AuraWatch
Deterministic security auto-fixes
What it does
AI coding assistants guess. AuraWatch proves. AuraWatch is a deterministic security engine for your IDE that relies on mathematical proofs (Z3) and structural tree transformations (AST) rather than probabilistic LLMs. It doesn't just flag deep architectural vulnerabilities—it surgically patches them with zero syntax errors, zero hallucinations, and zero dropped comments. Stop hoping your AI wrote safe code, and start mathematically proving it.
Does the same job
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Aura: Agents + Git + Intent Open SourceJul 2026 · auravcs.com · ▲174OSS IDE for controlling AI coding agents with built in loops
Aura – a Rust agent that investigates and fixes production incidents4d ago · github.com · ▲25AURA is a production-tested SRE agent platform you can deploy in minutes. AURA handles the guardrails, APIs, state management, streaming, and failure handling required to put AI to work safely on production infrastructure. - mezmo/aura
- ALAura – Like robots.txt, but for AI actions2025 · github.com · ▲41
Hi, I've been watching the rise of AI agents with a mix of excitement and dread. We're building incredible tools that can browse the web, but we're forcing them to navigate a world built for human eyes. They scrape screens and parse fragile DOMs. We're trying to tame them to act like humans. I believe this is fundamentally wrong. The goal isn't to make AI operate at a human level, but to unlock its super-human potential. The current path is dangerous. When agents from OpenAI, Google, and others start browsing at scale and speed, concepts like UI/UX will lose meaning for them. The entire…
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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, May 2026
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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