MCP’s are great tools. They empower agents to accomplish so much more. But where are they not as useful as a structured integration?
When there are multiple steps that require steps like approval, the use of additional agents for compliance, research, and another refinement tasks Auditable tasks and rollbacks.
MCP’s are great tools, but they do not replace structure and good process design.
When an MCP is not enough, use ClawRecipes combined with ClawKitchen as your operation layer. It’s what we’re built for. @openclaw
@intheworldofai We just launched our Hermes version of Recipes! It’s in testing rn. File-first agent team orchestration. Try it out! https://t.co/bpTynMjkKO
OpenClaw Had a Rough Week https://t.co/vLImljNECf via @openclaw
This was posted a week ago. A series of missteps cost OC dearly. I still know people on 2026.4.x releases because they spent days trying to figure out what was going wrong. We finally updated CR and CK to work with 2026.5.x releases. Lets hope OC has learned from their mistakes and dont roll out major changes in patch releases.
New Release!! We've tested and pushed new packages for CR and CK. Both now support @openclaw 2026.5.x
The best way to install is:
openclaw plugins install @jiggai/recipes --force
openclaw plugins install @jiggai/kitchen --force
Force is required on recipes because clawhub is broken, they still have the older package.
🚨 BREAKING: Someone just made OpenAI's Whisper transcribe 2.5 hours of audio in 98 seconds. 100% OPEN SOURCE.
It runs entirely on your GPU. No API keys. No cloud. No subscription.
It's called Insanely Fast Whisper.
You drop in an audio file. One command. You come back and there's a clean, timestamped transcript waiting. Not a rough draft. Not a partial output. The entire thing. Done.
Not a wrapper.
Not a web app.
A CLI that turns your local machine into a transcription engine that makes paid services look embarrassing.
Here's what it does on its own:
→ Transcribes 150 minutes of audio in under 98 seconds using Flash Attention 2, same model, 19x faster, zero quality loss
→ Auto-detects language across dozens of languages, or translates directly into English with a single flag
→ Speaker diarization built in, knows who said what, not just what was said
→ Word-level and chunk-level timestamps so you can jump to any exact moment in any recording
→ Runs on NVIDIA GPUs and Apple Silicon Macs with zero code changes between them
→ Works on Google Colab free tier if you don't own a GPU at all
Here's how fast it actually is:
Standard Whisper large-v3 out of the box: 31 minutes to process 2.5 hours of audio. The same exact model with Flash Attention 2 and batching: 1 minute 38 seconds. Same weights. Same accuracy. One flag difference.
Here's the wildest part:
This never started as a product. It was a benchmark demo to show what Hugging Face Transformers could do. Then the community started using it for real work. Podcast transcription. Legal recordings. Research interviews. Meeting notes at scale. The team kept adding what people actually needed until a benchmark became a full CLI that nobody planned to build.
8.8K GitHub stars. 100% Open Source.
@steipete Can you add remove capability to clawhub? You're forcing pluginApi rather than pluginApiRange, and people trying to update using a new version, we can't just support the range, we need to update pluginApi which can force unnecessary upgrades. It'll be easier to simply manage in npm then both clawhub and npm