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Feature
AI Coding Agents Produce More Code, But Stagnate Software Development
AI agents speed up coding but slow down the review process, raising efficiency questions.
Why it mattersAI coding agents produce more code at the cost of increased review times, highlighting a critical inefficiency in the software development lifecycle. As AI adoption grows, optimizing code review processes becomes crucial to realizing AI's potential benefits.
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Each morning: models, tools, research, and conversations distilled from across the web — with why they matter for builders.
Around the Web
AI as a Tool Debate
AI Agents in Action
Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. It launches with two core designs: - Elastic Scheduler: reallocates GPUs between rollout & training as demand shifts—live executi…
r/LocalLLaMA
Dynamic Workflow coming in clutch again, Anthropic fucking cooked by finally giving us a cheap model for this exact usecase. Not everything has to be Opus or Fable.
r/ClaudeCode
Tools
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right…
github | stars 32,560
Trending AI model on Hugging Face — image-text-to-text.
🤗huggingface
为 DeepSeek Harness (DSH) 插件生态打造的现代化桌面端解决方案。万物皆「插件」，桌面本身也是「插件」。
github | stars 30,573
Build
Why agentic payments require an executable model of organizational authority
thoughtworks
Research
In this paper, we study physically grounded articulated 3D asset generation---moving the goal from reproducing how objects look to capturing how they move and function. Inspired by the literature on procedural and agent…
arxiv
Deploying language-model agents onboard mobile robots requires task reasoning and responsive execution within limited compute and memory. We propose BT-Agent, an onboard architecture combining LLM-driven task decisions…
arxiv
Training long-horizon language model agents requires assigning credit to intermediate decisions from sparse terminal feedback. Within a finite group of sibling rollouts, useful action comparisons depend on their context…
arxiv
Playbooks
Your agents can now discover and load Agent Skills directly from a Model Context Protocol (MCP) server. Instead of shipping every skill inside your application or copying skill folders into each deployment, you point an…
devblogs.microsoft
Today, Shreya Shankar and I are publishing evals skills , a set of skills for AI product evals 1 . Eval tools often get in the way. They nudge you toward generic off-the-shelf metrics and fully automated evals before yo…
hamel
LangChain just released new capabilities in Managed Deep Agents. Agents can now schedule follow-ups, reconfigure themselves on every run, and react to Slack messages.
langchain
News
Learn how to run OpenAI GPT-6 Sol, Luna, and Astra on Amazon Bedrock through global cross-Region inference from Singapore, Jakarta, Thailand, Malaysia, and Taipei. This post covers the Converse and OpenAI-compatible API…
aws.amazon
Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Containers, with separate prefill and decode nodes behind one OpenAI-compatible endpoint. The pr…
aws.amazon
As developers increasingly delegate work to AI agents, these agents require broader access to systems and resources. They read files, run shell commands, execute generated code, and call APIs. That is what makes agents…
aws.amazon