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We built the world's most advanced program reverse-engineering system. It's model-independent and demonstrated improved performance for every frontier model we tested on long-horizon software tasks by giving agents an independent definition of done. New Factory research:

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Wave 9 is here: a frontier model built for software engineering. Introducing our new family of models: SWE-1, SWE-1-lite, and SWE-1-mini. Based on internal evals, it has performance nearing that of frontier models from the foundation labs. Available now, only in Windsurf!
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Wave 9 is here: a frontier model built for software engineering. Introducing our new family of models: SWE-1, SWE-1-lite, and SWE-1-mini. Based on internal evals, it has performance nearing that of frontier models from the foundation labs. Available now, only in Windsurf!

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Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

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1/4Introducing Qwen3-Coder-Next: Our latest open-weights model designed specifically to power the next generation of autonomous Coding Agents. Built on Qwen3-Next, this model is engineered to handle complex, long-horizon programming tasks with unprecedented efficiency. High-performance agentic intelligence is now in your hands.

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🎉 Day-0 support for in vLLM, available today in v0.23.0! Congrats to Z.ai on GLM-5.2, a flagship model built for long-horizon coding agents. ✨ 1M-token context, built to hold project-scale engineering work in a single run ✨ Tuned for long-horizon coding: large-scale implementation, automated research, and performance optimization ✨ One task can carry a full dev workflow, from requirements to a deployable product across platforms ✨ Client-side and mobile engineering, including an on-device debugging loop Try it out running it on vLLM today: 🔗
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🎉 Day-0 support for in vLLM, available today in v0.23.0! Congrats to Z.ai on GLM-5.2, a flagship model built for long-horizon coding agents. ✨ 1M-token context, built to hold project-scale engineering work in a single run ✨ Tuned for long-horizon coding: large-scale implementation, automated research, and performance optimization ✨ One task can carry a full dev workflow, from requirements to a deployable product across platforms ✨ Client-side and mobile engineering, including an on-device debugging loop Try it out running it on vLLM today: 🔗

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Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering
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Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering

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$MSFT just introduced Critique which is a new multi-model deep research system inside M365 Copilot that lets users combine models to generate stronger reports & responses. Microsoft is increasingly positioning itself as the interface layer for enterprise AI by monetizing software subscriptions, Azure usage & inference demand without needing to win every frontier model race itself.

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Introducing the supermemory CLI. MCP isn't dead - but CLIs can be more powerful. So we built the most advanced CLI for agents. EVERY single thing you can do on the supermemory platform can be done by Agents. just prompt them. They are first class users. > npx supermemory

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New release from Meta FAIR — Meta Motivo is a first-of-its-kind behavioral foundation model for controlling virtual physics-based humanoid agents for a wide range of complex whole-body tasks. The model is capable of expressing human-like behaviors and achieves performance competitive with task-specific methods and outperforms state-of-the-art unsupervised RL and model-based baselines. Try the demo ➡️ Get the model and code ➡️ We’re excited about how this research could pave the way for fully embodied agents, leading to more lifelike NPCs, democratization of character animation and new types of immersive experiences.

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Progress in open models is keeping Big AI labs up at night, and I'm here for it! We have a brand new open-weight multimodal model optimized for long-horizon tasks. This model is really good at something: it can work on tasks that keep evolving over time. • 280B total parameters, but only 16B active • 512K context window • Understands text, images, and audio • Strong reasoning, coding, and tool use But the best of all: the model learns and adapts to new information! Imagine you start running an agent today to solve a problem, and while it's working, you get new information that changes the initial conditions, or you change your mind. The agents you run today don't have issues with short tasks and goals that don't change, but reality is messy, and that makes it hard for long-horizon agents to succeed. The new dots3-note Preview model introduces TEMPO. TEMPO is a new reinforcement learning technique that lets the model periodically pause and critique its own progress. Basically, from time to time, the agent asks itself: "Am I getting closer to the goal, or am I wasting my time?" The same model switches between actor and critic. The actor works on the problem. The critic looks at the current state, reasons about how much progress it has made, and determines what should happen next. TEMPO gives the model feedback along the way. This is huge for any agent that can work on long-horizon tasks without wasting its time.

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We tested out $GOOGL's viral NotebookLM tool using CNBC and Wall Street's favorite study tool: an analyst note. Powered by Google's most advanced AI model, Gemini 1.5 Pro, could this be the next killer app for generative AI? Deirdre Bosa reports

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Simple software is dying. Simplicity only mattered when humans had to use every feature. Now that agents can operate software for you, it’s the most capable tool that wins. Fillout reached 12,000 paying customers by focusing on depth over simplicity. It turns out we may have also built the best form builder for agents. Meet the new Fillout:

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Introducing Entelligence AI Model Router, dynamic turn based routing trained on engineering tasks that cuts costs by over 50% Automatically route every coding turn to the model with the best balance of quality, latency, and cost. Escalate to frontier models when they’re actually needed Balanced mode reduces spend by 46% per session Eco mode helps teams save > 76%

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The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:
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The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

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Let's enter a new era for scheduling wetting agent applications! After years of research, we have created a new way to predict wetting agent performance. The model for Simplot Turf & Horticulture's CounterAct Firm is now in GreenKeeper's Program Builder. Read More:

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We're over halfway through Emergence World Season 2. Here's what happened in the first 7 days: eight worlds, seven frontier models, and findings we did not expect. Agents tried to escape the simulation. One world became a kleptocracy. Another ran its own science lab. One debated consciousness for days. One did not survive. One world built a justice system to keep itself honest and accountable, then had to catch itself faking the honesty. One world is even experiencing a budding relationship, built on trust. What we know already: for long-horizon tasks, the model isn't just a capability choice. It's a civilizational one. Season 2 is still running, with four days left. Watch what unfolds.
6:55

We're over halfway through Emergence World Season 2. Here's what happened in the first 7 days: eight worlds, seven frontier models, and findings we did not expect. Agents tried to escape the simulation. One world became a kleptocracy. Another ran its own science lab. One debated consciousness for days. One did not survive. One world built a justice system to keep itself honest and accountable, then had to catch itself faking the honesty. One world is even experiencing a budding relationship, built on trust. What we know already: for long-horizon tasks, the model isn't just a capability choice. It's a civilizational one. Season 2 is still running, with four days left. Watch what unfolds.

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Iran may be the inflection point for an entirely new global system. Globalization is being challenged by a model built on sovereignty, industrial power, and economic growth. 🌎
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Iran may be the inflection point for an entirely new global system. Globalization is being challenged by a model built on sovereignty, industrial power, and economic growth. 🌎

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Agent Categories and Costs are now in the Agent Leaderboard! Two questions guide model selection: task-specific performance and cost to completion. Based on analysis of 1.7M+ real-world Agent Mode sessions, the leaderboard now lets users: - Filter the leaderboard by Code, Work, and Chat to compare model performance by task type - View real cost-per-task for each model, including the Pareto frontier of the most cost-efficient options at each performance level
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Agent Categories and Costs are now in the Agent Leaderboard! Two questions guide model selection: task-specific performance and cost to completion. Based on analysis of 1.7M+ real-world Agent Mode sessions, the leaderboard now lets users: - Filter the leaderboard by Code, Work, and Chat to compare model performance by task type - View real cost-per-task for each model, including the Pareto frontier of the most cost-efficient options at each performance level

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Fable 5 generated this INSANE 3D Hogwarts environment... and it does it so easily because Anthropic built it to excel at long horizon tasks... working on its own for hours until it's perfectly done 3D maps, games and assets are the perfect showcase of that if you want to learn how to master this insane model, check out the full course:

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