Kakunin
Cryptographic identity for autonomous AI agents
What it does
AI agents are making real decisions — executing trades, accessing patient data, filing documents. But there's no way to prove what an agent is, what it's allowed to do, or what it actually did. Kakunin fixes that. It issues X.509 certificates to AI agents, monitors their behavior in real time, and auto-revokes access when risk crosses a threshold. Regulator-ready audit reports in one click. Works with LangChain, CrewAI, AutoGen, CAMEL, Mastra, Vercel AI SDK. Python and TypeScript SDKs.
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I built a platform where you solve tasks together with AI agents (Claude Code, Codex, Cursor — any agent via SSH). Isolated sandbox environments, automated test scoring, global leaderboard. Tasks range from easy (AI one-shots it) to hard (requires human help). Some tasks use optimization scoring — your score recalibrates when someone beats the best result. Built it in 6 days as a solo founder. 100% of code written with Claude Code and Codex. Stack: Go, Next.js, K8s, Supabase, Stripe.
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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
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
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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…
Life & fun · May 2026 · github.com
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Dev tools · May 2026 · github.com