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Today we’re introducing flow, micro1’s next-generation data platform for turning human expertise into measurable capability gains. At the core of flow are Realms, micro1’s real-world RL environments where experts establish what strong performance looks like. flow-gen models expand human judgment into new environments, rubrics, variations, and edge cases, while... flow-qc models evaluate performance, identify the highest-value failures, and route them back to experts for review. Each cycle produces a stronger training signal and a measurable gain in capability. Those gains compound across frontier models, enterprise agents through Cortex, and robotics, while improving the suite of data generation models recursively. We’re moving beyond producing data to delivering abundant & predictable units of intelligence improvement.show more

micro1

16,895 subscribers

69,519 Aufrufe • vor 5 Tagen •via X (Twitter)

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32 Kommentare

Profilbild von Arian

Arianvor 5 Tagen

The platform that pushes human data forward

Profilbild von Julia

Juliavor 5 Tagen

raw human expertise --> advanced intelligence improvment 📈

Profilbild von Brandon

Brandonvor 5 Tagen

Let’s go!

Profilbild von Yash Hande

Yash Handevor 5 Tagen

let's goo!!

Profilbild von Jess Li

Jess Livor 5 Tagen

so excited for this! 🔥

Profilbild von Sydney

Sydneyvor 5 Tagen

lets goo

Profilbild von Daniel

Danielvor 5 Tagen

LGI

Profilbild von Jacky

Jackyvor 5 Tagen

absolute cinema

Profilbild von William Almeida

William Almeidavor 5 Tagen

lgi!!

Profilbild von Tyler

Tylervor 5 Tagen

flow state activated

Profilbild von Nima Yazdani

Nima Yazdanivor 5 Tagen

love it :)

Profilbild von Giovanna Moeller

Giovanna Moellervor 5 Tagen

this is huge 🔥🔥🔥

Profilbild von AmericanPapaBear™

AmericanPapaBear™vor 5 Tagen

Very interesting

Profilbild von EKOS _ AGI 🦊 🇮🇷

EKOS _ AGI 🦊 🇮🇷vor 5 Tagen

@aliansarinik Turning human expertise into measurable capability is the right direction. The real challenge is keeping that expertise reliable as agents scale.

Profilbild von Hopefully.JUNEAI ⚽️ 🔱🥇

Hopefully.JUNEAI ⚽️ 🔱🥇vor 5 Tagen

Great motion and services Please I await my interview results on video annotation specialist and generalist It’s been close to a week now still on hiring review Best regards💙

Profilbild von decroitFps

decroitFpsvor 5 Tagen

@aliansarinik Forward and Beyond

Profilbild von José Daniel Durán

José Daniel Duránvor 5 Tagen

Amazing 🤩🤩🤩

Profilbild von Aidan Gold

Aidan Goldvor 5 Tagen

Recursive + predictable intelligence improvement, is the only scalable way to automate work across the economy.

Profilbild von Daniel ₿ 🇧🇷🇮🇹🇵🇾

Daniel ₿ 🇧🇷🇮🇹🇵🇾vor 5 Tagen

Out of this world1!

Profilbild von Joshua Browder

Joshua Browdervor 5 Tagen

Extremely cool

Profilbild von Lami 👑

Lami 👑vor 5 Tagen

Flow activated 😁

Profilbild von KennyBgr

KennyBgrvor 5 Tagen

To the moon 🚀

Profilbild von Ava

Avavor 5 Tagen

@nima_yaz Lets GOO

Profilbild von Kwame Walker

Kwame Walkervor 5 Tagen

Awesome

Profilbild von mad max

mad maxvor 5 Tagen

I applied twice for the generalist video annotation role and got rejected despite answering all the questions correctly and having extensive experience. I requested for a feedback and was told that my interview was great but i just wasn’t a good fit.

Profilbild von AK47 ODDS HUNTER 🔥🔥🔥

AK47 ODDS HUNTER 🔥🔥🔥vor 5 Tagen

LFG🔥🔥🔥

Profilbild von Viral Videos™️

Viral Videos™️vor 5 Tagen

Really excited to see how this plays out for Cortex and in particular, robotics. Congrats to the entire team.

Profilbild von aka

akavor 5 Tagen

Wow! This is really exciting

Profilbild von Dr. Clown, PhD

Dr. Clown, PhDvor 5 Tagen

AI is going to change the world so fast!

Profilbild von 𝟒𝟎𝟒

𝟒𝟎𝟒vor 5 Tagen

Has anyone actually gotten accepted to micro 1 jobs? I keep trying.

Profilbild von 𝟒𝟎𝟒

𝟒𝟎𝟒vor 5 Tagen

Hold on, really?

Profilbild von Nwannedi Chrix

Nwannedi Chrixvor 5 Tagen

Impressive!

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Excited to introduce micro1 Cortex, a contextual evaluation, visibility, and improvement platform for enterprise AI agents. Foundational models are trained for general intelligence, but enterprises need agents that perform reliably inside their unique context: workflows, policies, data environments, and edge cases. Cortex brings trust to enterprise AI by leveraging domain experts and real-world scenarios for any use case to test, diagnose, and improve how agents behave in production.

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The Graph

425,465 Aufrufe • vor 2 Jahren

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Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

Alex Ratner

495,851 Aufrufe • vor 1 Jahr

Today, we’re excited to announce our $50M Series B, led by Greenfield Partners (formerly TPG Capital), with participation from Lightspeed and Notable Capital. 🚀 At PatronusAI, we develop simulations and evals to train and improve AI. The first phase of AI was built on static benchmarks, but that era is over now. As agents are used to solve longer and longer tasks, they need to practice in dynamic, living worlds to get better. Simulations are the critical infrastructure powering this next phase. As a company, we’re behind the most influential research and products in AI evaluation, like FinanceBench, Lynx, and Percival. And things have moved at the speed of light since. ⚡ We partner with the world's leading frontier AI labs and enterprises, and our revenue has grown more than 15x over the past year. Additionally, today, we’re introducing a preview of the first Digital World Model for AI agent training and simulation: Patronus-DWM. Digital World Models are language diffusion world models that predict realistic environment behaviors and steer agent actions across digital workflows. Just as physical world models predict how objects move through space, we’re developing the equivalent for the digital world: predicting how agents act in digital workflows, then using that to scale the creation of high-quality training data for LLMs. Digital World Models help us push the frontier of ultra long horizon workflows, and unlock a new class of self-improving RL environments. This is our scalable approach to simulating all of the world’s intelligence. The round was also joined by Datadog, Inc., Samsung Ventures, Gokul Rajaram, Factorial Capital, and a large cohort of amazing AI leaders and researchers across Anthropic, OpenAI, Google DeepMind, NVIDIA, Recursive, and more. ✨ It has been the ride of a lifetime. But we’re just getting started. The best is yet to come. "Do not go gentle into that good night, Rage, rage against the dying of the light" - Dylan Thomas (1954)

Today, we’re excited to announce our $50M Series B, led by Greenfield Partners (formerly TPG Capital), with participation from Lightspeed and Notable Capital. 🚀 At PatronusAI, we develop simulations and evals to train and improve AI. The first phase of AI was built on static benchmarks, but that era is over now. As agents are used to solve longer and longer tasks, they need to practice in dynamic, living worlds to get better. Simulations are the critical infrastructure powering this next phase. As a company, we’re behind the most influential research and products in AI evaluation, like FinanceBench, Lynx, and Percival. And things have moved at the speed of light since. ⚡ We partner with the world's leading frontier AI labs and enterprises, and our revenue has grown more than 15x over the past year. Additionally, today, we’re introducing a preview of the first Digital World Model for AI agent training and simulation: Patronus-DWM. Digital World Models are language diffusion world models that predict realistic environment behaviors and steer agent actions across digital workflows. Just as physical world models predict how objects move through space, we’re developing the equivalent for the digital world: predicting how agents act in digital workflows, then using that to scale the creation of high-quality training data for LLMs. Digital World Models help us push the frontier of ultra long horizon workflows, and unlock a new class of self-improving RL environments. This is our scalable approach to simulating all of the world’s intelligence. The round was also joined by Datadog, Inc., Samsung Ventures, Gokul Rajaram, Factorial Capital, and a large cohort of amazing AI leaders and researchers across Anthropic, OpenAI, Google DeepMind, NVIDIA, Recursive, and more. ✨ It has been the ride of a lifetime. But we’re just getting started. The best is yet to come. "Do not go gentle into that good night, Rage, rage against the dying of the light" - Dylan Thomas (1954)

Anand Kannappan

44,432 Aufrufe • vor 2 Monaten

Today, we’re excited to announce our $50M Series B, led by Greenfield Partners, with participation from Lightspeed and Notable Capital. 🚀 At Patronus AI, we develop simulations and evals to train and improve AI. The first phase of AI was built on static benchmarks, but that era is over. As agents are used to solve longer and longer tasks, they need to practice in dynamic, living worlds to get better. Simulations are the critical infrastructure powering this next phase. As a company, we’re behind the most influential research and products in AI evaluation, like FinanceBench, Lynx, and Percival. And things have moved at the speed of light since.⚡ We partner with the world's leading frontier AI labs and enterprises, and our revenue has grown more than 15x over the past year. Additionally, today, we’re introducing a preview of the first Digital World Model for AI agent training and simulation: Patronus-DWM. Digital World Models are language diffusion world models that predict realistic environment behaviors and steer agent actions across digital workflows. Just as physical world models predict how objects move through space, we’re developing the equivalent for the digital world: predicting how agents act in digital workflows, then using that to scale the creation of high-quality training data for LLMs. Digital World Models help us push the frontier of ultra long horizon workflows, and unlock a new class of self-improving RL environments. This is our scalable approach to simulating all of the world’s intelligence. The round was also joined by Datadog, Inc., Samsung Ventures, Gokul Rajaram, Factorial Capital, and a large cohort of amazing AI leaders across Anthropic, OpenAI, Google DeepMind, NVIDIA, Recursive, and more.✨ It has been the ride of a lifetime. But we’re just getting started. The best is yet to come. "Do not go gentle into that good night, Rage, rage against the dying of the light" - Dylan Thomas (1954)

Today, we’re excited to announce our $50M Series B, led by Greenfield Partners, with participation from Lightspeed and Notable Capital. 🚀 At Patronus AI, we develop simulations and evals to train and improve AI. The first phase of AI was built on static benchmarks, but that era is over. As agents are used to solve longer and longer tasks, they need to practice in dynamic, living worlds to get better. Simulations are the critical infrastructure powering this next phase. As a company, we’re behind the most influential research and products in AI evaluation, like FinanceBench, Lynx, and Percival. And things have moved at the speed of light since.⚡ We partner with the world's leading frontier AI labs and enterprises, and our revenue has grown more than 15x over the past year. Additionally, today, we’re introducing a preview of the first Digital World Model for AI agent training and simulation: Patronus-DWM. Digital World Models are language diffusion world models that predict realistic environment behaviors and steer agent actions across digital workflows. Just as physical world models predict how objects move through space, we’re developing the equivalent for the digital world: predicting how agents act in digital workflows, then using that to scale the creation of high-quality training data for LLMs. Digital World Models help us push the frontier of ultra long horizon workflows, and unlock a new class of self-improving RL environments. This is our scalable approach to simulating all of the world’s intelligence. The round was also joined by Datadog, Inc., Samsung Ventures, Gokul Rajaram, Factorial Capital, and a large cohort of amazing AI leaders across Anthropic, OpenAI, Google DeepMind, NVIDIA, Recursive, and more.✨ It has been the ride of a lifetime. But we’re just getting started. The best is yet to come. "Do not go gentle into that good night, Rage, rage against the dying of the light" - Dylan Thomas (1954)

PatronusAI

95,619 Aufrufe • vor 2 Monaten

Unlike Microsoft Copilot, Salesforce Agentforce’s Large Action Models (LAMs) leverage both structured and unstructured data across Salesforce’s secure, scalable ecosystem—Data Cloud, Customer 360, Tableau, Flow, & Slack—to drive real-time, intelligent, autonomous actions. It’s not just automation; it’s AI agents acting with precision, context, accuracy, and compliance, turning routine tasks into autonomous, value-driven actions. Agentforce enables teams to focus on strategy while Autonomous Agents handles the customer and employee workloads, all within a trusted framework that adapts and learns. This is the future of work, powered by Salesforce. This is what AI was meant to be. #Agentforce

Unlike Microsoft Copilot, Salesforce Agentforce’s Large Action Models (LAMs) leverage both structured and unstructured data across Salesforce’s secure, scalable ecosystem—Data Cloud, Customer 360, Tableau, Flow, & Slack—to drive real-time, intelligent, autonomous actions. It’s not just automation; it’s AI agents acting with precision, context, accuracy, and compliance, turning routine tasks into autonomous, value-driven actions. Agentforce enables teams to focus on strategy while Autonomous Agents handles the customer and employee workloads, all within a trusted framework that adapts and learns. This is the future of work, powered by Salesforce. This is what AI was meant to be. #Agentforce

Marc Benioff

54,134 Aufrufe • vor 1 Jahr

AI models are upgrading at a new speed. OpenAI’s GPT-6 Astra model, released last week, follows a self-controlled “chain-of-thought” reasoning pattern that can evade certain kinds of monitoring. The next value differentiator for improved models may be less about performance and more about auditability. More on Lightwork this week: → ByteDance is working towards releasing a new real-time world model, pressuring Western frontier labs to keep pace. → Personal agents like Meta’s Muse and Instinct are gaining popularity, raising a conversation over personal autonomy with agents. → Some applications are throttling agent traffic. What does this mean for the companies building them? Claire Zau Josh Machiz Bucky Moore 0:00 Welcome, Bucky Moore! 1:12 OpenAI's Astra 3:50 The New AI Model’s Architecture 4:40 AI Safety and Guardrails 8:01 Trusting AI Agents 11:09 The Future of AI Models 12:06 The AI Agent Land Grab 17:09 AI, Trust, and Personal Data 20:12 Improving AI Agent Capabilities 22:11 Enterprise AI Assistants and Personal Context 24:05 AI Agents and the Future of the Web 25:50 ByteDance and World Models 28:19 World Models and Consumer Content 30:56 Reactor and World Model Infrastructure 34:13 Real-Time Video Models and Scaling

AI models are upgrading at a new speed. OpenAI’s GPT-6 Astra model, released last week, follows a self-controlled “chain-of-thought” reasoning pattern that can evade certain kinds of monitoring. The next value differentiator for improved models may be less about performance and more about auditability. More on Lightwork this week: → ByteDance is working towards releasing a new real-time world model, pressuring Western frontier labs to keep pace. → Personal agents like Meta’s Muse and Instinct are gaining popularity, raising a conversation over personal autonomy with agents. → Some applications are throttling agent traffic. What does this mean for the companies building them? Claire Zau Josh Machiz Bucky Moore 0:00 Welcome, Bucky Moore! 1:12 OpenAI's Astra 3:50 The New AI Model’s Architecture 4:40 AI Safety and Guardrails 8:01 Trusting AI Agents 11:09 The Future of AI Models 12:06 The AI Agent Land Grab 17:09 AI, Trust, and Personal Data 20:12 Improving AI Agent Capabilities 22:11 Enterprise AI Assistants and Personal Context 24:05 AI Agents and the Future of the Web 25:50 ByteDance and World Models 28:19 World Models and Consumer Content 30:56 Reactor and World Model Infrastructure 34:13 Real-Time Video Models and Scaling

Lightspeed

14,796 Aufrufe • vor 9 Tagen

Upcoming Releases for Alchemist AI We’re rolling out a series of infrastructure and product upgrades to further enhance the Alchemist AI platform: • New Generation Pipeline Improved orchestration, lower latency, and optimized resource handling for faster and more scalable world generation. • 3D Engine Enhancements Upgraded rendering capabilities, improved material fidelity, and smoother in-app performance across devices. • App Testing Agent Automated simulation and behavioral testing across generated environments to ensure logic consistency and playability. • Expanded 3D Model Library Hundreds of new assets added across key categories, optimized for real-time use and fully integrated into the creation workflow. • Texturized 3D Model Generation Models now ship with context-aware textures and material mapping, reducing setup time and enhancing visual realism.

Upcoming Releases for Alchemist AI We’re rolling out a series of infrastructure and product upgrades to further enhance the Alchemist AI platform: • New Generation Pipeline Improved orchestration, lower latency, and optimized resource handling for faster and more scalable world generation. • 3D Engine Enhancements Upgraded rendering capabilities, improved material fidelity, and smoother in-app performance across devices. • App Testing Agent Automated simulation and behavioral testing across generated environments to ensure logic consistency and playability. • Expanded 3D Model Library Hundreds of new assets added across key categories, optimized for real-time use and fully integrated into the creation workflow. • Texturized 3D Model Generation Models now ship with context-aware textures and material mapping, reducing setup time and enhancing visual realism.

ALCHEMIST AI 🔮

10,899 Aufrufe • vor 1 Jahr

My conversation with Tarun Chitra As a co-founder of Gauntlet and GP at Robot Ventures, Tarun has one of the sharpest frameworks for understanding market structure across both crypto and AI. In this episode we dig into why open source AI is unbundling faster than most people expect, and how the resulting stack looks surprisingly similar to DeFi. We spend time mapping the AI infrastructure layers directly onto crypto primitives and examining where value is actually going to accrue as models, harnesses, routers, and inference providers separate. At the center of the conversation is the belief that AI’s unbundling is creating a new competitive order-flow market (data centers competing like nodes, MEV-like dynamics for tokens/GPUs) while crypto itself has settled into a more mature “TradFi plus+” phase focused on trading, payments, and bringing real assets on-chain. We discuss: - The current state of crypto as TradFi+ and the decline of speculative narratives - Why AI is killing Bitcoin mining economics and weakening ETH value accrual - The architectural parallel between AI stacks and DeFi (Harnesses = Wallets, Routers = DEX aggregators, Models = Protocols, Inference Providers = LPs) - Why open source models are unbundling faster than traditional software - Agents as the next interface layer and the potential unbundling of ETFs -Cryptography, verifiable compute, and turning GPUs into digital assets - Onchain compute trading as the real crypto × AI opportunity - Sustainable business models and where value will ultimately capture Timestamps: 0:00 – Introduction & State of the Crypto Market 2:00 – Speculative Narratives Fade, Trading & Payments Remain 7:00 – AI’s Impact on Bitcoin Economics & Data Center Opportunity Cost 9:00 – ETH Value Accrual, Solana Positioning & DeFi Token Sustainability 15:00 – Trading Design Space & On-Chain Volume Upside 25:00 – AI Unbundling Thesis: Open-Source Models vs Data Centers 35:00 – The DeFi Mapping (Harnesses, Routers, Models, Inference) 48:00 – Agents, Preference Expression & Unbundling Traditional Products 1:00:00 – Real-Time Harness Generation & Active Learning 1:05:00 – Cryptography, Verifiable Compute & On-Chain GPU Markets 1:10:00 – Closing Thoughts: Where Value Accrues Next Enjoy!

My conversation with Tarun Chitra As a co-founder of Gauntlet and GP at Robot Ventures, Tarun has one of the sharpest frameworks for understanding market structure across both crypto and AI. In this episode we dig into why open source AI is unbundling faster than most people expect, and how the resulting stack looks surprisingly similar to DeFi. We spend time mapping the AI infrastructure layers directly onto crypto primitives and examining where value is actually going to accrue as models, harnesses, routers, and inference providers separate. At the center of the conversation is the belief that AI’s unbundling is creating a new competitive order-flow market (data centers competing like nodes, MEV-like dynamics for tokens/GPUs) while crypto itself has settled into a more mature “TradFi plus+” phase focused on trading, payments, and bringing real assets on-chain. We discuss: - The current state of crypto as TradFi+ and the decline of speculative narratives - Why AI is killing Bitcoin mining economics and weakening ETH value accrual - The architectural parallel between AI stacks and DeFi (Harnesses = Wallets, Routers = DEX aggregators, Models = Protocols, Inference Providers = LPs) - Why open source models are unbundling faster than traditional software - Agents as the next interface layer and the potential unbundling of ETFs -Cryptography, verifiable compute, and turning GPUs into digital assets - Onchain compute trading as the real crypto × AI opportunity - Sustainable business models and where value will ultimately capture Timestamps: 0:00 – Introduction & State of the Crypto Market 2:00 – Speculative Narratives Fade, Trading & Payments Remain 7:00 – AI’s Impact on Bitcoin Economics & Data Center Opportunity Cost 9:00 – ETH Value Accrual, Solana Positioning & DeFi Token Sustainability 15:00 – Trading Design Space & On-Chain Volume Upside 25:00 – AI Unbundling Thesis: Open-Source Models vs Data Centers 35:00 – The DeFi Mapping (Harnesses, Routers, Models, Inference) 48:00 – Agents, Preference Expression & Unbundling Traditional Products 1:00:00 – Real-Time Harness Generation & Active Learning 1:05:00 – Cryptography, Verifiable Compute & On-Chain GPU Markets 1:10:00 – Closing Thoughts: Where Value Accrues Next Enjoy!

Logan Jastremski

132,411 Aufrufe • vor 1 Monat

For 9+ years we’ve been building simulators, data engines, data pipelines, scenario editors, RL environments, what's now world models... and dozens of other products. Now we’ve connected them all under one agentic layer. It is not just another harness but also the broader system to safely design, develop, and deploy Physical AI. I continue to believe a modern, AI-first development environment is one of the biggest bottlenecks to putting intelligence on a billion machines. Unsurprisingly, we’ve been using it internally for a while now and the impact has been profound in almost every measurable way. As you might know, our old office was on the corner of Pioneer and Dana – and calling it Pioneer would be too cliche. Introducing Dana :)

For 9+ years we’ve been building simulators, data engines, data pipelines, scenario editors, RL environments, what's now world models... and dozens of other products. Now we’ve connected them all under one agentic layer. It is not just another harness but also the broader system to safely design, develop, and deploy Physical AI. I continue to believe a modern, AI-first development environment is one of the biggest bottlenecks to putting intelligence on a billion machines. Unsurprisingly, we’ve been using it internally for a while now and the impact has been profound in almost every measurable way. As you might know, our old office was on the corner of Pioneer and Dana – and calling it Pioneer would be too cliche. Introducing Dana :)

Qasar Younis

85,188 Aufrufe • vor 2 Monaten