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stack, context-rich apps that combine live Databricks warehouse data with information created and updated through the app. When an app built with Replit is ready to deploy, Replit Agent automatically provisions its Lakebase database, eliminating the need for manual setup. Automated Preview Deploys: Test your apps before they go live. Replit automatically creates a separate preview environment that keeps test data isolated from live business data in Databricks, giving teams a safe place to build,

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through Bedrock’s secure and reliable inference engine. Leading Performance in Enterprise Grok 4.3 is one of the strongest models available today for building enterprise-ready AI agents: 1 on Artificial Analysis Omniscience benchmark , achieving the lowest hallucination rate among frontier models. 1 on Artificial Analysis Tau2 Telecom benchmark , which evaluates real-world tool-calling performance in customer support agent scenarios. 1 on Vals AI Case Law and Corporate Finance benchmarks, which

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A framework for verifiable analysis of AI behavior Developing an AI agent is a complex data analysis problem. To know if the agent is working correctly, we need to track not just benchmark scores but the details of its behavior: how do the strategies change over the course of training? Why does the new scaffold perform worse? Is there reward hacking? Answering these questions requires a combination of quantitative and qualitative analysis tailored to the dataset at hand. Coding agents have the p

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をご覧ください。 開発の背景：研究と実装の統合 Sakana Marlinは、Sakana AIがこれまで蓄積してきた研究知見と実装経験を統合して開発したプロダクトです。 S研究領域では、科学的発見のプロセスを自動化する「AI Scientist」、複数のモデルを協調させて推論能力を高める「AB-MCTS」、アルゴリズムエンジニアリングを自動化する「ALE-Agent」などを発表してきました。同時に、国内の各産業へのAIエージェント実装をはじめとする実務適用を通じて、高度なワークフローをエージェントが自律的に実行する仕組みの構築を進めてきました。これらの長期推論・複数モデルの最適制御技術が、Sakana Marlinに結実しました。技術的な詳細はベータリリース時のブログでご覧いただけます。 【Nature誌掲載】アイデア創出から査読までの研究サイクルを自律完遂する「AIサイエンティスト」。この最先端の知見が、Marlinの高度なリサーチ能力を支えています。（Credit: Artwork by CERTO, Inc.） 約300名のβテスターとの協働 Sakana Marlinは、20

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ons are now fused into single Metal kernels via MLX’s just-in-time compiler features, and we’ve reworked Ollama’s GPU-backed sampling to run more efficiently. More responsive with agent workflows Agent workloads are dominated by prompt processing. Every tool call is a new request, and every request resends the whole transcript: system prompt, tool definitions, and every file read so far. Over a single task the model ends up processing the same context dozens of times. Prefix caching avoids the r

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MiMo Code is a terminal-based coding agent built by Xiaomi's MiMo team on top of OpenCode and open-sourced under the MIT license. It is designed for long-horizon automated programming tasks, with a core focus on how to maintain decision quality and state continuity over dozens or even hundreds of execution steps. This article introduces the core technical design of MiMo Code through three themes: computation, memory, and evolution. 1. Design Motivation The basic structure of a coding agent is to

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t announcements Build plugins for Claude Build plugins for Claude Build plugins for Claude Build plugins for Claude Sep 23, 2026 Claude Marketplace: one place to discover plugins, agents, and services from our partners Product announcements Claude Marketplace: one place to discover plugins, agents, and services from our partners Claude Marketplace: one place to discover plugins, agents, and services from our partners Claude Marketplace: one place to discover plugins, agents, and services from ou

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ion will fund your usage up to $10M until it does. We're calling it the AI Productivity Guarantee, and we hope other AI companies will move in a similar direction. How it works An agent reviews each completed Devin session and estimates two things: Did this session result in useful output?; If so: how long would a human engineer have taken to produce the same work? We measure in hours of productive output because lines of code don’t correspond to effort: a critical bug that takes hours to invest

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AI has moved from models to agents that go beyond solving individual problems and instead can execute on full broad scope projects through tool calls, sub-agent delegation, and iterations over long horizons. NVIDIA's Nemotron family is built for this paradigm. Nemotron 3 Ultra — a 550B-parameter Mixture-of-Experts model with 55B active parameters — is built for frontier reasoning and orchestration, with Nemotron 3.5 ASR for voice and Nemotron 3.5 Content Safety for guardrailing. Ultra sits at th

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Amp has a new UI and sidebar, built for watching and driving all of your Amp agents, on web, mobile, and CLI. We rebuilt Amp's foundation last month, so that your agents can run for longer, with less handholding needed. Amp is now a distributed system with durable execution for the agent loop and a plugin API. Now, with this new foundation in place, we're pulling ourselves back above ground to shape how you use Amp to get the most out of these models. We want you to be happy and productive runni

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AI AND ML Always-on agent promises to keep work moving, provided you trust it with practically everything Move over, Copilot: Microsoft is introducing a new category of agentic AI called "Autopilot," starting with Scout, its first agent. And it doesn't take much guessing to understand how Microsoft expects these things to operate: By constantly watching your every move and taking action in the background to ostensibly streamline your workday. Microsoft announced Autopilot, and the first Autopilo

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Claude Code can now write and orchestrate its own multi-agent harness on the fly. Here's how dynamic workflows work, and the patterns that get the most out of them. Last week, we released dynamic workflows in Claude Code. Claude can now write its own harness on the fly, custom-built for the task at hand. While the default Claude Code harness is built for coding, it is also useful for many other types of tasks because, as it turns out, many tasks resemble coding tasks. But there are certain class

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istral unveiled a new AI stack for industrial engineering, partnering with Airbus, BMW, and ASML to optimize design, simulation, and production while ensuring data security. Their agentic AI tool, Vibe, now handles long-horizon tasks like coding and research, while a new 10 MW data center in France will enhance compute control and security by 2026. These advancements reinforce Mistral’s focus on secure, high-impact AI solutions for critical enterprise workflows. Mistral for Industrial Engineerin

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Vibe is Mistral’s unified AI agent for both work and coding, capable of handling long-running tasks like inbox management, research, and document drafting, as well as coding workflows from feature development to pull requests. It offers Work Mode for multi-step tasks across enterprise tools and Code Mode for remote coding sessions, with integrations like VS Code and CLI for seamless collaboration. Available in free, Pro, Team, and Enterprise plans, Vibe streamlines workflows with visible reasoni

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ground, or production code. Working in code today feels a lot like design did 10 years ago. When Figma launched, design was offline and single-player. Today, more people are using agents to write code than ever and coding itself is changing rapidly. The quality of code output from frontier models is getting better all the time, and yet, despite so much innovation, the tools we have to work with code are very early. We're still stuck in 2016 when it comes to collaboration. IDEs and terminals just

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er from web search. Teams that need domain-tuned retrieval often end up building custom retrieval infrastructure from scratch, which is expensive to maintain and hard to evaluate. Agents working on enterprise tasks need access to enterprise context. They make retrieval decisions autonomously and at high volume, so the quality of the search infrastructure underneath them directly affects every downstream step. For searching across large document corpora, agents perform semantic search on an index

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obsessed over making the core workflow feel fast, fluid, and enjoyable to use. Code review has stayed painfully slow while everything else sped up. Growing PR volumes from coding agents are putting further strain on an already creaking process. So much so that the speed gained from using agents gets swallowed in review. Diffs is designed to keep code review fast without sacrificing the rigour of the work. We approached Diffs with a short list of beliefs about what review should be: Fast. Review

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May 28, 2026 OpenJarvis is an open-source framework for building personal AI agents that run on your own hardware. It’s built by Stanford’s Hazy Research and Scaling Intelligence labs, as part of their “Intelligence Per Watt” research into efficient local AI. Local models can already handle most day-to-day chat and reasoning, yet most personal AI still sends every request to the cloud. OpenJarvis makes local-first the default. Models run locally, and the cloud is optional. Energy, cost, and late

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recent deploy cause this?; Who owns the affected service?; Is this a known issue?; What should we do next? Most of this work is repetitive investigative work. Our goal is to have agents drive the repetitive investigation of incident management while pulling humans in primarily for judgment, prioritization, and remediation decisions. This article covers how we use the Augment Cosmos platform to automate large parts of incident response directly inside Slack, resulting in an 81% reduction in huma

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in can now operate natively in a Windows environment, bringing the full power of autonomous AI engineering to the world's most mature developer ecosystem. The benefits of cloud AI agents - task delegation, parallel execution, and autonomous iteration - now apply to Windows applications. Watch Devin build a new feature in a .NET Framework app. Then, Devin migrates the app to .NET Core - building and testing in an actual Windows environment. Every enterprise that runs on Windows has code that pred

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