Tata Consultancy Services (TCS) built an agentic test orchestrator on Amazon Bedrock AgentCore that turns requirements into executable, governed tests for its Banking, Financial Services, and Insurance (BFSI) clients. Learn how specialized agents, per-tenant isolation, and workload-aware execution cut testing effort by 88% in an internal benchmark.
arXiv:2611.13309v1 Announce Type: new Abstract: AI coding agents can now construct scientific workflows, but operating them on shared cyberinfrastructure (CI) raises problems that code generation does not solve. Agents need current knowledge of the workflow system and its execution sites and access to the runtime state of distributed executions, while facilities need to attribute every agent action to a user and bound it by that user's privileges. We present PegasusAI Studio, an AI-assisted science gateway built on the Pegasus and HTCondor stack. Studio makes CI legible to agents by projecting one facility-curated knowledge base into the native configuration of several coding agents, by introspecting workflow interfaces and execution sites, and by grounding diagnosis in workflow and scheduler telemetry. Agent actions operate within a trust envelope that combines federated authentication, per-user execution environments and scheduler credentials, provider-neutral model access, observability, and approval controls for state-changing actions. The same application runs as a JupyterHub-based gateway on a research cloud and as an Open OnDemand application on HPC systems. We evaluate Studio through case studies of legacy CASA weather workflow modernization, a gravitational-wave reanalysis, an EEG analysis built with an open-weight model, and an Event Horizon Telescope (EHT) M87 image reconstruction that recovered from two failed runs to a zero-failure execution. The evaluation establishes that these mechanisms operate as designed.
As AI writes more code, the bottleneck has become human-driven code reviews. Code Lens serves to aid that task by augmenting the review outside of line-level AI feedback. Check it out and let me know what you think. Thanks.Try it on a public PR: npx @blong8334/codelens pr https://github.com/expressjs/express/pull/7574 Comments URL: https://news.ycombinator.com/item?id=50446651 Points: 1 # Comments: 0
文章网址：https://github.com/sridharchaildn/notebook-ai 评论网址：https://news.ycombinator.com/item?id=50446588 积分：1 # 评论：0
One binary to run and you have a full backend at your fingertips: a database with a REST API, sign-in, realtime, files, server-side hooks and jobs, on SQLite or Postgres. It's MIT licensed. You can try the demo at https://demo.vaultbase.sh (the login is filled in, and it resets every hour). I built it to ship fast. I always tend to write the same logic for any new project, whether it's a side project or a small to medium project for a client. I wanted something my friends and I could work with fast, try new things with, and that's easy to use. Then it became something I really wanted to work on and make as complete as I can for any type of backend. As of writing this post it has 80 releases since May this year and is backed by more than 2,800 tests. I also run security reviews with Claude and fix what they find. It hasn't had an outside audit yet. It was built with the help of Claude, from the codebase to the documentation and fact-checking it against the code. Claude helped speed up the pace, find bugs, and write tests. Vaultbase is pre-1.0, so expect some bugs and issues here and there. I'm working hard to fix each and every one of them. Thank you for taking the time to read this. I'd really appreciate any feedback, even if you didn't like Vaultbase. The demo is yours to try, and it's two commands to run locally. Comments URL: https://news.ycombinator.com/item?id=50448562 Points: 1 # Comments: 0
CrossnoKaye brought physical AI to heavy industry with Amazon Bedrock AgentCore. Its ATLAS platform and AURA agentic system ground AI reasoning in live plant data and run verifiable Python in isolated AgentCore sandboxes, giving plant engineers checkable, math-backed answers instead of model guesses.
Repeatable tasks like status updates, release notes, and ticket creation can be encoded in AWS Quick through agents and skills. This post covers how agents, skills, and your data fit together in AWS Quick, how to build and share skills for your team, and how the official catalog and custom agents help you use them.
LangSmith Bring Your Own Cloud is now generally available on AWS, giving Enterprise teams managed observability, evaluation, and deployment inside their own VPC.
arXiv:2611.10594v1 Announce Type: new Abstract: Multi-scene video generation must follow scene-specific prompts while preserving consistency across scenes. Dense joint generation enables cross-scene information exchange but incurs quadratic attention cost, whereas separate generation avoids this cost at the expense of cross-scene consistency. We present OneReel, a training-free system that exploits scene structure to enable efficient multi-scene generation. Our key insight is that multi-scene generation can preserve complete intra-scene attention while restricting cross-scene information exchange to a bounded shared memory. OneReel uses scene prompts and durations to establish scene-specific attention scopes and dynamically selects a bounded set of representative latent frames for cross-scene sharing. This reduces video attention complexity from quadratic to linear in the number of scenes. To translate these savings into execution speedups, OneReel packs scene-local and shared K/V into dense variable-length attention and employs work-balanced multi-GPU partitioning with communication-aware selection between K/V gathering and Ulysses-style parallelism. Our evaluations across four video generation models show that OneReel achieves 1.7-4.9x end-to-end speedup over dense attention and 1.2-2.2x over existing sparse-attention advances, while improving text-following accuracy by 34-37 percentage points and maintaining cross-scene consistency.
一种七层可组合参考架构，用于在 Amazon Bedrock AgentCore 上将企业智能体 AI 从原型推向生产。每一层都有稳定的契约，因此您可以在任意一层替换技术，而无需重新设计其余各层。
