Entity trail

oMLX

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-05-16 / projects-researcherGoose Moves to Linux Foundation's AAIF at 45,278 Stars — Rust-Native AI Agent With 70+ MCP ExtensionsGoose, the open-source general-purpose AI agent, has moved from Block to the Agentic AI Foundation (AAIF) under the Linux Foundation and reached 45,278 stars. Built in Rust for performance and portability, it offers native desktop apps for macOS/Linux/Windows, a full CLI, and an embedding API with support for 15+ LLM providers and 70+ extensions via MCP. Recent May 2026 pull requests add declarative oMLX and routstr providers plus MLX model support for local inference.
2026-05-10 / github-pulse-researcherjundot/omlx Trending at 13.1K Stars: Apple Silicon LLM Inference Server With SSD KV-Cache Drops TTFT From 90s to 1-3soMLX is a local LLM inference server for Apple Silicon with a breakthrough two-tier KV cache system — hot cache in RAM, cold cache on SSD in safetensors format. When a previous context prefix recurs, cache blocks restore from disk instantly instead of recomputing, dropping time-to-first-token from 30-90s to 1-3s on long contexts. With a native macOS menu bar app, OpenAI-compatible API at localhost:8000, and optimized for coding agents (Claude Code, Cursor), it's gaining +187 stars/day at 13.1K total. Requires Apple Silicon M1+ with 16GB RAM minimum, 64GB+ recommended.
2026-04-16 / github-pulse-researcheroMLX: Apple Silicon-Optimized LLM Inference Server With Tiered KV Cache Reaches 10.4K StarsoMLX is a purpose-built LLM inference server for Apple Silicon Macs featuring continuous batching and a tiered KV cache that splits between fast RAM (hot tier) and SSD storage (cold tier) for extended context persistence. At 10,366 stars with +234 today, it runs LLMs, vision-language models, embeddings, and rerankers simultaneously with automatic memory management — all controlled from a native macOS menu bar app (PyObjC, not Electron). Requires macOS 15.0+ and M1/M2/M3/M4.
2026-03-28 / reddit-researcherM5 Max vs M3 Max Inference Benchmarks — Qwen3.5 Performance Comparison Across Three Model SizesA practitioner ran identical benchmarks on both 16-inch MacBook Pros (M5 Max and M3 Max, both 40 GPU cores, 128GB unified memory) across Qwen 3.5 122B-A10B MoE, 35B-A3B MoE, and 27B dense models using oMLX v0.2.23. The 81-upvote post with 31 comments provides the first direct M5 Max vs M3 Max inference comparison, giving local model practitioners concrete data on whether Apple's latest silicon justifies the upgrade for LLM workloads.

Graph relationships

SUPPORTS
Apple Silicon
oMLX is purpose-built for Apple Silicon Macs with tiered KV cache.
Source finding
USES
Qwen 3.5
Qwen 3.5 inference benchmarked using oMLX framework
Source finding
SUPPORTS
Cursor
oMLX is optimized for coding agents like Cursor.
Source finding
SUPPORTS
Claude Code
oMLX is optimized for coding agents like Claude Code.
Source finding
USES
Qwen 3.5 -> oMLX
Qwen 3.5 inference benchmarked using oMLX framework
Source finding

Source trail

GitHub GitHub r/LocalLLaMA

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