Acing AI — AI education, tutorials, research and datasets for data scientists
Constrained Decoding: How to Get Guaranteed JSON from an LLM (and the Reasoning Tax)
How constrained decoding guarantees valid JSON from an LLM: runnable vLLM and structured-output examples, the latency cost, and the reasoning tax that JSON-mode hides.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
DeepSeek DSpark: What Semi-Autoregressive Speculative Decoding Actually Changes
DeepSeek DSpark adds semi-autoregressive drafting and load-aware verification to speculative decoding. What is new versus EAGLE-3, and why the benchmarks are not yet independently verified.
Running LLMs Locally in 2026: A Step-by-Step Setup Guide for Ollama, llama.cpp, and vLLM
A hands-on guide to running LLMs locally in 2026: install Ollama, verify the API, then build llama.cpp and serve with vLLM, with the VRAM and bandwidth math behind each step.
ArticleEffective Context Length: Why 1M-Token Windows Fall Short, and When RAG Still Wins
ArticleSpeculative Decoding in vLLM: A Practical Guide to Faster LLM Inference
ArticleQuantization Deep Dive: FP8 Training, FP4, and the Outlier Problem
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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.
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Guided sequences through our best content — structured to build understanding from the ground up.
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.
Understanding Modern LLM Architectures
A guided journey from transformer fundamentals to the cutting edge of LLM engineering. You will build an intuition for how modern language models are designed, scaled, optimized, and deployed, by covering attention mechanisms, Mixture of Experts, architectural innovations like DeepSeek's MLA, reasoning capabilities, inference optimization, and the open-source ecosystem reshaping AI.