Octopus Code Review is now free for OSI-licensed repos
Show HN: Octopus Code Review is now free for OSI-licensed repos
Furwall
Furwall is a tiny macOS menu bar app. While you're at the keyboard or mouse, the FaceTime camera looks for a human face or upper body. When it doesn't find one, the keyboard stops accepting input. Cat walks across your laptop, nothing happens to your code. Some notes: Apple's Vision framework runs locally. Video is processed in memory and never uploaded. On a block, Furwall saves one local JPEG to ~/.furwall/catpures/. A second Vision pass throws out anything that isn't a cat, so the daily count in the menu only reflects confirmed cats. There is now a folder on my disk that is slowly filling up with photos of Pepper and Beets walking across my keyboard. The camera turns on only while you're at the computer (typing, mouse motion, app switch, screen wake) and powers down 30 seconds after the last activity. The green camera dot tracks that. The keystroke drop uses a CGEventTap at .defaultTap. Furwall ships unsandboxed because of this. A .listenOnly tap with Input Monitoring is enough to see keys, but dropping them needs .defaultTap, which needs Accessibility, which the App Sandbox blocks. Watching keystrokes is sandbox-compatible; stopping them is not. Mouse events are observed (to wake the camera) but never intercepted or dropped, so the menu bar always works. Three escape hatches: click the icon and quit, mash Escape five times in 1.5 seconds for a 5-minute pause, or revoke Accessibility in System Settings (macOS invalidates the tap). If Vision stalls for any reason the keyboard fails open after 10 seconds, which is better than soft-bricking the machine. Furwall never uploads camera frames or keystrokes. Its own network traffic is Sparkle update checks plus the donate sheet's anonymous totals/click counter. One short charity slug per click, no user identifier. The donate item in the menu opens the donate page of a vetted animal-welfare charity for your system Region. Ten orgs across nine regions: Alley Cat Allies and PetSmart Charities in the US, Cats Protection in the UK, Cat Protection Society NSW in Australia, Toronto Cat Rescue in Canada, NSPCA in Ireland, SPCA in New Zealand, Deutscher Tierschutzbund in Germany, La SPA and Fondation 30 Millions d'Amis in France, Japan SPCA in Japan. Each org is registered or recognized under its local charity or nonprofit regime, and the list gets re-vetted every release. No money flows through the app. macOS 15+, signed and notarized, MIT. https://olliewagner.com/furwall
Open-source CLI to generate UI tests from user flows
Show HN: Open-source CLI to generate UI tests from user flows
I built a new word game, Wordtrak
Hi HN! Looking for feedback on this 1v1 and daily word dueling game I've built over the last few months. Play here: https://wordtrak.com/ Or on iOS here: https://apps.apple.com/us/app/wordtrak/id6760442363 (Android version soon!)
Brainio
Show HN: Brainio – Markdown notepad that turns notes into visual mind maps
Got tired of paying $100/mo for SEO tools, so I built an alternative
Show HN: Got tired of paying $100/mo for SEO tools, so I built an alternative
Retrodex
I was looking for apps to track my retro game collection and everything looked.... really dated and not great. So I spent the last 5 months making my own. I've made a lot of apps for other people over the years, but this is the first one I've made for myself. iOS and Android. Let me know what you think!
Bonsai 1.7B ternary model at 442T/s on M4 Max
We took a recently released Bonsai 1.7B ternary model from PrismML (https://github.com/PrismML-Eng/Bonsai-demo) and ran our agentic evolution search on it for 6 hours to optimize the Metal kernels. The search was fully autonomous. Measured against unmodified upstream llama.cpp at the same Bonsai/Q2_0 commit, same M4 Max: - tg128: 309.82 → 442.42 t/s (+42.0%) - pp512: 4250.32 → 4622.63 t/s (+8.8%)
Generate SKILL.md files from URLs, in the browser
I created this tool after writing a few agent skills by hand and noticing this pattern was repetitive. Paste a documentation URL, enter your own model API key, and it gets the page content client-side to create a reusable SKILL.md. There is no backend/proxy, so it stays as secure as possible. I would like feedback on the structure of the output and the edge cases.
Bhatti
Bhatti spins up Linux VMs on any box with KVM — Pi 5, Hetzner AX, cloud VM with nested virt. - Each VM has its own kernel, filesystem, and IP - Idle VMs pause their CPUs and snapshot themselves to disk; the next request wakes them in 3.7ms warm or 360ms cold (p50, Hetzner AX102) - Publish any port → public URL with auto-wake on first hit - Pull any OCI/Docker image as a rootfs, or save a running sandbox as one - Multi-tenant from day one — per-user bridges, encrypted secrets, rate limits - Single Go binary, Apache 2.0 The decisions page is the most fun read on the site: vsock state after restore, why all snapshots are Full, the systemctl shim, the ARP retransmit trick. curl -fsSL bhatti.sh/install | sudo bash (sudo because the daemon needs /dev/kvm and sets up the Firecracker jailer + a bridge; the CLI-only install — pipe to plain `bash` — needs no root) Site: https://bhatti.sh Repo: https://github.com/sahil-shubham/bhatti Decisions & learnings: https://bhatti.sh/docs/under-the-hood/decisions/
State of the Art of Coding Models, According to Hacker News Commenters
Hello HN, I was away from my computer for two weeks, and after coming back and reading the latest discussions on HN about coding assistants (models, harnesses), I felt very out of the loop. My normal process would have been to keep reading and figure out the latest and greatest from people's comments, but I wanted to try and automate this process. Basically the goal is to get a quick overview over which coding models are popular on HN. A next iteration could also scan for harnesses that people use, or info on self-hosting or hardware setups. I wrote a short intro on the page about the pipeline that collects and analyzes the data, but feel free to ask for more details or check the Google Sheet for more info. https://hnup.date/hn-sota
Large Scale Article Extract of Newspapers 1730s-1960s
Hello HN, over the past 7 months I've spent nearly 3,000 hours on building SNEWPAPERS, the first historical newpaper archive with full-text extractions, nearly perfect OCR, a vast categorization taxonomy and of course with semantic and agentic search capabilities. Problem: I wanted to search through newspaper archives, but when I tried every service only lets you search for keywords and dates, and gives you back raw images of the papers, and too many of them with no context. A sea of noise. Solution: I taught machines how to read the newspapers and so far I've extracted the content from > 600k pages (about 5TB) from the Chronicling America collection. Problems I had to deal with were an infinite variety of layouts, font sizes, image scan qualities, resolutions, aspect ratios, navigating around the images on the page. I also had to figure out how to get OCR to be nearly perfect so people wouldn't hate reading the extracts. I stitched together a multi-model pipeline (layout tech, ocr tech, llm, vllm) with heuristics to go from layout -> segmentation -> classification. I put it all in OpenSearch / Postgres and made it semantically searchable and also put an agentic search tool on top that knows how to use the API really well and helps you write queries to find what you're looking for. Happy to discuss AWS architecture and scaling as well, that was tough! If you have five minutes and you just want to jump in and have your own personalized experience, what I would suggest is: Before searching for anything, go to the Sleuth page Ask it about anything from 1736 to 1963, maybe 1 or 2 follow up questions Then go to the search page so you can see the queries it wrote for you (bottom left "saved queries") and uncover more info on whatever it is you're interested in If you think it's cool and you want to learn more, then there's about 10 minutes of video guides on the various capabilities in "Guide" on the nav bar Some other people have also taken a crack at this, notably: https://dell-research-harvard.github.io/resources/americanst... (very good attempt) https://labs.loc.gov/work/experiments/newspaper-navigator/ (focused on images)