GitHub's biggest debates right now? Whether to shut down AI-generated "noise" fromCopilot—stuff like auto-written issues and code reviews. No clear answers from GitHub yet. Frustration is piling up. Some devs are ditching the platform altogether, shifting their projects toCodebergor spinning upself-.. read more  

In the “Month of AI Bugs,” researchers poked deep and found prompt injection holes bad enough to run **arbitrary code** on major AI coding tools—**GitHub Copilot**, **Amazon Q**, and **AWS Kiro** all flinched. They didn’t stop at theory. They built **AgentHopper**, a proof-of-concept AI virus that .. read more  

Agent engineering with **small language models (SLMs)**—anywhere from 270M to 32B parameters—calls for a different playbook. Think tight prompts, offloaded logic, clean I/O, and systems that don’t fall apart when things go sideways. The newer stack—**GGUF** + **llama.cpp**—lets these agents run loc.. read more  

The new **Model Context Protocol (MCP) Registry** just dropped in preview. It’s a public, centralized hub for finding and sharing MCP servers—think phonebook, but for AI context APIs. It handles public and private subregistries, publishes OpenAPI specs so tooling can play nice, and bakes in communit.. read more  

LinkedIn tore down its GenAI stack and rebuilt it for scale—with agents, not monoliths. The new setup leans on distributed, gRPC-powered systems. Central skill registry? Check. Message-driven orchestration? Yep. It’s all about pluggable parts that play nice together. They added sync and async modes.. read more  

Booking.com built Judge-LLM, a framework where strong LLMs evaluate other models against a carefully curated golden dataset. Clear metric definitions, rigorous annotation, and iterative prompt engineering make evaluations more scalable and consistent than relying solely on humans. **The takeaway**:.. read more  

Fastly says95% of developersspend extra time fixing AI-written code. Senior engineers take the brunt. That overhead has even spawned a new gig: “vibe code cleanup specialist.” (Yes, seriously.) As teams lean harder on AI tools, reliability and security start to slide—unless someone steps in. The re.. read more  

LLMs generate text by predicting the next word using attention to capture context and MLP layers to store learned patterns. Mechanistic interpretability shows these models build circuits of attention and features, and tools like sparse autoencoders and attribution graphs help unpack superposition, r.. read more  

A new static verification framework wants to make runtime safeguards look lazy. It slaps **mathematical safety proofs** onto LLM-generated workflows *before* they run—no more crossing fingers at execution time. The setup decouples **code from data**, then runs checks with tools like **CodeQL** and .. read more  

A barebonestime-sharing OS kernel, written inZig, running onRISC-V. It leans onOpenSBIfor console I/O and timer interrupts. Threads? Statically allocated, each running inuser mode (U-mode). The kernel stays insupervisor mode (S-mode), where it catchessystem callsandcontext switchesvia timer ticks. .. read more