Tested Gemma 4 MTP drafter on M1 Max 64GB with mlx-vlm 0.5.0. Only the 26B A4B MoE got +13%; 31B Dense and E4B got slower. Code gen vs short haiku prompts flip the result.
Reading Google's MTP drafter docs, vLLM recipes, and the AI for Developers guide. The 3x claim holds for 31B Dense but 26B A4B MoE stalls at batch 1 because speculative decoding verification loads extra expert weights per candidate token.
Tested connecting MCP servers to Ollama local LLMs on M1 Max 64GB. MCPHost is deprecated, tool calling breaks with quantized models, and context fills fast. Includes working TypeScript and Python custom MCP server setups.
Hands-on log of building the DEV article's PDF RAG on M1 Max 64GB, extending it with images via CLIP, and pushing through Japanese with bge-m3 + Qwen3.6 35B. Documents the modality gap, the dual inference server crash, and LLM-jp 4-8B's empty chat template silently dropping the system role.
Notes on a DEV Community article that wires up FastAPI as an OpenAI-compatible RAG API layer with llama.cpp, Chroma, and Open WebUI, plus where the architecture fits and what to watch for.
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.
A port that replaces TRELLIS.2's CUDA-only libraries (flash_attn, nvdiffrast, sparse 3D convolution) with pure-PyTorch equivalents and runs Microsoft's 4B image-to-3D model on an M4 Pro in about 3.5 minutes without any NVIDIA GPU.
LLM safety stacks five layers — input filter, system prompt, RLHF, Constitutional AI, output filter — and each provider blocks at different layers. A breakdown of where abliterated vs uncensored models cut, and the default censorship level baked into local LLMs.
9 Japanese-specialized LLMs as of April 2026 — LLM-jp-4 (11.7T tokens from scratch), PLaMo, Nemotron Nano 9B JP (#1 sub-10B on Nejumi 4), Swallow 30B-A3B, Namazu — broken down by whether they were scratch-trained, continued pre-trained, or post-trained, with size, license, benchmark scores.
