Acing AI — AI education, tutorials, research and datasets for data scientists
KV-Cache Engineering: The Memory Wall of LLM Serving
The KV cache is the real bottleneck in LLM serving: 70-90% of GPU memory at long context. PagedAttention, prefix caching, MLA, and quantization explained.
Latest Intelligence
Curated technical papers and hands-on implementation guides for the modern AI engineer.
World Models: Three Research Programs Wearing One Name
World models explained: latent dynamics models, generative simulators like Genie 3, and language world models inside LLMs, plus concrete tests for coherence.
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.
ArticleDeepSeek DSpark: What Semi-Autoregressive Speculative Decoding Actually Changes
TutorialRunning LLMs Locally in 2026: A Step-by-Step Setup Guide for Ollama, llama.cpp, and vLLM
ArticleEffective Context Length: Why 1M-Token Windows Fall Short, and When RAG Still Wins
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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.