Cryo chambers, mini pigs, and $26 billion are fueling his quest for immortality.
The study of bureaucracy
Play Historic Firsts, the daily history timeline game where you sort landmark inventions, discoveries, breakthroughs, and historic firsts from earliest to latest.
A markup language and transport format for HTML and XML fragments.
We have the pleasure of celebrating the birthday of Blaise Pascal by announcing the release of OCaml version 5.5.0. Some of the highlights in OCaml 5.5.0 are: Module-dependent Functions Modules can now be used as function arguments in a form of lightweight functors. For instance, we can define a function for printing a map generated by the Map.Make functor: let pp_map (module M: Map.S) pp_key pp_v ppf set = if M.is_empty set then Format.fprintf ppf "ø" else let pp_sep ppf () = Fo...
I'm a recent CS grad trying to break into ML engineering, and I just finished the first version of a side project I've been working on. Posting it here because I want people who know this space better than me to poke holes in it. The idea started from that feeling every dev has had, where you commit something and a second later your stomach drops because you think you just pushed an AWS key. Server side scanning catches that eventually, but only after it's already in your git history. Local tools like gitleaks run before that, which is the right idea, but they're pure pattern matching, so anything that isn't a known secret format slips through and they say nothing about whether the code itself is risky. Piping your diff to a cloud LLM just trades that problem for a different one. So I built a git hook that runs three checks on a commit, all on device. A fast Rust regex pass catches known secret formats and blocks the commit if it finds one. A small classifier running on the Neural Engine through CoreML catches riskier patterns that don't have a fixed string to match, things like shell=True in a subprocess call or disabled TLS checks, and also blocks if it fires. A small local LLM (Qwen2.5-Coder, 1.5B, running through MLX) reads the diff more like a human reviewer and flags things like injection risks or dead code, but it never blocks anything, just leaves notes. I kept it that way on purpose, since a false positive that blocks your commit kills trust fast, but a false positive that's just a comment costs nothing. Biggest weaknesses right now: it's Apple Silicon only since CoreML and MLX are both Apple specific, and the classifier is trained on a fairly small dataset so I wouldn't call it bulletproof yet. Repo's here if anyone wants to dig into the code: https://github.com/stalzkie/local-forge Mainly curious whether the three layer split makes sense to people who do this for a living, and what risky code patterns I might be missing for the classifier. submitted by /u/StalWrites [link] [Kommentare]
Benchmarks, context-window behavior, token economics, and the MCP wiring for running Claude Code and OpenAI Codex as a single coding pipeline.