ChromiumFish
AI Native Fingerprint-Hardened Browser for Web Scraping
What it does
An AI-native, fingerprint-hardened Chromium fork with a drop-in Playwright harness for Python and Node.
Does the same job
all alternatives →- WPWe Put Chromium on a Unikernel (OSS Apache 2.0)2025 · github.com · ▲132
We’ve been building infrastructure to spin up browsers for AI agents. Originally, we built[0] it as a pool of warm Docker containers running Chromium, exposing: - Chrome DevTools Protocol (for Playwright/Puppeteer) - noVNC (for live view) We’ve been following the unikernel space for a while, so we decided to see if we could get our image on one. We went with Unikraft Cloud[1]. Here’s how it did: - Boot-up time: 10–20ms (vs. ~5s for Docker containers) - Near 0 CPU/memory consumption when idle - Still ~8GB RAM when active (headful Chromium) Potential use cases: - Standby mode during…
- COChromiumFish – Open-source fingerprint-hardened Browser for ScrapingJun 2026 · github.com · ▲7
- CAChromiumoxid – An Async Headless Chrome API in Rust2020 · github.com · ▲127
- OSOpen-source browser for AI agentsMar 2026 · github.com · ▲155
Hi HN, I forked chromium and built agent-browser-protocol (ABP) after noticing that most browser-agent failures aren’t really about the model misunderstanding the page. Instead, the problem is that the model is reasoning from a stale state. ABP is designed to keep the acting agent synchronized with the browser at every step. After each action (click, type, etc), it freezes JavaScript execution and rendering, then captures the resulting state. It also compiles the notable events that occurred during that action loop, such as navigation, file pickers, permission prompts, alerts, and downloads,…
- SDSteel.dev – An open-source browser API for AI agents and apps2024 · github.com · ▲114
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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, June 2026
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- H6Homebrew 6.0.0▲1,481
Today, I’m proud to announce Homebrew 6.0.0. The most significant changes since 5.1.0 are a new tap trust security mechanism, the new faster, smaller, default internal Homebrew JSON API, sandboxing on Linux, better defaults informed by our user survey, many brew bundle improvements, improved performance and initial support for macOS 27 (Golden Gate). Happy to discuss any questions here!
Dev tools · Jun 2026 · brew.sh
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