Tenet AI
Stop tracing AI errors. Start proving your AI decisions.
What it does
Go beyond observability. Tenet is the Decision Auditability Platform for high-stakes AI that turns your agent's black box into a provable, ISO-ready Reasoning Ledger. First decision-centric layer for AI agents, built on three pillars: 1. PROOF. Capture every agent decision permanently on-ledger. 2. VERIFICATION. Replay & re-execute decisions semantically. 3. IMPROVEMENT. Every error & Human Override → structured fine-tuning data. Prove compliance with EU AI Act, SOC2 & HIPAA — in 2 lines of code
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DecisionBox EnterpriseApr 2026 · decisionbox.io · ▲67Agent that writes SQL for you to validate database insights

- TATraceMem – A trace-native memory layer for AI agent decisionsJan 2026 · tracemem.com · ▲15
Hi HN, There’s been a lot of discussion lately around context graphs, decision traces, and how AI systems reason. One thing we kept running into: when AI agents make real decisions, the why behind those decisions often disappears. The context is scattered across prompts, tools, policies, and approvals. Logs show what happened, but not why it was allowed. TraceMem is an attempt to make decision context durable. It records the reasoning, authority, and context behind AI actions as a system of record, not as monitoring data, but as memory. Happy to share more details or answer questions. - Tommi
A replayable A2A jury for tracing how agents influence decisions28d ago · github.com · ▲20Build autonomous Python agents with native Agent-to-Agent (A2A) communication - protolink/examples/ai_courtroom at main · nMaroulis/protolink
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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…
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Launched alongside, April 2026
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Thought the resources for GPU arch were lacking, so here we are
Life & fun · Apr 2026 · jaso1024.com
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Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
AI · Apr 2026 · github.com

- BC
Life & fun · Apr 2026 · sam-burns.com
- IB
With social media and now AI, its important to keep the indie web alive. There are many people who write frequently. Blogosphere tries to highlight them by fetching the recent posts from personal blogs across many categories. There are two versions: Minimal (HN-inspired, fast, static): https://text.blogosphere.app/ Non-minimal: https://blogosphere.app/ If you don't find your blog (or your favorite ones), please add them. I will review and approve it.
AI · Apr 2026 · text.blogosphere.app