Xiaomi launched two MiMo-V2.5 models at once. MiMo-V2.5-Pro hits SWE-bench Pro 57.2, Claw-Eval 63.8, and τ3-Bench 72.9 — frontier-tier — while MiMo-V2.5 brings native omnimodality plus a 1M context. Both are API-only for now; open weights are promised but unscheduled.
NVIDIA's build.nvidia.com serves a free inference API that covers 100+ models including MiniMax M2.7, GLM-5, Kimi K2.5, DeepSeek, GPT-OSS, and Sarvam-M. Because integrate.api.nvidia.com/v1 is OpenAI-compatible, OpenClaw, OpenCode, Zed, and Cursor can call it directly.
The NotebookLM clone open-notebook assumes Docker and cloud APIs by default. I installed SurrealDB natively, ran four processes in tmux, and wired everything through Ollama's qwen3.6:35b and bge-m3. I fed it the Qwen3.6 benchmark article I wrote this morning, and it answered with the correct numbers.
Tried Qwen3.6-27B on both Ollama and MLX. Ollama couldn't load the VL-projector-embedded GGUF, MLX ran it at 11 tok/s. On the side, running 35B-A3B under MLX was roughly 2× faster than the Ollama GGUF. Also had both models build a BBS to gauge intent handling.
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.
