Rapid-MLX
openclaw
| Rapid-MLX | openclaw | |
|---|---|---|
| 6 | 208 | |
| 2,756 | 378,511 | |
| 90.1% | 3.9% | |
| 9.8 | 10.0 | |
| 4 days ago | 2 days ago | |
| Python | TypeScript | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Rapid-MLX
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Chrome's Gemini Nano Prompt API: A Step-by-Step Guide
💡 💡 Make the fallback cheap to operate. The whole point of using Nano on the supported path is reduced cost. If your fallback is GPT-5.5 at $5/M tokens, you've moved the bill, not deleted it. Two patterns work well: (1) route the fallback to a smaller hosted model (Haiku, Gemini Flash, Mistral Small) that matches Nano's "short summarization" sweet spot; (2) for Mac users specifically, run Rapid-MLX as your /api/llm endpoint — Apple Silicon owners get on-device performance via your server's Mac, not theirs. Same thesis as our DeepClaude guide: the harness is one product, the model is another, and you can swap them.
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Anthropic is allowing the Claude CLI to run OpenClaw again
> Large-context requests auto-route to a cloud LLM (GPT-5, Claude, etc.) when local prefill would be slow. Routing based on new tokens after cache hit. --cloud-model openai/gpt-5 --cloud-threshold 20000
https://github.com/raullenchai/Rapid-MLX
- Show HN: Rapid-MLX – Run local LLMs on Mac, 2-3x faster than alternatives
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Gemma 4 on Apple Silicon: 85 tok/s with a pip install
I've verified this end-to-end with structured output (output_type=BaseModel), streaming, multi-turn conversations, and multi-tool workflows. Test suite here.
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vLLM-mlx – 65 tok/s LLM inference on Mac with tool calling and prompt caching
pip install git+https://github.com/raullenchai/vllm-mlx.git
openclaw
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Self-hosting OpenClaw: a money trap and two silent failures
I run OpenClaw on a Hetzner CAX ARM VPS. It talks to me over Signal and does a morning press review. Three gotchas on that box are worth writing down: one money trap in the model-routing layer, and two silent failures that each left the briefing dead for days. In case someone is staring at the same thing.
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I Tested 33 AI Memory Engines — Here's What Actually Works
The base stack (layers 1–2) is built into OpenClaw — conversation compression, native memory files, and semantic search work out of the box. The long-term engine (layer 3) requires additional setup: Mem0 needs a vector store, Cognee needs a graph database, Graphiti runs on FalkorDB.
- OpenClaw: Own Personal AI Assistant. Any OS. Any Platform
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Hermes vs OpenClaw: The Two Most-Starred AI Agent Frameworks of 2026
OpenClaw — github.com/openclaw/openclaw
- OpenClaw and Claude Code - Multi Agents talking via Handoff File
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Hermes Just Killed OpenClaw (Here's Why)
For context, I checked the public repos directly: openclaw/openclaw and NousResearch/hermes-agent. OpenClaw has the bigger gravity right now. Hermes has the more interesting agent-runtime thesis.
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AI Coding Tip 020 - Create a Second Brain
Set up OpenClaw or a local LLM (Ollama or LM Studio) to index your vault and answer questions via Telegram or WhatsApp, as a private assistant that never sends your data to the cloud.
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The last six months in LLMs in five minutes
Haven’t noticed much significant progress in LLMs myself in 6 months (significant as in new or vastly improved capabilities or understanding, not new releases, there are plenty of those).
So what’s happening with openclaw, the biggest experiment in agentic, vibe coded by the agents themselves? The thing that was so hot a few months ago.
https://github.com/openclaw/openclaw/pulse?period=daily
279 commits to main from 77 authors in the last 24 hours.
Why is there so much churn and how could you trust it with your data? This is changes in ONE day!
If these are useful changes, surely it’d be superhuman by now given months of dev work.
What are people using this for?
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🔮 Hermes Agent 🤖: The Self-Improving Agent Framework — and How It Compares to OpenClaw & GoClaw 📊
⚔️ How it compares with two adjacent open-source projects: OpenClaw and GoClaw — when to pick which
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Installing OpenClaw on the Homelab
OpenClaw is that thing. It's an open-source personal AI assistant with 367K GitHub stars, a plugin ecosystem, and connectors for every chat platform you can name. The pitch: "Your own personal AI assistant. Any OS. Any Platform."
What are some alternatives?
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
pi-mono - AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods [Moved to: https://github.com/earendil-works/pi]
MindsDB - General-purpose AI designed for knowledge workers — creators, strategists, and operators — and individuals seeking AI systems they can truly control to help them get work done, with full flexibility to extend and deploy anywhere (VPC, on-prem, or cloud).
clawhub - Skill + Plugin Registry for OpenClaw
gym - A toolkit for developing and comparing reinforcement learning algorithms.
moltbot - Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞 [Moved to: https://github.com/openclaw/openclaw]