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All news · Today5 items

#Tutorials and practice

All collected and published items, ordered by time. Picks are a selected subset.
Today10/17Sat
  1. OpenAI Developers · X54

    How OpenAI engineers use Codex to tackle software maintenance

    OpenAI Developers shares how its engineers use Codex to explore their codebase, identify areas needing attention, and reduce the maintenance backlog. In the quoted post, @rrroim says Codex can now find dead-code candidates in Slack, check them against production telemetry, and draft cleanup PRs, whereas a few months ago it needed explicit directions on where to look. https://developers.openai.com/blog/maintaining-software-with-the-gpt-6-family

  2. AWS Machine Learning Blog60

    Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore

    AWS explains how its SMGS Operations team built a nine-stage evaluation framework with Amazon Bedrock AgentCore for an internal sales intelligence system. The framework combines domain-specific evaluators, versioned golden datasets, CI/CD gates, continuous monitoring, and trace-driven improvements across tools, agents, and multi-agent workflows. AWS reports that automated evaluation reduced quality regression detection from days or weeks to under four hours.

    Why it matters: The framework offers a transferable way to connect domain-specific failure categories with release gates and production monitoring across tools, individual agents, and multi-agent workflows.

10/16Fri
  1. AWS Machine Learning Blog73

    Introducing Reflection Beam, an open-weight model for agentic coding on AWS

    Reflection AI introduced Beam, an open-weight model for agentic coding and multi-step tasks, with 501 billion total parameters and 23 billion active parameters. Reflection reports an effective context length of 1 million tokens and releases Beam 1.0 under Apache 2.0 in BF16, FP8, and NVFP4 variants. The guide explains deployment through Amazon SageMaker JumpStart or HyperPod Inference with JumpStart, SGLang, or vLLM, including capacity planning, invocation, performance measurement, and cleanup.

    Why it matters: The deployment comparison clarifies when managed endpoints suffice and when Kubernetes infrastructure or custom runtimes offer useful control over serving configuration, distributed execution, and scaling.

  3. Hugging Face · X72

    Hugging Face announces livestream on how Reflection built Beam

    Hugging Face says a livestream with the Reflection team about how they built Beam starts in minutes. Stream: https://t.co/q6OKvCiB9o https://t.co/scNsrfsC3s In the quoted announcement, Reflection says Beam is available across platforms with open weights under Apache 2.0, in collaboration with 40+ partners, including a tokenizer, reference implementation, BF16, FP8 and NVFP4 weights, and a DFlash2 drafter.

    Why it matters: The listed artifacts and Apache 2.0 license provide concrete context for assessing how Beam could fit into an existing model deployment workflow.

  4. Simon Willison48

    Simon Willison’s Chirp Pong Experiment Works with Audio Networking

    Simon Willison says his experiment shows a two-player game can run on separate phones using static HTML, with game state sent through microphone-detected chirps. The game is a competitive Pong-like experience with graphical and gameplay twists. The source ends with “Claude Opus 5.5 Extra in Claude Code” but does not explain how that relates to the experiment.

  5. 量子位31

    QbitAI Think Tank Releases Report on Scaling Enterprise Agents

    The QbitAI Think Tank released a report, “Enterprise Intelligence Driven by Agents: From Improving Individual Tasks to Scaling Enterprise Transformation,” examining six obstacles to enterprise AI adoption: task definition, data reliability, verifiable decisions, execution, responsibility and permissions, and measuring and replicating value. It proposes a full-chain framework around business outcomes and recommends proving one high-value use case before expanding.

  6. 宝玉 · X63

    Cheng Lou releases Handwired to build neural networks from JavaScript program logic

    Cheng Lou released Handwired, an MIT-licensed project for manually expressing JavaScript program logic as neural networks with exact outputs, no training, and differentiable operations. Baoyu’s overview describes a browser desktop, document search, and a trainable photo layout demo. Handwired does not automatically convert existing JavaScript source code; developers must rebuild logic using its construction functions, declare value ranges, and work within 32-bit floating-point and execution constraints. https://chenglou.me/handwired/ https://github.com/chenglou/handwired

  8. AWS Machine Learning Blog60

    Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart

    Meta’s coding-optimized open-weight model Muse Spark Open is now available in Amazon SageMaker JumpStart in BF16 and NVFP4 variants, with a 1 million-token context window and 816B total parameters, including 42B active per token. AWS provides deployment instructions using SageMaker Studio or the SageMaker Python SDK, plus inference and cleanup examples. At launch, availability covers US East (N. Virginia) and US East (Ohio).

    Why it matters: The deployment walkthrough connects BF16 and NVFP4 memory requirements to supported instance types, giving teams a concrete basis for planning infrastructure when evaluating the model on their repositories.

  9. Gemini App · X49

    Plus up your furniture with 3D-printed accessories using Gemini 4 Argon. Upload a pic with rough measurements, and use Canvas in Gemini to build an interactive 3D CAD studio where you can explore styles, tweak live sliders, and export printable STLs to upgrade your space.

    Plus up your furniture with 3D-printed accessories using Gemini 4 Argon. Upload a pic with rough measurements, and use Canvas in Gemini to build an interactive 3D CAD studio where you can explore styles, tweak live sliders, and export printable STLs to upgrade your space.

  10. Google DeepMind49

    Google Antigravity Walkthrough Finds an Antimicrobial Peptide Candidate

    Google DeepMind describes how biologists used Google Antigravity to find a novel antimicrobial peptide candidate in *Streptococcus pseudopneumoniae*. ProtNLM helps scan millions of previously uncharacterized UniProt proteins, while the agent coordinates database searches, structural analysis, and literature synthesis. The candidate has predicted activity against pathogens involved in respiratory tract infections and is undergoing wet-lab validation.

10/15Thu
  2. AWS Machine Learning Blog64

    AWS explains Trust in Depth for giving agents autonomy within enforceable boundaries

    AWS’s Shawn Bice explains Trust in Depth, a framework for governing agents through four layers: Scope, Know, Bound, and Observe. He describes how AWS tools support policies, agent identities, external enforcement, and tracing, using employee offboarding to illustrate sequence-dependent safeguards. Automated reasoning can prove precisely stated rules within a system’s defined assumptions, while context-dependent expectations require evaluation and human judgment.

    Why it matters: The offboarding example offers a transferable method for translating business requirements into enforced prerequisites, showing how action order can matter as much as individual tool permissions.

  4. Kimi · X43

    Automate Your Workflows with Kimi Work - Tutorial #3 See how Kimi fits into your daily workflows: - Work across your local projects, browser, and computer. - Break down complex tasks using Swarm; iterate in Goal mode. - Customize how Kimi works with Plugins, AGENTS.md and Dashboard. What should we cover next? Let us know in the comments!

    Automate Your Workflows with Kimi Work - Tutorial #3 See how Kimi fits into your daily workflows: - Work across your local projects, browser, and computer. - Break down complex tasks using Swarm; iterate in Goal mode. - Customize how Kimi works with Plugins, AGENTS.md and Dashboard. What should we cover next? Let us know in the comments!

  6. Simon Willison45

    Claude Opus 5.5 Max Builds a Tree Spot-the-Difference Game

    Simon Willison describes prompting Claude Opus 5.5 Max in the default Claude iPhone app’s chat mode to build “Tree appreciator,” a three.js game about comparing algorithmically generated trees. Claude worked for 3 hours and 7 minutes before returning a spot-the-difference game with music, animation and achievements that Willison found compelling to play.

  7. 宝玉 · X30

    How BaoCut Maps English and Chinese Subtitles with Different Word Orders

    The author describes subtitle translation problems they encountered: subtitles appearing before a speaker talks, sentences split into fragments that do not match the spoken words, and multiple lines obscuring the video. The post introduces their open-source project, BaoCut, in the context of these challenges but does not explain how it handles them. https://t.co/WFPLrSceV7

  8. 宝玉 · X34

    How BaoCut Aligns English and Chinese in Bilingual Subtitles

    The author introduces an episode about subtitle translation challenges and how their open-source project BaoCut aligns English and Chinese when the languages use different word orders. They describe subtitles appearing before someone speaks, sentences split into pieces that do not match the spoken translation, and two or three lines covering half the screen. The post links to the project: https://t.co/c3VnQEt8x3

  9. 宝玉 · X67

    Claude Code adds idle compaction before prompt caches expire

    Baoyu says Claude Code now proactively runs /compact before the cache expires, helping avoid a spike in token costs on the first request after an hour away, and calls it useful 👍 https://t.co/4PT3UQ5kWD In the quoted post, @lydiahallie says idle compaction compresses long idle sessions just before the prompt cache expires (~1h), so returning users only need the summary reprocessed instead of the entire conversation, saving some usage. https://t.co/SG3QyTNFwr

  10. Aravind Srinivas · X56

    Perplexity explains how SPACE maintains execution identity across sandbox lifecycle changes

    Aravind Srinivas shared a write-up on how Perplexity’s sandbox platform handles millions of executions a day while using multiple machines to lower serving costs. https://t.co/lmRrwROweu In the quoted post, @perplexity_ai says SPACE has an error rate below 0.0002% and uses cryptographic identity to keep requests tied to the right execution as sandboxes pause, resume, and move. https://t.co/jH6TX2cgI9

  11. AWS Machine Learning Blog58

    Build self-improving document processing using Amazon Bedrock AgentCore

    AWS explains how to build a document processing system using Amazon Bedrock AgentCore and eight specialized agents that classify documents, extract data, validate results, and save refined extraction instructions for reuse. In its purchase order example, processing takes 3 minutes 49 seconds for an unfamiliar format and 1 minute 30 seconds for a subsequent document using stored instructions. The guide includes deployment steps and explains how to customize schemas, validation logic, and prompts.

  12. 宝玉 · X53

    How to ask Grok about its AI news curation workflow

    Grok’s AI news updates are pretty good, but you may want to use the approach in another bot or agent. A simple method is to ask how it works: When you compile news for me, how do you search X for the information I want? How do you judge whether it is new and valuable? The basic approach is simple too: follow specific accounts, grouped into three categories, starting with AI. https://t.co/FBLL9FvCPa

10/14Wed
  2. AWS Machine Learning Blog45

    Deploy and Evaluate Models with Amazon SageMaker JumpStart

    Amazon SageMaker JumpStart provides a workflow to deploy a pretrained model, run a repeatable performance test with the SageMaker Python SDK, and delete the endpoint afterward. The test measures latency, estimated output length, and single-request generation rate using sample prompts. Model Hub discovery and preconfigured endpoint options simplify browsing and deployment, while a deep link integration lets users deploy from Hugging Face model discovery in a single click.

  3. AWS Machine Learning Blog44

    How Tamnoon Built Tami for Cloud Security Remediation on Amazon Bedrock

    Tamnoon built Tami, an agentic cloud remediation orchestrator on Amazon Bedrock that investigates cloud security findings, assesses whether fixes are safe, and orchestrates remediation with guardrails and governance. The company reports an over 90 percent reduction in critical cloud exposure in 90 days, with zero production issues.

  4. AWS Machine Learning Blog47

    Generate presentations with Amazon Bedrock AgentCore

    AWS’s post explains how to build an AI agent system with Amazon Bedrock AgentCore to create client PowerPoint presentations from enterprise documents. In internal testing, the workflow completed in under 90 seconds; it uses a supervisor agent, RAG, and specialized tools for presentation generation and summarization. The post includes a public repository with code assets.

  7. 宝玉 · X81

    Why Anthropic built Scion to replace git for its monorepo

    Drawing on Anthropic’s engineering blog, Baoyu explains why the company replaced git with Scion as daily merged code changes grew from around 2,500 in May to more than 30,000 in September. Scion retrieves files on demand, moves operations to the server, and uses layered caches with hit rates above 99%. Anthropic reports that median agent workspace startup fell from 7.1 minutes to 5.1 seconds, while emphasizing that Scion serves exceptionally large monorepos and is not a general-purpose git replacement.

    Why it matters: The comparison illustrates how assuming continuous server access changes source control architecture, connecting on-demand file retrieval and server-side operations to the infrastructure demands of agent-generated code.

  8. Simon Willison47

    Profiling web-edit with Python 3.15 Reveals Line-Ending Workload Hotspots

    Profiling the unmodified web-edit app with Python 3.15’s built-in `profiling.sampling` found that preserving mixed line endings dominated the editing workload. `preserve_endings` appeared in 64.8% of 15,296 CPU stacks, while `SequenceMatcher.find_longest_match` contributed 38.3% of leaf samples; 95.1% of stacks came from AnyIO’s worker thread, making all-thread sampling essential.

  9. Simon Willison64

    Simon Willison updates llm-meta-ai 0.2 for Meta AI’s Muse Wonder

    Simon Willison updated his llm-meta-ai plugin to support Meta AI’s newly released Muse Wonder models. He shares pelican drawings across six reasoning levels, from minimal to max, and compares them with Muse 1.3 and Muse 1.2, reporting material drawing improvements between releases. He suggests these may be Meta’s first Fable class models and reports that Artificial Analysis scores Muse Wonder (Max) as equivalent to GPT-6.1 Sol.

  10. Simon Willison74

    Simon Willison adds Gemini 4 Argon support to llm-gemini 0.35

    Simon Willison added Gemini 4 Argon support to llm-gemini, following the model’s availability today through the Gemini API and a $100/month subscription plan. He praised its SVG pelican illustrations, with the high illustration costing 17.166 cents. Argon launches at $2/million input and $10/million output, with prices set to double on December 1st; Artificial Analysis reports $2,407 to run its benchmark, which Willison considers a more useful comparison than per-token pricing.

  11. 宝玉 · X76

    How Gemini 4 Argon Built a Japanese Temple Complex in Blender

    Baoyu describes a demonstration in which Gemini 4 Argon used Antigravity to build a Japanese temple complex in Blender from one prompt containing a detailed workflow. The prompt referenced a scene-building skill document and requested architectural plans, four Nano Banana reference images, and coordinated agents that checked renders against those references and iteratively revised the scene. The demonstrator requested animation and programmatically generated bell sounds and ambient audio the next day, making those later additions to the initial modeling workflow.

    Why it matters: The workflow offers a transferable approach to complex 3D generation by combining construction rules, visual references, agent coordination, and repeated comparison of rendered results.