A letter from our founders
Our founders on the age of intelligence: agents, open data infrastructure, and the humans building the agentic future.
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Amazon Bedrock tokenomics data lake
Running Amazon Bedrock across many accounts shows you the bill but not what happened inside each call. This post builds a centralized observability data lake that aggregates Bedrock invocation logs from every linked account into one queryable table, so you can see prompts, token counts, model choice, and per-invocation cost across your organization.
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AWS Machine Learning Blog
Autonomous treasury agents: How Trovata built an AI-native solution on Amazon Bedrock and AgentCore
Learn how Trovata built autonomous treasury agents on Amazon Bedrock and Amazon Bedrock AgentCore, using an RBAC-secured architecture for bank-branded AI deployment and agentic workflows that move corporate treasury from manual operations to real-time decision-making at enterprise scale.
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AWS Machine Learning Blog
b12249
The update introduces graceful failure handling for unreachable RPC endpoints and failed handshakes in ggml-rpc, replacing fatal aborts with reportable errors. Functions now return appropriate values (e.g., false, nullptr) on connection failures, allowing host applications to handle issues without crashing. A regression test was added for unreachable endpoints, and low-value comments were removed. Mid-stream failures remain unchanged to prevent silent corruption.
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llama.cpp releases
Encoder Disaggregation in vLLM: Scaling Multimodal Serving with EPD
Recent advances in vLLM encoder disaggregation: metadata-only multimodal requests, dedicated encoder execution, remote embedding transfer, cross-encoder caching, and lower-overhead EPD routing.
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vLLM · 官方技术博客
How TCS built an agentic test orchestrator with Amazon Bedrock AgentCore
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.
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AWS Machine Learning Blog
PegasusAI Studio: Legible Cyberinfrastructure and Accountable AI Agents for Scientific Workflows
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.
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arXiv cs.DC
Show HN: CodeLens AI-Augmented Code Review Tool
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
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Hacker News Show HN
Show HN：Notebook AI——适用于 Mac 的本地优先会议记录
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
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Hacker News Show HN
Show HN: Vaultbase – a self-hosted back end in a single binary
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
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Hacker News Show HN