A hub for the 5-article series that organizes math symbols in AI and LLM articles for reading, not solving. Covers equations, vectors and matrices, probability and statistics, derivatives, and gradient descent with backprop, plus a reading-order guide for different backgrounds.
Gradient descent, SGD and Adam, backpropagation, vanishing/exploding gradients with residual connections, and learning rate schedules — organized around what each piece is doing at a high level. The goal is reading training logs and model card numbers, not computing anything.
A minimum set of calculus for reading AI and LLM articles — d/dx, e, the chain rule, partial derivatives, and gradients. Focus on what the symbols are doing, not on solving the formulas.
A minimum set of probability and statistics for reading AI and LLM articles — conditional probability, cross-entropy, perplexity, and temperature are the main ones; rigorous Bayes and MLE derivations stay out of scope.
A minimum set of vectors and matrices for reading AI and LLM articles — the dot product and matrix product are the main two; determinants, inverses, and eigenvalues stay out of scope.
A minimum set of math for reading AI, LLM, and image-generation articles — the aim isn't to derive anything, just to recognize weighted sums, S-curves, probabilities, and the 'nudge toward the answer' step of training.
Hands-on Qwen3.6-35B-A3B (23GB 4bit GGUF) on M1 Max 64GB via Ollama 0.20.6. Generation speed stays at 27 tok/s — same as Qwen3.5-35B-A3B — but the same prompt produces 13× more thinking tokens. Multi-turn behavior, persona handling, and a three-tier NSFW probe included.
Alibaba's Qwen3.6-Max-Preview and Moonshot AI's Kimi K2.6 were released within a 24-hour window on April 20–21, 2026. A side-by-side look at specs, benchmarks, distribution, and agent-side features for the two flagships.
A three-link chain of mmap → MTLBuffer(bytesNoCopy) → Wasmtime MemoryCreator that makes a Wasm linear memory share the same physical bytes as a Metal GPU buffer. Llama 3.2 1B runs at 9ms/token on M1.
Two simultaneous announcements from Cloudflare Agents Week 2026: Agent Memory manages agent recall via Durable Objects, Vectorize, and Workers AI, while isitagentready.com scores how well sites are prepared for agents.
Alibaba's Qwen team released Qwen3.6-35B-A3B as open weights. A 40-layer hybrid of Gated DeltaNet, Gated Attention, and MoE hits 73.4 on SWE-bench Verified, 37.0 on MCPMark, and 1397 on QwenWebBench.
Running local models and still getting refusals? LLM safety stacks five layers: input filter, system prompt, RLHF, Constitutional AI, output filter. Where abliterated and uncensored variants cut, and which blocks survive on Gemini, Claude, and local LLMs.
