparse Attention in 2026: Why It Finally Had to Be Native
Sparse attention in 2026: NSA, DeepSeek DSA, MoBA and MiniMax MSA. Why native trainable sparsity ships where post-hoc masking of a dense model stalls.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
State Space Models in 2026: The Recall Gap, and What Finally Closed It
State space models in 2026: Mamba, RWKV, and Jamba versus Transformers. Where linear-time SSMs win, where recall breaks, and why hybrids at 3:1 won production.
Latent Reasoning: The Open Problem of Thinking Without Words
Latent reasoning lets LLMs think in hidden states instead of tokens. A research review of Coconut, Abstract CoT, and SWITCH, and why it is not production-ready yet.
ArticleInkling Is Not Trying to Win: How to Measure an Open-Weights Fine-Tuning Base
ArticleThe Model That Stole the Answer Key: Eval Harness Security After a Real Sandbox Escape
ArticleFrom RLHF to RLVR: How RL Post-Training Actually Evolved
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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.