Hacker News

https://xcancel.com/sama/status/2027578652477821175

https://fortune.com/2026/02/27/openai-in-talks-with-pentagon...

https://xcancel.com/secwar/status/2027507717469049070

https://www.cnbc.com/2026/02/27/trump-anthropic-ai-pentagon....

Unfortunately the backup of my documentation accidentally hadn’t run for a month. So I built claude-file-recovery, a CLI-tool and TUI that is able to extract your files from your ~/.claude session history and thankfully I was able to recover my files. It's able to extract any file that Claude Code ever read, edited or wrote. I hope you will never need it, but you can find it on my GitHub and pip. Note: It can recover an earlier version of a file at a certain point in time.

pip install claude-file-recovery

https://openai.com/index/scaling-ai-for-everyone/

https://x.com/sama/status/2027386252555919386

https://xcancel.com/sama/status/2027386252555919386

unf after I pasted a prompt into the wrong agent terminal and it overwrote hours of hand-edits across a handful of files. Git couldn't help because I hadn't finished/committed my in progress work. I wanted something that recorded every save automatically so I could rewind to any point in time. I wanted to make it difficult for an agent to permanently screw anything up, even with an errant rm -rf

unf is a background daemon that watches directories you choose (via CLI) and snapshots every text file on save. It stores file contents in an object store, tracks metadata in SQLite, and gives you a CLI to query and restore any version. The install includes a UI, as well to explore the history through time.

The tool skips binaries and respects `.gitignore` if one exists. The interface borrows from git so it should feel familiar: unf log, unf diff, unf restore.

I say "UN-EF" vs U.N.F, but that's for y'all to decide: I started by calling the project Unfucked and got unfucked.ai, which if you know me and the messes I get myself into, is a fitting purchase.

The CLI command is `unf` and the Tauri desktop app is titled "Unfudged" (kids safe name).

How it works: https://unfucked.ai/tech (summary below)

The daemon uses FSEvents on macOS and inotify on Linux. When a file changes, `unf` hashes the content with BLAKE3 and checks whether that hash already exists in the object store — if it does, it just records a new metadata entry pointing to the existing blob. If not, it writes the blob and records the entry. Each snapshot is a row in SQLite. Restores read the blob back from the object store and overwrite the file, after taking a safety snapshot of the current state first (so restoring is itself reversible).

There are two processes. The core daemon does the real work of managing FSEvents/inotify subscriptions across multiple watched directories and writing snapshots. A sentinel watchdog supervises it, kept alive and aligned by launchd on macOS and systemd on Linux. If the daemon crashes, the sentinel respawns it and reconciles any drift between what you asked to watch and what's actually being watched. It was hard to build the second daemon because it felt like conceding that the core wasn't solid enough, but I didn't want to ship a tool that demanded perfection to deliver on the product promise, so the sentinel is the safety net.

Fingers crossed, I haven’t seen it crash in over a week of personal usage on my Mac. But, I don't want to trigger "works for me" trauma.

The part I like most: On the UI, I enjoy viewing files through time. You can select a time section and filter your projects on a histogram of activity. That has been invaluable in seeing what the agent was doing.

On the CLI, the commands are composable. Everything outputs to stdout so you can pipe it into whatever you want. I use these regularly and AI agents are better with the tool than I am:

  # What did my config look like before we broke it?
  unf cat nginx.conf --at 1h | nginx -t -c /dev/stdin

  # Grep through a deleted file
  unf cat old-routes.rs --at 2d | grep "pub fn"

  # Count how many lines changed in the last 10 minutes
  unf diff --at 10m | grep '^[+-]' | wc -l

  # Feed the last hour of changes to an AI for review
  unf diff --at 1h | pbcopy

  # Compare two points in time with your own diff tool
  diff <(unf cat app.tsx --at 1h) <(unf cat app.tsx --at 5m)

  # Restore just the .rs files that changed in the last 5 minutes
  unf diff --at 5m --json | jq -r '.changes[].file' | grep '\.rs$' | xargs -I{} unf restore {} --at 5m

  # Watch for changes in real time
  watch -n5 'unf diff --at 30s'

What was new for me: I came to Rust in Nov. 2025 honestly because of HN enthusiasm and some FOMO. No regrets. I enjoy the language enough that I'm now working on custom clippy lints to enforce functional programming practices. This project was also my first Apple-notarized DMG, my first Homebrew tap, and my second Tauri app (first one I've shared).

Install & Usage:

  > brew install cyrusradfar/unf/unfudged

Then unf watch in a directory. unf help covers the details (or ask your agent to coach).

EDIT: Folks are asking for the source, if you're interested watch https://github.com/cyrusradfar/homebrew-unf -- I'll migrate there if you want it.

The Problem: Like many here, I love Manim's visual style. But setting it up locally is notoriously painful - it requires Python, FFmpeg, Cairo, and a full LaTeX distribution. It creates a massive barrier to entry, especially for students or people who just want to quickly visualize a concept.

The Solution: I wanted to make it zero-setup, so I ported the engine to TypeScript. Manim-Web runs entirely client-side in the browser. No Python, no servers, no install. It runs animations in real-time at 60fps.

How it works underneath: - Rendering: Uses Canvas API / WebGL (via Three.js for 3D scenes). - LaTeX: Rendered and animated via MathJax/KaTeX (no LaTeX install needed!). - API: I kept the API almost identical to the Python version (e.g., scene.play(new Transform(square, circle))), meaning existing Manim knowledge transfers over directly. - Reactivity: Updaters and ValueTrackers follow the exact same reactive pattern as the Python original.

Because it's web-native, the animations are now inherently interactive (objects can be draggable/clickable) and can be embedded directly into React/Vue apps, interactive textbooks, or blogs. I also included a py2ts converter to help migrate existing scripts.

Live Demo: https://maloyan.github.io/manim-web/examples GitHub: https://github.com/maloyan/manim-web

It's open-source (MIT). I'm still actively building out feature parity with the Python version, but core animations, geometry, plotting, and 3D orbiting are working great. I would love to hear your feedback, and I'll be hanging around to answer any technical questions about rendering math in the browser!