ETrust
The trust layer for production-ready AI agents
What it does
ETrust Labs builds the foundational trust layer for production-ready AI agents. We help organisations create identity for AI agents, enforce permissions controls, generate dynamic trust scores, and maintain tamper-proof audit trails. Organisations gain control, visibility, and provable accountability across systems and workflows. Designed for regulated industries including Fintech, InsurTech and eCommerce. ETrust Labs enables secure deployment and governance of AI agents at scale.
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TrustClaw by ComposioMay 2026 · github.com · ▲152Self-hosted AI agent that connects 1000+ apps on Vercel
- TDTrustGraph – Do More with AI with Less (Open Source AI Infrastructure)2024 · github.com · ▲22
Hi HN, We’re Daniel and Mark, the creators of TrustGraph (https://github.com/trustgraph-ai/trustgraph). TrustGraph is an open source, full end-to-end AI infrastructure that automates knowledge graph building and querying along with modular agent integration. A unique aspect of TrustGraph is that the graph building is a one-time process that builds reusable knowledge cores that can be stored, shared, and reloaded. You can read more about TrustGraph knowledge cores here (https://trustgraph.ai/docs/cores/). Throughout our careers, we’ve been faced…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
