Noah Labs
The AI-native IDE built for classified environments
What it does
Most AI coding tools assume you're on the cloud. Sentinel was built for teams that never can be. The AI-native IDE for defense, federal & aerospace — fully airgapped, no telemetry, no cloud calls. ✅ 7.5M token codebase context ✅ 25K-line agent mode ✅ Formal provers built in ✅ 100% local & airgapped https://www.noahlabs.ai/
Does the same job
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- BDBoxes.dev: ditch localhost; run Claude Code and Codex in the cloudJun 2026 · boxes.dev · ▲105
Hi HN, we’re Nick and Drew, and we’re building boxes.dev – the first cloud-only agentic dev environment (ADE) that gives every Codex and Claude Code agent its own cloud computer. We’re two engineers who previously built Gem (co-founder/CTO and first hire), and we spent the last year coding almost exclusively using Codex and Claude Code. It’s been a huge change to how we code, and it’s been exhilarating seeing the models keep getting better – but we eventually realized that developing on localhost was holding us back: - Git worktrees are clunky to set up and use for parallelizing work -…
AI Hive May 2026 · app.aihive.global · ▲53Build, deploy, and run all your AI agents in one platform.
- IGI got tired of switching AI tools, so I built an IDE with 11 of themDec 2025 · hivetechs.io · ▲20
Each AI has strengths - Claude reasons well, Gemini handles long context, Codex integrates with GitHub. But switching between them means losing context. Built HiveTechs: one workspace where Claude Code, Gemini CLI, Codex, DROID, and 7 others run in integrated terminals with shared memory. Also added consensus validation - 3 AIs analyze independently, 4th synthesizes. Real IDE with Monaco editor, Git, PTY terminals. Not a wrapper. Looking for feedback: hivetechs.io
More ai this month
the category →
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.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
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