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Ana Sayfaya Dön

Today I’m excited to introduce micro1 Intelligence, the world’s most advanced platform for training frontier AI models. Achieving AGI is bottlenecked by one main thing: high-quality data. Data based on real-world environments that capture human expert workflows, complex decision-making, and reward signals models need to learn. With micro1 Intelligence,... show more

Ali Ansari

28,959 subscribers

333,257 görüntüleme • 10 ay önce •via X (Twitter)

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38 Yorum

AshutoshShrivastava profil fotoğrafı

AshutoshShrivastava10 ay önce

@aliniikk Congratulations Ali, whoever built the website did an amazing job. love it 👌

Ali Ansari profil fotoğrafı

Ali Ansari10 ay önce

thank you!!

micro1 profil fotoğrafı

micro110 ay önce

@aliniikk

Aidan Gold profil fotoğrafı

Aidan Gold10 ay önce

@aliniikk This is the infrastructure to teach AI how to do all of human work. A critical step in our path to abundance!

Ali Ansari profil fotoğrafı

Ali Ansari10 ay önce

LGI

Mark Esposito, PhD profil fotoğrafı

Mark Esposito, PhD10 ay önce

@aliniikk @micro1_ai is where we are making symbiotic history as we write it daily through the best ingenuity of humans, powered by the most amazing tech on the planet! History scriptwriters! That’s what we are!

Suhaib Saqib profil fotoğrafı

Suhaib Saqib10 ay önce

@aliniikk @Joshuabrowder If micro1 delivers on this, it’ll unlock levels of automation most founders can only dream about right now.

micro1 profil fotoğrafı

micro110 ay önce

@aliniikk 🚀🚀

Farhan profil fotoğrafı

Farhan10 ay önce

@aliniikk This is the first time I’ve seen someone tackle the data side with the same ambition as the model side.

Sydney profil fotoğrafı

Sydney10 ay önce

@aliniikk Chills!!!!

Philip profil fotoğrafı

Philip10 ay önce

@aliniikk Lfg!!

Sahil Jindal profil fotoğrafı

Sahil Jindal10 ay önce

@aliniikk Hyped!!

Arian profil fotoğrafı

Arian10 ay önce

@aliniikk So HYPED!!

Drew Levin profil fotoğrafı

Drew Levin10 ay önce

@aliniikk Lets go! Sick! Congrats

Ronny Chatterjee profil fotoğrafı

Ronny Chatterjee10 ay önce

@aliniikk Big win for micro1 and a HUGE win for AGI and your foundational customers! 🔥

William Almeida profil fotoğrafı

William Almeida10 ay önce

@aliniikk SICK!! 🔥

Cameron profil fotoğrafı

Cameron10 ay önce

@aliniikk banger

Joe Magyer profil fotoğrafı

Joe Magyer10 ay önce

@aliniikk Huge. Big win for customers. Congrats to the team!

Flo Crivello profil fotoğrafı

Flo Crivello10 ay önce

@aliniikk Congrats!

Ahmed Xubair Sid profil fotoğrafı

Ahmed Xubair Sid10 ay önce

@aliniikk Lovely

Nakul Bang profil fotoğrafı

Nakul Bang10 ay önce

@aliniikk 🚀🚀🚀

Assasino! profil fotoğrafı

Assasino!10 ay önce

@aliniikk My Boss

BALOYI VUTOMI QUEEN profil fotoğrafı

BALOYI VUTOMI QUEEN10 ay önce

@aliniikk Congratulations 🎊

Scott with Keep Your Love Alive profil fotoğrafı

Scott with Keep Your Love Alive10 ay önce

@aliniikk Very cool!

mads campbell profil fotoğrafı

mads campbell10 ay önce

@aliniikk Hell yeah

RAKESH AAMALEPATIL profil fotoğrafı

RAKESH AAMALEPATIL10 ay önce

@aliniikk Congrats 👏

Sai Rajeswar profil fotoğrafı

Sai Rajeswar10 ay önce

@aliniikk Congrats Ali! This is big.

Ali Ansari profil fotoğrafı

Ali Ansari10 ay önce

thank you let’s chat!

Elana profil fotoğrafı

Elana10 ay önce

@aliniikk congrats!! So exciting!

Aditya_Nikhil profil fotoğrafı

Aditya_Nikhil10 ay önce

@aliniikk This is incrediblee🥹❤️

Kwame Walker profil fotoğrafı

Kwame Walker10 ay önce

@aliniikk Awesome 🤩

Yev Marusenko profil fotoğrafı

Yev Marusenko10 ay önce

@aliniikk I thought something else was going to happen when the robot grabbed a book. Tons of potential. Congrats!

Anthony gachie profil fotoğrafı

Anthony gachie10 ay önce

@aliniikk Congratulations 🎉🥳🥳

Brady Long profil fotoğrafı

Brady Long10 ay önce

@aliniikk This is such an electric video. Thought it was a trailer for a sequel of I robot

Neet M profil fotoğrafı

Neet M10 ay önce

@aliniikk LETS GOOO!! So pumped for this

Layne Bouche profil fotoğrafı

Layne Bouche10 ay önce

@aliniikk Let's go!!

Melilumusic profil fotoğrafı

Melilumusic10 ay önce

@aliniikk 🤖🦾❤️

Hubert Thieblot profil fotoğrafı

Hubert Thieblot10 ay önce

@aliniikk Coool

Benzer Videolar

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.

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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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.

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.

micro1

72,581 görüntüleme • 24 gün önce

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. we’re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots today (link in comments).

the micro1 robotics lab: real world data for intelligent models that co-exist in the physical world. we’re in-the-wild across 75 countries in 6,000+ unique environments collecting data. diverse movements, objects, and settings. the future of AI is as human as you can imagine. join us to start training robots today (link in comments).

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Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

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Base Labs

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Introducing Midcentury. We’re building the data and simulation infra for physical AI. Today, we’re coming out of stealth with a $15M Series Seed to scale robotics beyond polished demos. We’re already supporting frontier labs with: → The world’s largest egocentric dataset: 2M+ hours, 50+ environments, 20,000+ tasks → Matrix: a frontier simulation platform scaled with our real-world data to evaluate and post-train policies at scale

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Midcentury

632,185 görüntüleme • 18 gün önce

Hiring RL Engineer! Started off as a curious project at Lossfunk to push the boundaries of LLMs in social reasoning - we are now building RL environments, data, and benchmarks to simulate more real-world scenarios. If you want to train SoTA RL models over multi-GPUs (H200s/B200s) to unlock next AI frontier, this is for you.

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Satpal Singh Rathore

46,010 görüntüleme • 1 yıl önce

Physical AI is quickly becoming one of the biggest stories in tech. As robots move beyond controlled environments and into the real world, the need for high-quality real-world data grows with them. jacob on why that matters ↓

Physical AI is quickly becoming one of the biggest stories in tech. As robots move beyond controlled environments and into the real world, the need for high-quality real-world data grows with them. jacob on why that matters ↓

The DATA Foundation

17,325 görüntüleme • 1 ay önce

.Polymath is training world generation models to automate the creation of RL environments. Traditionally, RL environment generation has been bottlenecked by human data. Superintelligence will never be achieved by human data alone. Polymath is building the core technology to enable automated environment generation using far less human effort than traditionally required, and eventually none. This allows for more complex and realistic worlds, and higher quality, scale, and diversity of tasks. This will be essential to unlock RL scaling. The end goal is to create large-scale, long-horizon environments from a text description alone. This will enable the creation of worlds of arbitrary complexity and scale, which is foundational for training & evaluating autonomous, superintelligent AI agents. Congrats on the launch, Dylan Ma and Naren Yenuganti!

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Y Combinator

44,192 görüntüleme • 7 ay önce

AI is only as good as its data. Everyone talks about building smarter AI models, but fewer people ask where the training data comes from. That’s what makes Humyn Labs interesting. They’re focused on collecting high-quality, real-world human data across different environments, behaviors, and sensory inputs. Because if AI is going to understand the physical world, it first needs to learn from the people who already live and work in it. I shared my thoughts in this short video. Let me know what stood out to you. 👇🎥 Humyn Labs KGeN Community #KGeN

AI is only as good as its data. Everyone talks about building smarter AI models, but fewer people ask where the training data comes from. That’s what makes Humyn Labs interesting. They’re focused on collecting high-quality, real-world human data across different environments, behaviors, and sensory inputs. Because if AI is going to understand the physical world, it first needs to learn from the people who already live and work in it. I shared my thoughts in this short video. Let me know what stood out to you. 👇🎥 Humyn Labs KGeN Community #KGeN

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Yann LeCun says we're never gonna get to human-level intelligence by just training on text AI must learn from high-bandwidth sensory data like video to build true world models Current models look PhD-smart but mostly regurgitate, with no real understanding "even a cat understands the physical world better"

Haider.

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Whop CEO Steven Schwartz 🦅 breaks down micro1's data labeling business: "There are so many ways that people can earn income on the internet today, and it's only going to get crazier and crazier." "[micro1] partners with AI labs to provide access to prerecorded videos and situations that the general public can weigh in on and say what they would interpret the most likely solution to any set of visual problems to be." "micro1 is essentially aggregating all those opportunities and delivering payouts at scale to the general public. It's paid for by the labs."

TBPN

47,898 görüntüleme • 10 ay önce

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495,851 görüntüleme • 1 yıl önce

Today, we’re introducing Forge, a system for enterprises to build frontier-grade AI models grounded in their proprietary knowledge. 🌎 Forge bridges the gap between generic AI and enterprise-specific needs. Instead of relying on broad, public data, organizations can train models that understand their internal context embedded within systems, workflows, and policies, aligning AI with their unique operations. We have already partnered with world-leading organizations, like ASML, DSO National Laboratories Singapore, Ericsson, European Space Agency, Home Team Science and Technology Agency (HTX) Singapore and Reply to train models on the proprietary data that powers their most complex systems and future-defining technologies.

Today, we’re introducing Forge, a system for enterprises to build frontier-grade AI models grounded in their proprietary knowledge. 🌎 Forge bridges the gap between generic AI and enterprise-specific needs. Instead of relying on broad, public data, organizations can train models that understand their internal context embedded within systems, workflows, and policies, aligning AI with their unique operations. We have already partnered with world-leading organizations, like ASML, DSO National Laboratories Singapore, Ericsson, European Space Agency, Home Team Science and Technology Agency (HTX) Singapore and Reply to train models on the proprietary data that powers their most complex systems and future-defining technologies.

Mistral AI

449,745 görüntüleme • 6 ay önce

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

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The First 100 begins now. Verify Quality is live on PrismaX. For the first time, anyone can score the robot training data that models learn from, earn points, and compete to become one of The First 100. Better data. Better models. The standard starts with you.

The First 100 begins now. Verify Quality is live on PrismaX. For the first time, anyone can score the robot training data that models learn from, earn points, and compete to become one of The First 100. Better data. Better models. The standard starts with you.

PrismaX

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.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

Jawwwn

1,105,799 görüntüleme • 29 gün önce

On The Information's TITV talking about where open weights actually land inside a big company Nobody serious is picking one model. They'll ride the frontier of intelligence(rent) and most will train OSS models(own) on their own data for the work nobody else can do. For many critical end to end agentification the expertise is in your people and your systems, not on the internet.

On The Information's TITV talking about where open weights actually land inside a big company Nobody serious is picking one model. They'll ride the frontier of intelligence(rent) and most will train OSS models(own) on their own data for the work nobody else can do. For many critical end to end agentification the expertise is in your people and your systems, not on the internet.

Garrett Lord

90,679 görüntüleme • 24 gün önce

CARV envisions a new form of evolution for Artificial General Intelligence (AGI): one driven by the aggregation, sharing, and refinement of high-quality on-chain and off-chain data. To do that, CARV is building an AI chain ecosystem to enable data sovereignty at scale: • CARV SVM Chain, an AI agentic infrastructure that extends SVM's capabilities onto Ethereum. • D.A.T.A. Framework, powered by CARV ID, delivers high-quality on-chain and off-chain data, enriched with tags and metrics, enabling AI agents to learn, share, and evolve dynamically. • CARV Labs accelerates ecosystem growth in and beyond CARV’s ecosystem, spearheading innovation in the AI agentic infrastructure enabling data sovereignty with incubation, grants, and infrastructure. You build?

CARV envisions a new form of evolution for Artificial General Intelligence (AGI): one driven by the aggregation, sharing, and refinement of high-quality on-chain and off-chain data. To do that, CARV is building an AI chain ecosystem to enable data sovereignty at scale: • CARV SVM Chain, an AI agentic infrastructure that extends SVM's capabilities onto Ethereum. • D.A.T.A. Framework, powered by CARV ID, delivers high-quality on-chain and off-chain data, enriched with tags and metrics, enabling AI agents to learn, share, and evolve dynamically. • CARV Labs accelerates ecosystem growth in and beyond CARV’s ecosystem, spearheading innovation in the AI agentic infrastructure enabling data sovereignty with incubation, grants, and infrastructure. You build?

CARV

274,805 görüntüleme • 1 yıl önce

$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.

$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.

Shay Boloor

265,871 görüntüleme • 4 ay önce