AI Coding Live News — Real-Time 𝕏/Twitter Updates & Breakthroughs

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LM Studio Bionic 1.1.10 adds the oMLX engine, bringing RAM-plus-SSD KV caching and MLX speculative decoding to local models on Macs

LM Studio posted on X at 15:32 UTC on Oct 29 that users can now download a new oMLX engine inside LM Studio and run it with MLX models they already have. The official changelog lists it in Bionic 1.1.10, alongside speculative decoding for MLX models and an in-app terminal. oMLX is an open-source Apple Silicon inference server maintained by developer jundot (Apache 2.0, about 23,000 GitHub stars) built around continuous batching and a two-tier RAM and SSD KV cache. LM Studio has not published speed comparisons for the engine.

Key Takeaways

LM Studio says the oMLX engine can now be downloaded inside the app and used with existing MLX models; the post has about 540 likes (post)
The Bionic 1.1.10 changelog (Oct 29) also lists speculative decoding for MLX models, a public in-app terminal and llama.cpp 2.66.0 extension packs (changelog)
oMLX is Apache 2.0 open source with about 23,000 GitHub stars and needs macOS 15+ on Apple Silicon M1 to M5 (GitHub)
Its core is a two-tier KV cache: hot blocks stay in RAM, overflow is written to SSD as safetensors, and matching prefixes are restored from disk even after a restart
LM Studio has published no speed comparison with its existing MLX engine; one user reported that restart-to-update failed after launch and LM Studio is investigating

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Linked Models & Agents:

⚡ MLX🤖 LM Studio 🤖 oMLX 🌐 LM Studio 🌐 Hugging Face 🌐 Apple

#LM Studio#oMLX#MLX#Apple Silicon#local LLM#KV cache#inference engine

GitHub Next@GitHubNext·Collected 45d ago

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GitHub Next opens LocalJev: local Jev-compatible decision API on oMLX

GitHub Next open-sourced LocalJev (github.com/githubnext/localjev): a morning build of a “poor man’s Jev” for teammates without TypeSafe Jev access. It exposes a Jev-compatible POST /v1/systemone API in Bun/TypeScript, defaults to oMLX-backed local models such as DiffusionGemma, and reports evals across DiffusionGemma, Qwen MoE, Gemma 4 MoE, and Gemma 4 e4b/e2b (benchmark report in-repo). The LocalJev server works with normal Jev client wrappers, but probabilities are model-reported rather than logit reads—calibrate before consequential use.

Key Takeaways

Open-sources LocalJev: a local Jev-compatible POST /v1/systemone decision API (Bun/TypeScript).
Defaults to oMLX-backed local models such as DiffusionGemma, with multi-model evals in-repo.
Works with normal Jev client wrappers; probabilities are model-reported, not logit reads—calibrate first.

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Linked Models & Agents:

⚡ diffusiongemma⚡ qwen⚡ Gemma 4🌐 github

#LocalJev#GitHub Next#Jev#TypeSafe#oMLX#DiffusionGemma#local LLM#decision model

Inco AI@inco_ai·Collected 45d ago

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Inco open-sources Splash: ~144 tok/s for Qwen3.8-27B on M5 Max

Inco AI open-sourced Splash, a local inference engine built for Apple silicon and models like Qwen3.8-27B. On an M5 Max MacBook Pro it reports about 144 tok/s—up to 3× Ollama, 2× oMLX, and nearly 4× when an agent fans out sub-agents. LM Studio shipped day-0 support under Settings > Runtime > Experimental backends.

Key Takeaways

Open-source Splash: local inference tuned for Apple silicon and Qwen3.8-27B.
Demo: ~144 tok/s on M5 Max; up to ~3× Ollama / 2× oMLX, nearly 4× with agent fan-out.
LM Studio day-0 support; repo github.com/incoai/splash; blog lmstudio.ai/blog/splash-engine.

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Linked Models & Agents:

⚡ Qwen3.8-27B 🌐 inco 🌐 Qwen (通义千问)🌐 lmstudio

#Inco#Splash#Qwen3.8-27B#Apple Silicon#M5 Max#LM Studio#local inference#open source