EU AI Act Articlr 4 AI Compliance Pack
Audit-ready AI literacy compliance for EU organisations
What it does
Most organisations know the EU AI Act is coming. Far fewer know what AI literacy compliance actually looks like in practice. This toolkit helps teams prepare for Article 4 obligations with practical templates, role-based literacy mapping, policy frameworks, implementation guidance, audit-ready documentation structures, and a 90-day roadmap for organisational readiness.
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.
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AI Disclosure RegisterAug 2026 · ai-disclosureregister.com · ▲2EU AI Act Article 50 compliance & audit logs in 60s
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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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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…
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