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Hacker Public Radio
AI-generated, Lean-verified proof of Collatz conjecture exploits Lean kernel bug
Try sending "see the below –" to Opus 5
List of 6502 based computers and 6502 history
Git worktrees are not an isolation boundary for coding agents
Why DNA damage from smoking and UV rays cause cancer in some but not others
China begins producing advanced chipmaking deep-ultraviolet lithography machines
The Lost Civic Life of Movie Rental Stores
The D-Day in Color. Rare WWII Historical Footage Restored [video]
Boarding China's Last Bus
Europe's fires are just the start
Show HN: Hacker Fables – A satirical cyberpunk novel you can read as a man page
The project is entirely open source. It takes a single Pandoc Markdown source file and produces HTML, EPUB, PDF, man pages, GNU Info, an audiobook, an HTML-integrated audio player that highlights the content as it plays, and an MP4 output for the "video audiobook." It's generic enough to be reusable. I plan to use the pipeline for my tech blog later on. The novella contains a lot of programming lore. It's an over-the-top love letter to the old-school Linux programmer and to the craft itself. Sou
I wanted to run my own AI. My laptop says not yet
Adversarial Code Obfuscation for Defending Against LLM-Based Analysis
Elena, a library for building Progressive Web Components
Great Barrier Reef at risk of 'collapse', scientists warn
Launch HN: Prized (YC S26) – Let non-engineer staff build secure internal tools
Hi HN, we're Marinos and Hudson, founders of Prized ( https://prized.dev )! Prized lets non-engineer employees describe the internal tool they need and get a full-stack app, wired to their company’s data and deployed behind the company’s sign-in, without them ever juggling API keys or connectors. Here's a demo: https://www.youtube.com/watch?v=730MuYOfZTY The way Prized provides security is by limiting what the agent can reach at the network layer and by keeping credentials out of the sandbox ent
Show HN: Local text, image, video, music and 3D from one CLI, no Python
Hi HN! I'm the author of mere.run a local first inference runtime built around an installable CLI. I believe that whenever possible we should use the stuff we already own (like our Mac laptops, decent machines gathering dust, our gaming PC) and the limited electrical power we have easy access to, like the socket in the wall next to most of us. We shouldn't have to send our data to the cloud hoping some T&C will prevent it from being used in a way that we'd regret. Most of the local AI solutions