Hands-on running inclusionAI Ling-flash-2.0 (100B / 6.1B active, MXFP4 quant, 54.7GB) on SwiftLM via mlx-swift-lm on an M1 Max 64GB. Covers bailing_moe + MXFP4 support check in mlx-swift, the startup surprise, and what --stream-experts actually saves.
WAI-Illustrious SDXL v17 tested on M1 Max 64GB ComfyUI against v16 with the same seed. Hires fix now auto-corrects hands and feet, the four rating tags (general/sensitive/nsfw/explicit) still drive NSFW output, and v16-trained LoRAs mostly carry over — with one case where they don't.
In its April 23 update, Vercel disclosed customer accounts compromised prior to and independently of the Context.ai incident. Covering the Lumma Stealer infection path, the ShinyHunters $2M BreachForums listing, and what non-sensitive environment variables actually mean.
Reading Anthropic's postmortem and the DEV operational report in the same week reveals that Claude Code's quality degradation and Max weekly quota exhaustion are not separate incidents—they're two sides of the same quality×cost design decisions. A timeline and operational priority walkthrough.
A hands-on build and run of the Swift-based LLM inference server SwiftLM on an M1 Max 64GB. Covers Qwen3.6-35B-A3B and Qwen3.5-122B-A10B, with the same BST, BBS, and persona tests used in the existing Ollama and MLX-lm write-ups.
TRACER, a recent arXiv paper, takes the input/output logs of an LLM classification endpoint and reuses them as training data, then swaps in a lightweight surrogate only on regions that pass a parity gate to cut inference cost. The surrogate absorbs 83–100% of traffic on a 77-class intent dataset and 100% on a 150-class one, while correctly refusing to deploy on an NLI task — that refusal behavior is the interesting part.
Japan's Digital Agency released parts of Gennai, the generative AI platform it runs for central-government staff, on GitHub under MIT / CC BY 4.0. The web app and cloud-specific AI templates for AWS, Azure, and Google Cloud are bundled together so local governments and private companies can redeploy the same stack.
DeepSeek V4 Preview ships V4-Pro (1.6T/49B active) and V4-Flash (284B/13B active) as open weights under MIT, both with 1M context. CSA+HCA hybrid attention, mHC, and the Muon optimizer cut per-token FLOPs at 1M tokens to 27% of V3.2. Day-one API and chat.deepseek.com mode switch covered.
Two open-weight Chinese MoEs landed within 24 hours: Ant Ling-2.6-flash (104B/7.4B active, 7x token-efficiency claim) and Tencent Hy3-preview (295B/21B active, frontier-tier open weights). Specs, licenses, and how they line up against DeepSeek-V3 and GLM-4.5.
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.
