Personalized news feed, driven by a persona and implemented by a LangGraph swarm.
Every Claude Code user has watched a new session start from zero. You open it on a system you built The post Claude Code found my app’s shared contract in two MCP calls. Then the records ran out. appeared first on The New Stack .
Anthropic’s new OSS Scanner, launched last week as part of its broader Cyber Mission, exposes a problem inside the company’s The post Claude found 29,000 possible bugs in open source. Only 516 have been fixed. appeared first on The New Stack .
GitLab has released version 19.4 of its DevSecOps platform, adding a goal-driven mode to GitLab Duo CLI, three hosted open-weight models, a larger set of Model Context Protocol (MCP) tools, and more detailed controls for AI spending. By Matt Saunders
An AI agent is writing a report and needs one data point. The API answers: this costs five cents. The...
CostGraph treats Kubernetes node rightsizing as vector bin packing, using geometric heuristics (Panigrahy et al. 2011) to pack pods sized from 7-day P95 usage across CPU, memory, pod count, GPUs, disk, and IP constraints. Pod requests are rightsized by dividing P95 by a utilization target (90% for stable workloads with CV<0.25, 60% for spiky with CV>=0.75, else 80%), and the greedy packing yields a floor on savings for consolidation.
David Silver, the architect of AlphaGo, discusses his new startup Ineffable which is developing an AI 'super learner' using reinforcement learning as an alternative to large language models. The interview covers his moral drive to advance AI and the potential of RL-based self-teaching machines.
Disclaimer This is an independent, personal write-up — not official AWS content, and...
Meta, MIT, and UW researchers introduced Context Language Models (CLMs) that let LLMs autonomously edit their own context like a file, learning to prune, preserve, and summarize. This intrinsic approach outperformed external orchestration (RAG, compaction), achieving 11.4% higher accuracy with 21.5% fewer FLOPs on BrowseComp-Plus and a 47.6% relative accuracy gain for Qwen3.5-9B with 12% less compute. The trade-off includes new safety risks from persistent prompt injections via the editable context window.
A passing eval for an AI agent doesn't validate its behavior under partial failure, like a database commit succeeding while the agent receives a timeout. This exposes a classic distributed systems gap where the agent cannot safely determine state, risking data duplication or missed work. The article argues that evals must be scoped to the specific production action and authority, and that tool outputs must be independently verified against external state, not just the agent's summary.
This is a submission for the Kaggle Benchmarking Challenge What I Benchmarked Every data...