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#Agent

Today 3 items
Oct 10Sat
  1. AWS Machine Learning Blog65

    How Postman runs Agent Mode for 40 million developers on Amazon Bedrock

    Postman and AWS detail the production architecture of its Agent Mode, an AI-native capability covering API testing, documentation, discovery, and implementation, serving 40 million developers.

    Why it matters: The piece explains Postman's concrete practices in production—controlling tool scale, using schema reads instead of piling up tools, and treating context as the bottleneck—all transferable to other agent engineering.

Oct 9Fri
  1. The Decoder55

    Anthropic's Claude Science creates the first complete ultraviolet map of the sky

    Anthropic's Claude Science has for the first time produced an ultraviolet map covering the entire sky, with astronomer Brice Ménard describing the process on Anthropic's website. The AI agent downloaded data from multiple space missions, calibrated and merged it, and used inpainting to fill in missing regions; in tests, predictions differed from actual measurements by about 10% on average. NASA's GALEX previously covered only about two-thirds of the sky and skipped bright star-forming regions, and the new map will be used as teaching material.

  2. Hugging Face Blog72

    The model that didn't exist, so you made it yourself

    Because the Hub only had a compressed version of the official 9B prompt rewriter for Qwen-Image 2.1, the author used HuggingChat's ML-intern mode to build a 0.8B student version that runs on CPU.

    Why it matters: The author shares seven complete projects offering a reusable workflow covering prompt structure, budget control, baselines and smoke tests, with all prompts and itemized compute costs included.

  3. Latent Space53

    Synthesis Superintelligence: from Semiconductors to Superconductors — Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk

    The Latent Space podcast interviews Periodic Labs co-founders Liam Fedus and Ekin Dogus Cubuk about their "synthesis superintelligence" vision: building reinforcement learning environments around physical labs where AI agents learn through an end-to-end cycle of predicting, synthesizing and characterizing materials, rather than relying only on internet data.

  4. TechCrunch · AI53

    Goodfire says its new ‘inside-out’ monitors catch rogue AI agents at a fraction of the cost

    Interpretability startup Goodfire has launched a monitor that reads signals inside the model rather than having an external AI re-read outputs, now available to Baseten customers. In tests on Kimi K3, monitoring about 1,500 sessions cost roughly $51, far below $233 for step-by-step checks by cheap AI models and about $10,000 for top models; the probes caught 94% of malicious intrusion sessions with an 8.7% false-positive rate on harmless sessions, and running four probes in parallel added less than 2% response latency.

Oct 8Thu
  1. Latent Space83

    [AINews] Claude Haiku 5.5 — better than GPT-6 Luna at the same pricing

    Anthropic released Claude Haiku 5.5, the first Haiku-tier update in about a year, scoring 43 on the Artificial Analysis Intelligence Index, surpassing GPT-6 Luna (38) and GLM-5.3 Flash (42).

    Why it matters: The piece compiles third-party benchmarks and actual pricing details, letting readers weigh the real cost-effectiveness of small models in low-cost agent workflows.

  2. Google Research72

    Does better work always mean better workers?

    Google Research published an NBER paper reporting a three-month randomized field experiment with 133 lawyers at 11 IP firms, giving two-thirds of the lawyers early access to the then-unreleased Google Labs AI patent drafting tool (now part of Gemini Notebook).

    Why it matters: The three-month randomized experiment separates AI-assisted from unassisted performance, and readers can see the transferable finding that junior lawyers' skills did not grow along with output.

  3. The Decoder76

    Claude Haiku 5.5 arrives with massive price cuts proving the AI pricing arms race is far from over

    Anthropic released Claude Haiku 5.5, cutting average prices by about 75% versus Haiku 4.5, with discounts of up to 90% for prompts under 100,000 tokens, and for the first time offering adjustable reasoning levels in a Haiku-tier model.

    Why it matters: The article compiles Haiku 5.5's price cuts, key benchmark data and adjustable reasoning levels, allowing readers to compare its positioning against GPT-6 Luna and Sonnet 5.5.

  4. AWS Machine Learning Blog66

    Introducing Claude Haiku 5.5 on AWS

    Anthropic's Claude Haiku 5.5 is now available on Amazon Bedrock and Claude Platform on AWS.

    Why it matters: The article covers Haiku 5.5's positioning, pricing changes, and how it splits duties with Opus 5.5, plus code for accessing it on Bedrock, so readers can start evaluating it right away.

  5. NVIDIA Blog72

    NVIDIA, Microsoft Kick Off a New Beginning for Windows PCs With RTX Spark and AI Agents

    NVIDIA and Microsoft announced at the Windows AI and Surface event in San Francisco that they are jointly building agent software and hardware for Windows PCs.

    Why it matters: The article details the specs and launch timeline for RTX Spark and DGX Station for Windows, helping readers assess how realistic running large models locally with resident agents will be.

  6. GitHub Blog · AI & ML54

    Secret protection must scale with software

    GitHub released a fine-tuned ModernBERT classifier for push protection that evaluates candidate secrets in context in under 2 milliseconds per batch, potentially more than doubling the number of secrets it can block; the feature is currently in private preview.

  7. Microsoft Research67

    Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

    Microsoft Research Asia proposed the Harnessed Agentic RL training paradigm and open-sourced a rebuilt Agent Lightning v1.0, a roughly 3,500-line framework that lets the real agent harness used in deployment participate directly in reinforcement learning via an LLM proxy, with no need to reimplement the agent inside the training framework.

    Why it matters: The article explains the design ideas, architecture components, and reproducible training results of Harnessed Agentic RL, letting readers assess its fit for existing agent training workflows.