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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,...

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Mistral AI

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441,393 просмотров • 4 месяцев назад •via X (Twitter)

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.Snorkel AI helps organizations leverage their own data and expertise to build high-quality, specialized AI systems orders of magnitude faster than fully manual approaches. The company drives value in partnership with AWS and NVIDIA to help their customers. Available in AWS Marketplace, Snorkel’s AI Data Development platform enables enterprises to turn their proprietary data and domain knowledge into expert evaluation and training data, with options for in house data development or white glove dataset delivery.

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Today, Mistral AI and Microsoft are expanding our partnership to bring Mistral’s frontier models across Azure, Microsoft Foundry, Copilot Studio, and Azure Local. Europe should have access to the world’s most capable AI without compromising control over their data, operations, or digital future. By bringing Mistral’s frontier European models into our environment, we are providing customers with a trusted foundation for AI, one that offers both leading-edge innovation and the freedom to operate on their own terms. Learn more:
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Today, Mistral AI and Microsoft are expanding our partnership to bring Mistral’s frontier models across Azure, Microsoft Foundry, Copilot Studio, and Azure Local. Europe should have access to the world’s most capable AI without compromising control over their data, operations, or digital future. By bringing Mistral’s frontier European models into our environment, we are providing customers with a trusted foundation for AI, one that offers both leading-edge innovation and the freedom to operate on their own terms. Learn more:

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306,045 просмотров • 12 дней назад

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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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.

Ali Ansari

117,975 просмотров • 5 месяцев назад

The pilot phase of enterprise AI is ending. What separates the organizations that scale from those that stall is infrastructure, data readiness, and governance built before models are ever deployed. Patrick Moorhead and Daniel Newman sit down with Varun Chhabra of Dell Technologies and Jason Schroedl of NVIDIA at Dell Technologies World 2026 to break down new approaches to agentic AI deployment, investments in data readiness, and how enterprises can expand AI capabilities while maintaining control over security, costs and operations.
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The pilot phase of enterprise AI is ending. What separates the organizations that scale from those that stall is infrastructure, data readiness, and governance built before models are ever deployed. Patrick Moorhead and Daniel Newman sit down with Varun Chhabra of Dell Technologies and Jason Schroedl of NVIDIA at Dell Technologies World 2026 to break down new approaches to agentic AI deployment, investments in data readiness, and how enterprises can expand AI capabilities while maintaining control over security, costs and operations.

Six Five Media

148,133 просмотров • 2 месяцев назад

$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. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.
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$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

264,063 просмотров • 2 месяцев назад

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, frontier labs can train on RL environments across every subject matter all in one place.
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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, frontier labs can train on RL environments across every subject matter all in one place.

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332,574 просмотров • 8 месяцев назад

We’re excited to partner with OpenAI to launch their new open source models natively on Databricks! gpt-oss sets a new standard of quality for open language models, supporting advanced reasoning with the transparency, flexibility and control enterprises need. Running on Databricks, the gpt-oss models connect securely to your data and scale with built-in governance, and expand what you can build and do with GenAI. Try both the 20B and 120B today in the Mosaic AI Playground.
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We’re excited to partner with OpenAI to launch their new open source models natively on Databricks! gpt-oss sets a new standard of quality for open language models, supporting advanced reasoning with the transparency, flexibility and control enterprises need. Running on Databricks, the gpt-oss models connect securely to your data and scale with built-in governance, and expand what you can build and do with GenAI. Try both the 20B and 120B today in the Mosaic AI Playground.

Databricks

10,132 просмотров • 1 год назад

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.
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DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

paul.nft

32,465 просмотров • 1 год назад

Strategy is proud to announce a strategic partnership with marking an important milestone in their AI journey. Strategy makes AI reliable on enterprise data, and is committed to helping organizations transform data into actionable insights and make faster, more informed decisions. Through this collaboration, we are enabling to further leverage data as a strategic asset across their organization, powered by Strategy Mosaic, our Universal Semantic Layer that gives AI the business context it needs to produce consistent, governed results. This partnership aligns with long-term vision of embedding AI and data-driven decision-making into every aspect of their business. By enhancing their analytical capabilities with Strategy, we are helping to build a stronger foundation for innovation, operational excellence, and customer value. As continues its AI-first journey, Strategy is proud to play a role in supporting their growth and shaping the future of their organization. #AI #SemanticLayer #InsuranceTech #StrategyMosaic
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Strategy is proud to announce a strategic partnership with marking an important milestone in their AI journey. Strategy makes AI reliable on enterprise data, and is committed to helping organizations transform data into actionable insights and make faster, more informed decisions. Through this collaboration, we are enabling to further leverage data as a strategic asset across their organization, powered by Strategy Mosaic, our Universal Semantic Layer that gives AI the business context it needs to produce consistent, governed results. This partnership aligns with long-term vision of embedding AI and data-driven decision-making into every aspect of their business. By enhancing their analytical capabilities with Strategy, we are helping to build a stronger foundation for innovation, operational excellence, and customer value. As continues its AI-first journey, Strategy is proud to play a role in supporting their growth and shaping the future of their organization. #AI #SemanticLayer #InsuranceTech #StrategyMosaic

Strategy

55,060 просмотров • 21 дней назад

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.
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Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

Aish

240,611 просмотров • 1 год назад

We’re launching a coin for a new vertical within the world of intellectual property, focused entirely on AI data licensing. AI companies are spending billions of dollars on data (video, text, sound, etc) to train their models. Kled is the middleman. We partner with top film studios and production houses, label their video content, and sell it to AI companies like OpenAI to help train models like ChatGPT, Sora, Midjourney and more. So far, we've already sold millions of hours of data. Our next drop includes 3 million hours of footage, with deals ranging from $2–5 per minute. We've strategically raised $2 million from investors such as K5 Global, who back Uber, Xai and more, alongside creators like Alex Botez and Panda Boi who stand by our platform and its goals. We're excited to bring this project of ours on chain, where we find that it'll benefit most from the space's interactivity and leaders alike. $KLED is now live on Believe
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We’re launching a coin for a new vertical within the world of intellectual property, focused entirely on AI data licensing. AI companies are spending billions of dollars on data (video, text, sound, etc) to train their models. Kled is the middleman. We partner with top film studios and production houses, label their video content, and sell it to AI companies like OpenAI to help train models like ChatGPT, Sora, Midjourney and more. So far, we've already sold millions of hours of data. Our next drop includes 3 million hours of footage, with deals ranging from $2–5 per minute. We've strategically raised $2 million from investors such as K5 Global, who back Uber, Xai and more, alongside creators like Alex Botez and Panda Boi who stand by our platform and its goals. We're excited to bring this project of ours on chain, where we find that it'll benefit most from the space's interactivity and leaders alike. $KLED is now live on Believe

Avi Patel

476,586 просмотров • 1 год назад

VAAP Highlights: Devium, built on zkPass. Devium enables private order data + AI by storing users' order data on Devium's decentralized network nodes. It categorizes the data into vertical sectors and trains specialized AI large models for these vertical domains. These large models support enterprise operations, product development, and upgrades within their respective industries. By replacing data hosting with verifiable proofs, Devium reduces platform risks and minimizes user friction. Devium empowers teams to build real-world applications on verifiable infrastructure such as zkTLS.
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VAAP Highlights: Devium, built on zkPass. Devium enables private order data + AI by storing users' order data on Devium's decentralized network nodes. It categorizes the data into vertical sectors and trains specialized AI large models for these vertical domains. These large models support enterprise operations, product development, and upgrades within their respective industries. By replacing data hosting with verifiable proofs, Devium reduces platform risks and minimizes user friction. Devium empowers teams to build real-world applications on verifiable infrastructure such as zkTLS.

zkPass

10,856 просмотров • 6 месяцев назад

And this just in ... Snowflake and Anthropic are expanding our work across Snowflake Cortex AI – with Claude powering Snowflake Cortex Code and Snowflake Intelligence – as well as Claude Marketplace. 🚀 As more organizations use Anthropic Claude models directly inside Snowflake, demand continues to grow for governed, production-ready AI that delivers the security, observability, and scale enterprises require. Together, Snowflake and Anthropic are helping accelerate enterprise AI adoption and build the foundation for the agentic enterprise. See how we’re building it together, and how customers are using Snowflake and Anthropic to power real-world AI use cases across their organizations:
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And this just in ... Snowflake and Anthropic are expanding our work across Snowflake Cortex AI – with Claude powering Snowflake Cortex Code and Snowflake Intelligence – as well as Claude Marketplace. 🚀 As more organizations use Anthropic Claude models directly inside Snowflake, demand continues to grow for governed, production-ready AI that delivers the security, observability, and scale enterprises require. Together, Snowflake and Anthropic are helping accelerate enterprise AI adoption and build the foundation for the agentic enterprise. See how we’re building it together, and how customers are using Snowflake and Anthropic to power real-world AI use cases across their organizations:

Snowflake

10,894 просмотров • 2 месяцев назад

We’re thrilled to announce a strategic multi-year partnership with Paul Jankura that will enable global enterprises to develop and scale easy, efficient, and trusted AI products, apps, and workflows powered by the latest state-of-the-art models. Anthropic’s newest Claude 3.5 models will be available for users to securely leverage within Snowflake Cortex AI. With Claude’s industry-leading accuracy and expansive context window, users can build mission-critical AI apps that deliver reliable, factual responses across their entire knowledge base — from customer documentation to technical specifications. Claude will serve as a key large language model powering Snowflake’s agentic AI products, including Snowflake Intelligence and Cortex Analyst, so users can reduce time-to-market and begin seeing value with industry-leading accuracy and scalability.
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We’re thrilled to announce a strategic multi-year partnership with Paul Jankura that will enable global enterprises to develop and scale easy, efficient, and trusted AI products, apps, and workflows powered by the latest state-of-the-art models. Anthropic’s newest Claude 3.5 models will be available for users to securely leverage within Snowflake Cortex AI. With Claude’s industry-leading accuracy and expansive context window, users can build mission-critical AI apps that deliver reliable, factual responses across their entire knowledge base — from customer documentation to technical specifications. Claude will serve as a key large language model powering Snowflake’s agentic AI products, including Snowflake Intelligence and Cortex Analyst, so users can reduce time-to-market and begin seeing value with industry-leading accuracy and scalability.

Snowflake

16,287 просмотров • 1 год назад

Are open source models catching up to proprietary models? We’ve looked back at 3 years of Arena’s data to show how the race has evolved. For comparison, we’ve taken the top 20% of the models and uncovered the following: - Before mid 2024: The gap was between 100-150 points - In the second half of 2024, the gap rapidly narrowed to ~50 points - This was driven by the fact that proprietary models have not significantly improved between early 2023 and November 2024 - hovering around the 1350 score for the top 20% of proprietary models - Then since Jan 2025, we saw a parallel improvement in both proprietary and open source models - Meaning that for the last ~14 months the gap stayed steady at around 50-60 points For context, 50 points is the gap between the 1st and 20th place in the Overall leaderboard Today, proprietary models take up the first 20 spots on the Text Arena, with Anthropic, Google DeepMind , xAI and OpenAI leading in the rankings. The top open source models are ranked #20 (GLM-5 by Z.ai ), #23 (Kimi-K2.5-Thinking by Kimi.ai ) and #27 (Qwen3.5-397b-a17b by Qwen ).
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Are open source models catching up to proprietary models? We’ve looked back at 3 years of Arena’s data to show how the race has evolved. For comparison, we’ve taken the top 20% of the models and uncovered the following: - Before mid 2024: The gap was between 100-150 points - In the second half of 2024, the gap rapidly narrowed to ~50 points - This was driven by the fact that proprietary models have not significantly improved between early 2023 and November 2024 - hovering around the 1350 score for the top 20% of proprietary models - Then since Jan 2025, we saw a parallel improvement in both proprietary and open source models - Meaning that for the last ~14 months the gap stayed steady at around 50-60 points For context, 50 points is the gap between the 1st and 20th place in the Overall leaderboard Today, proprietary models take up the first 20 spots on the Text Arena, with Anthropic, Google DeepMind , xAI and OpenAI leading in the rankings. The top open source models are ranked #20 (GLM-5 by Z.ai ), #23 (Kimi-K2.5-Thinking by Kimi.ai ) and #27 (Qwen3.5-397b-a17b by Qwen ).

Arena.ai

22,447 просмотров • 4 месяцев назад

With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.
53:51

With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.

RoboPapers

85,927 просмотров • 8 месяцев назад

INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7
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INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

Factory

327,877 просмотров • 1 год назад

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.
1:21

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 просмотров • 10 месяцев назад

Today we are introducing Tara. Biological datasets are a source of insights and a means to train biological AI models. As the ability to reason at scale emerges, they take on a new role: the ground truth for testing what reasoning models produce, and the environment in which those models operate, get feedback, and improve. Tara, our autonomous research agent, is embedded in our ever-expanding datasets, lab-generated and synthetic, and built to test and evolve the hypotheses frontier models generate, matching the pace at which they produce new ideas. By keeping those models grounded in a vast space of high-precision biological data, we believe we can compound biological reasoning and close the impedance mismatch between hypothesis generation and validation.
4:03

Today we are introducing Tara. Biological datasets are a source of insights and a means to train biological AI models. As the ability to reason at scale emerges, they take on a new role: the ground truth for testing what reasoning models produce, and the environment in which those models operate, get feedback, and improve. Tara, our autonomous research agent, is embedded in our ever-expanding datasets, lab-generated and synthetic, and built to test and evolve the hypotheses frontier models generate, matching the pace at which they produce new ideas. By keeping those models grounded in a vast space of high-precision biological data, we believe we can compound biological reasoning and close the impedance mismatch between hypothesis generation and validation.

Nima Alidoust

27,670 просмотров • 1 месяц назад

Mira Murati says the outcome of frontier AI is not predestined: "Predicting a dystopia or a utopia, to me, feels very simplified because the truth is we actually have a lot of agency in how we build this technology, in the tools that we're building, how we're deploying it." "There are certainly those risks. We all understand the potential for greatness that comes with building frontier AI systems, and that's why we're working on them." "Where I might disagree or take a different path is that I think we have a lot of agency. This period of time where both humans and AI systems have their hands on the wheel, and we can collaborate, it's a very important time to get right." Mira Murati at Bloomberg Tech live with Emily Chang
1:35

Mira Murati says the outcome of frontier AI is not predestined: "Predicting a dystopia or a utopia, to me, feels very simplified because the truth is we actually have a lot of agency in how we build this technology, in the tools that we're building, how we're deploying it." "There are certainly those risks. We all understand the potential for greatness that comes with building frontier AI systems, and that's why we're working on them." "Where I might disagree or take a different path is that I think we have a lot of agency. This period of time where both humans and AI systems have their hands on the wheel, and we can collaborate, it's a very important time to get right." Mira Murati at Bloomberg Tech live with Emily Chang

a16z

120,011 просмотров • 1 месяц назад