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FreeBSD Laptop Support Continues Improving with WiFi, GPU and Audio Driver Work
Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation
Stripe introduces a benchmark suite to evaluate whether AI agents can build real-world Stripe integrations across backend, frontend, and browser-based checkout workflows. The study examines end-to-end software engineering capability, focusing on execution, testing, and validation gaps in agentic systems under production-like constraints. By Leela Kumili
US charges Russian ‘bulletproof’ web hosts over cyberattacks that netted $62M from cybercrime victims
The 2024 indictment, now unsealed, accuses three Russians and two web hosts of aiding hackers and profiting from cybercrime.
Ignoring OS files (like .DS_Store) in Git inside dev containers
The well-calibrated Bayesian [pdf] (1982)
Hack Reveals Suno AI Music Generator Scraped YouTube, Deezer, and Genius
Towards a Harness That Can Do Anything
AI Lays Bare the Authoritarianism of Modern Work. Time to Rethink Education
A Tree and a Server Walk Into a Core...
This is about treesitter and LSP, specifically on their utility and recent journey into the Emacs core. Includes interactive visualizations of concrete syntax trees. submitted by /u/misterchiply [link] [留言]
Show HN: StyleSeed – a design-rules engine so AI agents stop building generic UI
Show HN: 18KB ls alternative in no_std rust and Libc
Tutorial: Algebraic Foundations Powering FlashAttention
Requisites change while you iterate: Lessons I learned from building a concurrent DevOps tool for automatically triggering GitHub workflows
I learned a lot of things by building a project from scratch: a backend DevOps tool that facilitates managing the fast update cycle in git dependencies. Overall, the system works by running two concurrent tasks: one to check dependencies, and the other to trigger workflows. When a dependency is updated, a workflow of the dependent GitHub repository is triggered. Initially, to pass data between tasks, I used an MPSC channel, but subsequently I transitioned to a database-backed queue because the channel was not resilient enough to crashes and network errors. In the article you can find more examples of unexpected quirks that I needed to iron out after the first iteration. submitted by /u/nilirad [link] [留言]
Show HN: Production-grade LangGraph template
Show HN: Fast NF4 dequantization Triton kernel (1.41x faster than bitsandbytes)
"I wanted to see if I could optimize the dequantization bottleneck during 4-bit LLM inference. By writing a custom kernel in Triton to optimize memory access patterns, I managed to get up to a 1.41x speedup over the standard bitsandbytes implementation. Check out the source code and benchmarks, feedback is highly appreciated!"
Ask HN: Is it just me, or is software buggier across the board?
I feel like I’m going crazy, I’ve been hitting the strangest bugs across all my devices in the last 6 months. Is anyone else experiencing this too?