APIR - The Verification Layer
EU AI Act compliance, Insurance and Ghost Audit
What it does
Your AI agents are unmonitored, unaudited, and uninsured. The EU AI Act takes effect Aug 2 - fines up to €35M. APIR is the verification layer for the Agent Economy. Purpose-built for AI agent compliance, insurance, and governance. 10 proprietary systems: Trust Genome, Ghost Audit (24/7), Kill Switch (47ms), Insurance Score (FICO for AI), Behavioral Fingerprinting, Agent Credit Bureau, AI-SBOM, A2ATP, AIM, and PRO. 3 sectors. 6 frameworks. 47 jurisdictions. Start free today.
Does the same job
all alternatives →- EOEuConform – Offline-first EU AI Act compliance tool (open source)Jan 2026 · github.com · ▲71
I built this as a personal open-source project to explore how EU AI Act requirements can be translated into concrete, inspectable technical checks. The core idea is local-first compliance: – risk classification (Articles 5–15, incl. prohibited use cases) – bias evaluation using CrowS-Pairs – automatic Annex IV–oriented PDF reports – no cloud services or external APIs (browser-based + Ollama) I’m especially interested in feedback on whether this kind of technical framing of AI regulation makes sense in real-world projects.
- OSOpen-Source Article 12 Logging Infrastructure for the EU AI ActMar 2026 · ▲42
EU legislation (which affects UK and US companies in many cases) requires being able to truly reconstruct agentic events. I've worked in a number of regulated industries off & on for years, and recently hit this gap. We already had strong observability, but if someone asked me to prove exactly what happened for a specific AI decision X months ago (and demonstrate that the log trail had not been altered), I could not. The EU AI Act has already entered force, and its Article 12 kicks-in in August this year, requiring automatic event recording and six-month retention for high-risk systems,…
Pitstop EU AI Act Compliance ScannerMay 2026 · thepitstop.ai · ▲3August 2026. €35M penalties. Is your AI agent compliant?
Adaptive Intelligence FabricJun 2026 · aif.predictai.ai · ▲3Tamper-proof audit trails for every AI agent
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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
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com