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odels on quality, run at a fraction of the cost, and are fully customizable for your task. That combination has made them the foundation for teams building serious AI products and agents. But the strategic motivation for adopting open-weight models is still the ability to exercise control. Unlike their closed alternatives, open-weight models give teams control over performance, quality, and functionality. Many commercial AI applications have adopted a majority open-weight endpoint strategy so th

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Grok Build can now run workflows : describe a large task in plain language, and Grok plans it, fans it out across hundreds of parallel agents in the background, and reports back when everything is done. Your session stays free the whole time. When to use a workflow Workflows are built for complex, multi-faceted tasks a single conversation can't hold: reviewing every feature in a large PR, triaging the last 100 issues, auditing a codebase for one class of bug. If the work splits into many indepen

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The open research ecosystem has produced many great datasets for the three main agentic domains - software engineering, terminal use, and web research - but every one of them ships with its own harness, its own image conventions, its own grading scripts, and its own failure modes. 23 tasksets behind one taskset API We integrated them all. We ship first-party integrations for 23 agentic tasksets behind one taskset API, unified runtime/sandbox hooks, and one command: 198,000 software engineering t

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lled tools, everything. Resume it later and there's no re-cloning the repo or reinstalling dependencies. One command sets up your outpost. Standing up secure infrastructure for an agent to run on your own machines usually means real work: a queue watcher, credential handling, sandboxing, lifecycle management. uvx modal-devin init does all of it, and generates code you own and can keep customizing: a scheduler function (the orchestrator from above) and a session function (the worker): Each team g

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Today we're bringing Grok into Microsoft Outlook. The add-in puts an agent beside your inbox: it summarizes long threads, drafts replies in your voice, and keeps your mailbox organized. Legal came back on the renewal terms Five stories to start your day A lunar landing, a marathon record, and more We need sign-off from finance and legal All 1,204 checks green on main Three flows to review, deck attached Current rate holds if we commit by Jun 30 Accruals due to finance by Wednesday Q3 Vendor Cont

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t, Ryzen and data center GPUs with up to 2× faster with 70% less VRAM and no accuracy loss via our custom Triton kernels and math algorithms. We also support optimized ROCm builds for GGUF & Safetensors inference. Unsloth is an open-source local UI for faster LLM training and inference, with tool-call healing, code execution, secure web search, remote APIs, and HTTPS deployment. Connect local models to Claude Code, Codex agents and run the latest Kimi, GLM, DeepSeek, Qwen3.6, and Gemma 4 models.

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At Modal, we build sandboxes, among other things. Agents run in sandboxes, and agents are eating software. Today, Modal runs millions of sandboxes per day, supports up to fifty thousand concurrent sandboxes per customer, and supports a variety of use cases at scale, from reinforcement learning to background agents. Increasingly, our users require more and more sandboxes, created at higher and higher rates. Reinforcement learning can require running millions of sandboxes concurrently, and creatin

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keeps compute and memory flat as sequences grow. For endpoints on Modal, we took this even further with Day 0 support for a custom DFlash speculator tuned to this model shape. On agentic workloads on 8x B200s, we saw 250 tokens per second per user at 2.5M TPM of per-GPU throughput, 67% faster interactivity than the model's built-in speculative path at matched throughput. Towards local attention: Evolving DFlash speculation for frontier attention models Inkling’s unique architecture moves in a d

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ugh Observability, lineage and telemetry trace a production output back to the version of the asset behind it, and back to the usage that prompted the last change. The skills your agents run are reachable as MCP servers straight from Studio, so what executes in production is the same governed asset you versioned, not a copy that drifted. You define behaviour, watch it run, and improve it, all against one source of truth. That closed loop is the difference between cataloging your AI and governing

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, EMEA, and beyond, and a one-month minimum term. Inference spend has become a line item the board asks about. In a not too distant future every knowledge worker will have several agents working on their behalf, but companies are struggling to achieve this without astronomic inference budgets. To succeed companies need to consume a range of models that span the Pareto frontier, matching task difficulty with model quality (and cost). Open weight models occupy an ever-greater fraction of that fron

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You can now start new agents remotely from ampcode.com anywhere you can run amp: That means, in addition to running agents in orbs, you can now run agents on any machine you want: your laptop, your server, your cloud dev box, your Raspberry Pi. Your lawn mower even, if it has a shell. Enable it by using the command amp: enable remote creation of threads or with the setting: Once enabled, every Amp client you start will accept and run new threads in its working directory. Runner Mode You can also

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eir flow. There’s no context switching, no manual environment setup, and no permission troubleshooting. More from this author Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets 34 August 13, 2026 From the Hugging Face Hub to robot hardware with Strands Agents and LeRobot 17 June 17, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment

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Microsoft Foundry is a platform for building and operating agentic AI applications. Foundry starts with the widest model selection on any cloud — models from Microsoft, OpenAI, Anthropic, Meta, Mistral, DeepSeek, Hugging Face, and others, spanning frontier, open-source, and custom weights — all accessible through a single endpoint and a single set of SDKs in Python, C , JavaScript, and Java. On top of those models sits the Foundry Agent Service : multi-agent orchestration with built-in memory, k

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runtime, and ships remediation PRs. Security Swarm finds more verified vulnerabilities at 30% lower cost than the nearest comparable alternative. How it works A swarm of parallel agents investigate segments of the codebase. Each agent reasons across files—catching business logic flaws, chained auth bypasses, and cross-service exploit paths. Devin then composes individual findings into full attack paths and reproduces each one in an isolated sandbox to confirm exploitability at runtime. What rea

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Internally, a significant amount of engineering time is consumed by manually verifying that the code an agent writes works E2E in dev. We sought to change that. We built an internal agent, the Verifier, to take over that step. On every run it deploys the change to a live environment, exercises the affected behavior, and posts what it observed (logs, API responses, and screenshots) back to the PR. It runs on Cosmos, our cloud-agent platform. Why not write more tests? We weighed a maintained suite

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You can now launch Amp agents remotely in orbs: Orbs are machines where agents can run without supervision. When you create a new thread you get a fresh orb that contains your code, plugins, and tools the agent might need. Orbs are billed by the minute. See the current orb sizes and pricing. They start up quick and go to sleep when you and the agent no longer need them. Remote and Yet So Near Agents in orbs aren't running on your machine, but you can control them just like they were. You can rev

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dynamic mid-session routing. We welcome you to try Devin Fusion in preview at app.devin.ai/signup. The Trick: Sidekick The key idea behind our architecture is to run two parallel agents: one with a frontier model, the other with a more cost-effective "sidekick" model. Both are fully capable agents with their own toolsets and ability to gather & act on their own context. As the task progresses, the main agent decides which tasks to give the sidekick and which tasks to do itself. Making sidekick

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June 29, 2026 Gemma 4 is now significantly faster in Ollama 0.31. On Apple Silicon, it generates tokens nearly 90% faster on average across a coding-agent benchmark. The speedup is on by default, and it does not change the model’s output: The speedup comes from multi-token prediction (MTP). Gemma 4 ships with a small, fast draft model that runs alongside the main model and proposes the next several tokens. The main model then verifies that proposal in a single pass and keeps the tokens it agrees

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omponents, your variables, and your team—so your designs can come to life from day one.Inside Figma; Product updates; Figma Design; Motion; 3D design; Config; News; Figma’s design agent, now with custom tools and greater contextJune 24, 2026By Georgia Rust, Rodrigo DaviesPrompting gets you started. Custom tools, real context, and skills take the design agent further—so what ends up on the canvas actually looks like you.Inside Figma; Product updates; Figma agent; Figma Design; Config; AI; News; C

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Prompting gets you started. Custom tools, real context, and skills take the design agent further—so what ends up on the canvas actually looks like you. Share Figma’s design agent, now with custom tools and greater context Explore our playground files for the agent and custom tools. The difference between an agent that helps you work more efficiently and one that actually understands how you work is context. When an agent knows your team's way of working it can do more than produce. It can collab

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