btw the codex for claude code plugin is open sauce – go play around with it, tinker and make it fit for your use cases gotta love the open standards
OPEN SOURCE
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Free Open Source Offline Transcription App Built with AI
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I Built a Free Open Source Offline Transcription App — Here's How https://
madebyagents.com/blog/free-open
-source-offline-transcription-app-echos?utm_source=dlvr.it&utm_medium=twitter
… #ArtificialIntelligence #MachineLearning #DataScience #DataEngineering #CloudComputing #MLOps #Innovation #BusinessStrategy -
Team Congratulations for llama.cpp Open Source Project
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Thanks for llama.cpp and congrats to you, @ngxson
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OpenClaw Alternative: Pokee Claw with Enhanced Security
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If you liked OpenClaw but weren't a fan of the security risks, try it out:
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PokeeClaw: Securing Local AI Assistants with Isolated Sandbox
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OpenClaw has proven that local AI assistants have product-market fit. But the big issue with them has been security.
— François Chollet (@fchollet) 30 mars 2026
The team at @Pokee_AI is fixing it with PokeeClaw: works like OpenClaw, but with in a secure sandbox architecture with isolated environments, approval workflows,… https://t.co/7Q5cmQJbzhOpenClaw has proven that local AI assistants have product-market fit. But the big issue with them has been security. The team at @Pokee_AI is fixing it with PokeeClaw: works like OpenClaw, but in a secure sandbox architecture with isolated environments, approval workflows,
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PyTorch trunc_normal_ initialization bugs in LLM training code
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Okay LLM + PyTorch people, trunc_normal_, what the fuck! Many LLM inits use it w/ default cutoffs. It's either not doing anything or it's quite broken due 2 issues. 1. The a/b cutoffs in PyTorch are not in std-devs, they are absolute. So w/ a std=0.02, and -2/2 (default arg) cutoffs that's 100σ!! That is a normal distribution, trun isn't doing anything. 2. There are numerical issues. Even in float32, the truncation produces a handful of -2 (lower cutoff) values, 100σ!! That's incomprehensibly improbable. I doubt a float32 or even float64 algo could even produce it, but clamping a bad float value does. Olmo (@allenai codebases) appear to be one of the few that uses trunc_normal_ and bothered to set the cutoffs properly. It'd be nice to see more train code opened up as a default. We so often only end up with a sanitized version of the inference/fine-tune friendly model these days and may lose details like original init. I've known about #1 for ages, I have an alternate trunc_normal_tf_ implementation in timm for that reason. But I saw those -2's last week when I was debugging something and was a little surprised.
→ View original post on X — @jeremyphoward, 2026-03-30 15:09 UTC
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llama.cpp reaches 100k stars, local AI movement thriving
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llama.cpp at 100k stars now that 90% of the code worldwide is being written by AI agents, I predict that within 3-6 months, 90% of all AI agents will be running locally with llama.cpp 😄 Jokes aside, I am going to use this small milestone as an opportunity to reflect a bit on the project and the state of AI from the perspective of local applications. There is a lot to say and discuss and yet it feels less and less important to try to make a point. Opinions about viability of local LLMs are strongly polarized, details are overlooked, the scientific approach is lacking. Arguments are predominantly based on vibes and hype waves. One thing is clear though – local LLMs are used more and more. I expect this trend to continue and likely 2026 will end up being one of the most important years for the local AI movement. I admit that I didn't expect the agentic era to come so quickly to the local LLM space. One year ago, the available models were too computationally expensive for doing long-context tasks. There wasn't an obvious path towards meaningful agentic applications. The memory and compute requirements were huge. Last summer, with the release of gpt-oss, things started to change. It was the first time we saw a glimpse of tool calling that actually works well within the resource constraints of our daily devices. Later in the year, even better models were released and by now, useful local agentic workflows are a reality. Comparing local vs hosted capabilities at a given moment of time is pointless. To try put things into perspective: – We don't need frontier intelligence to automate searches and sending emails – We don't need trillion parameter models to be able to summarize articles or technical documents – We don't need massive GPU data centers to control our home appliances or turn the lights off in the garage I believe that there is a certain level of intelligence we as humans can comprehend and meaningfully utilize to improve our working process. Beyond that level, access to more intelligence becomes unnecessary at best and counterproductive at worst. I also believe that that level of useful artificial intelligence is completely within reach locally and it has always been just a matter of implementing the right software stack to bring it to the end user. With llama.cpp, I am confident that we continue to be on the right track of building that software stack! The llama.cpp project is going stronger than ever. With more than 1500 contributors, the project keeps growing steadily. From technical point of view, I think that llama.cpp + ggml is the only solution that actually makes sense. That is, the software stack must run efficiently on every possible device, hardware and operating system. The technology is too important to be vendor-locked. It has to be developed in the open, by the community, together with the independent hardware vendors. This is the only right way to build something that will truly make a difference in the long run. I won't try to convince you about what is currently and will be possible with local AI. We will just continue to build as usual. I am confident that after the smoke clears and we look objectively at what we have built together, the benefits will be obvious to everyone. Big shoutout to all llama.cpp maintainers. I feel extremely lucky to be able to work together with so many talented contributors. Every day I learn something new and I feel there is so much more cool stuff that we are going to build. Also, I am really thankful that the project continues to have reliable partners to support it! Cheers!
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New local 2GB nanochat model Mr. Chatterbox released
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Mr. Chatterbox is a new 2GB nanochat model trained from scratch by Trip Venturella on "28,000 Victorian-era British texts published between 1837 and 1899" – I released an llm-mrchatterbox plugin which can run it locally on my Mac
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AI Engineering Playbook: Complete Guide and Resources
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Here's the playbook: github.com/louisfb01/ai-engi…