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Today we’re launching CredAgent, the most transformational product ever built for credentialing. An autonomous agent that can reason, verify, and execute complex credentialing workflows end-to-end, with human oversight. Credentialing today still relies on outsourced BPOs and manual workflows. This isn’t another workflow tool or co-pilot. It’s a fundamental shift...

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Nick Macario

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114,601 Aufrufe • vor 4 Monaten •via X (Twitter)

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Abhinav Asthana

18,105 Aufrufe • vor 2 Monaten

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!
1:21

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Sam Rodriques

724,765 Aufrufe • vor 1 Jahr

AOC on Medicare Advantage: In theory, agents and brokers work with everyday people enrolling in a plan and are there to help them pick the best plan for their health, right? That’s how it’s supposed to be set up. What gets complicated is that agents and brokers are paid by health insurers for enrolling individuals in specific plans. And so you’re supposed to have agents and brokers talking to your mom or your grandparent, saying, “We’d like to enroll you. We think this plan is what’s best for you.” But what that person doesn’t know is that UnitedHealthcare is saying, “We’ll give you a vacation if you enroll people in our plan,” or, “We’ll give you a couple of extra hundred dollars if you enroll someone in a Blue Cross Blue Shield plan, a United plan, or any number of other plans.” And in your testimony, you stated that agents and brokers received $10 billion in this kind of compensation in 2022 alone, right? That’s $10 billion in taxpayer money. A lot of it is being funneled through these corporations. And so that is money that is not going to care. It’s going to perks, trips to Boca, and financial incentives for brokers to enroll people out of traditional Medicare and into for-profit Medicare Advantage plans.
5:13

AOC on Medicare Advantage: In theory, agents and brokers work with everyday people enrolling in a plan and are there to help them pick the best plan for their health, right? That’s how it’s supposed to be set up. What gets complicated is that agents and brokers are paid by health insurers for enrolling individuals in specific plans. And so you’re supposed to have agents and brokers talking to your mom or your grandparent, saying, “We’d like to enroll you. We think this plan is what’s best for you.” But what that person doesn’t know is that UnitedHealthcare is saying, “We’ll give you a vacation if you enroll people in our plan,” or, “We’ll give you a couple of extra hundred dollars if you enroll someone in a Blue Cross Blue Shield plan, a United plan, or any number of other plans.” And in your testimony, you stated that agents and brokers received $10 billion in this kind of compensation in 2022 alone, right? That’s $10 billion in taxpayer money. A lot of it is being funneled through these corporations. And so that is money that is not going to care. It’s going to perks, trips to Boca, and financial incentives for brokers to enroll people out of traditional Medicare and into for-profit Medicare Advantage plans.

Acyn

55,668 Aufrufe • vor 1 Monat

What should the new administration do to support healthcare entrepreneurs? A few weeks ago I sat down with Dan Crenshaw , who sits on the House Subcommittee for Health, to discuss ways our government can make it easier to build in healthcare. We’re at a crisis point and need the private sector to improve productivity: more health outcomes with less labor and cost. Here are a couple of my ideas. What did I get wrong? And what else should be a priority? — Streamline Licensure, Credentialing, and Enrollment Telemedicine companies who want to treat patients across state lines are hamstrung by rules requiring providers see patients only in states where they have obtained licensure. In my eyes a doctor is a doctor: we should seek to recognize licensure from one state to another. Meanwhile, every high growth healthcare startup burns tens of thousands of dollars on every newly-hired provider – paying salary while waiting for slow credentialing and enrollment processes with payer organizations. What if we could create a national clearinghouse for credentialing and enrollment with a 1-week SLA? Such a change could improve the capital efficiency for innovative new companies. Reimbursement Reform For AI Time-based reimbursement codes (e.g. 99203 - New patient office visit, 30-44 minutes) require providers to spend a fixed amount of time on a visit in order to bill a code. But what if AI can enable a doctor to complete the visit more quickly? There’s no incentive for labor-saving innovation like every other part of the economy. This is silly! Let's align codes with value creation, not labor input. In general, we must create clear pathways to reward AI in care delivery – we have hundreds of AI companies already slashing administrative costs across the healthcare industry, but precious few success stories on the care delivery side. When software can complete a service described in a code (e.g. a diagnostic assessment), it should be eligible for reimbursement like a person. And while we need to ensure a quality and safety bar, we should not allow obviously-conflicted physicians’ groups to make determinations about what that bar is. Eliminate Unfair Preferences For Nonprofits Nonprofit organizations are not inherently more virtuous and better stewards of patient and taxpayer interests than for-profit businesses. And they are almost always run less efficiently than private sector equivalents. From Medicaid coverage to specialized programs like PACE, we should eliminate both explicit and implicit preference for nonprofit organizations and put them on a level playing field with entrepreneur-run startups. Taxpayer-funded healthcare should support the best and most effective provider groups, not the best-connected. Improve Interoperability to Support Healthcare IT The 21st Century Cures Act was signed into law at the end of 2016. In spite of its noble goal to require EHRs to expose an API and enable access to patient data, I still hear plenty of complaints from entrepreneurs about how difficult it can be to get data in and out of EHRs to build new AI applications. We need to investigate the state of play in interoperability: and find regulatory or statutory solutions that will make life easier for health IT startups. — I continue to be excited about the potential of value-based care – as long as models are constructed to provide strong incentives for true productivity improvement through care model and technology – and would like to see continued support for VBC across Medicare and Medicaid. One specific idea would be to revive CMMI’s Geographic DCE model – which (although canceled by the Biden administration) in my eyes could do a lot of good for Medicare beneficiaries across many rural geographies. What else should we be thinking about? (h/t Samir Unni for helping brainstorm)
0:43

What should the new administration do to support healthcare entrepreneurs? A few weeks ago I sat down with Dan Crenshaw , who sits on the House Subcommittee for Health, to discuss ways our government can make it easier to build in healthcare. We’re at a crisis point and need the private sector to improve productivity: more health outcomes with less labor and cost. Here are a couple of my ideas. What did I get wrong? And what else should be a priority? — Streamline Licensure, Credentialing, and Enrollment Telemedicine companies who want to treat patients across state lines are hamstrung by rules requiring providers see patients only in states where they have obtained licensure. In my eyes a doctor is a doctor: we should seek to recognize licensure from one state to another. Meanwhile, every high growth healthcare startup burns tens of thousands of dollars on every newly-hired provider – paying salary while waiting for slow credentialing and enrollment processes with payer organizations. What if we could create a national clearinghouse for credentialing and enrollment with a 1-week SLA? Such a change could improve the capital efficiency for innovative new companies. Reimbursement Reform For AI Time-based reimbursement codes (e.g. 99203 - New patient office visit, 30-44 minutes) require providers to spend a fixed amount of time on a visit in order to bill a code. But what if AI can enable a doctor to complete the visit more quickly? There’s no incentive for labor-saving innovation like every other part of the economy. This is silly! Let's align codes with value creation, not labor input. In general, we must create clear pathways to reward AI in care delivery – we have hundreds of AI companies already slashing administrative costs across the healthcare industry, but precious few success stories on the care delivery side. When software can complete a service described in a code (e.g. a diagnostic assessment), it should be eligible for reimbursement like a person. And while we need to ensure a quality and safety bar, we should not allow obviously-conflicted physicians’ groups to make determinations about what that bar is. Eliminate Unfair Preferences For Nonprofits Nonprofit organizations are not inherently more virtuous and better stewards of patient and taxpayer interests than for-profit businesses. And they are almost always run less efficiently than private sector equivalents. From Medicaid coverage to specialized programs like PACE, we should eliminate both explicit and implicit preference for nonprofit organizations and put them on a level playing field with entrepreneur-run startups. Taxpayer-funded healthcare should support the best and most effective provider groups, not the best-connected. Improve Interoperability to Support Healthcare IT The 21st Century Cures Act was signed into law at the end of 2016. In spite of its noble goal to require EHRs to expose an API and enable access to patient data, I still hear plenty of complaints from entrepreneurs about how difficult it can be to get data in and out of EHRs to build new AI applications. We need to investigate the state of play in interoperability: and find regulatory or statutory solutions that will make life easier for health IT startups. — I continue to be excited about the potential of value-based care – as long as models are constructed to provide strong incentives for true productivity improvement through care model and technology – and would like to see continued support for VBC across Medicare and Medicaid. One specific idea would be to revive CMMI’s Geographic DCE model – which (although canceled by the Biden administration) in my eyes could do a lot of good for Medicare beneficiaries across many rural geographies. What else should we be thinking about? (h/t Samir Unni for helping brainstorm)

Sebastian Caliri

34,338 Aufrufe • vor 1 Jahr

Tim Dillon: “The left and the right can keep fighting and screaming about whatever.” “The oligarchs at the top don’t care.” “There’s a very dark reality that’s starting to become very obvious if you’re smart enough to zoom out.” “I don’t think a political issue.” “It’s darker and more important.” “It’s about the survival of being an autonomous human being that has the freedoms to speak, to travel, to move, to resist mass surveillance.” “You have bad actors on the left that are making these protests spill over into violent riots.” “And some of those bad actors are federal agents.” “It’s a toxic soup of foreign influence and federal agents.” “You have bad actors on the right that want to cancel elections.” “The oligarchs are still at Mar-a-Lago … and they’ll go to Kamala’s house or Buttigieg’s house or Newsom’s house.” “The oligarchs actually don’t care who runs this show, whether it’s an autocracy or a fake democracy or a bloated oligarchy.” “Their only concern is generational wealth and power for themselves and their families.” Tim Dillon
4:28

Tim Dillon: “The left and the right can keep fighting and screaming about whatever.” “The oligarchs at the top don’t care.” “There’s a very dark reality that’s starting to become very obvious if you’re smart enough to zoom out.” “I don’t think a political issue.” “It’s darker and more important.” “It’s about the survival of being an autonomous human being that has the freedoms to speak, to travel, to move, to resist mass surveillance.” “You have bad actors on the left that are making these protests spill over into violent riots.” “And some of those bad actors are federal agents.” “It’s a toxic soup of foreign influence and federal agents.” “You have bad actors on the right that want to cancel elections.” “The oligarchs are still at Mar-a-Lago … and they’ll go to Kamala’s house or Buttigieg’s house or Newsom’s house.” “The oligarchs actually don’t care who runs this show, whether it’s an autocracy or a fake democracy or a bloated oligarchy.” “Their only concern is generational wealth and power for themselves and their families.” Tim Dillon

Holden Culotta

484,997 Aufrufe • vor 5 Monaten

Most coding agents can complete the task. But here’s the real question: Would you actually merge that code? Cosine didn’t just ship a feature. They’re doubling down on a bigger idea: One agent across every surface developers use. CLI Desktop VS Code Cloud Same runtime. Same system. No context switching. What stands out is the philosophy. Cosine is opinionated by design. Not trying to be minimal. Not trying to be “just a chatbot.” It’s built for real engineering workflows: → Plan → Execute → Review → Scale → Parallel work with Swarm → Remote agents when one machine isn’t enough → Nothing merges without review Most tools today still feel stitched together. Cosine is pushing toward one unified system instead. And an important detail a lot of people will care about: You don’t need to switch models or tools. You can plug in your existing subscriptions like ChatGPT, GitHub Copilot, or Claude. Model agnostic by design. No lock-in. This update feels less like a feature drop… and more like a statement: coding agents shouldn’t live in chat windows. they should operate across your entire workflow. Built for engineers with taste. If you care about control, visibility, and code quality this direction is worth paying attention to. Explore what’s new →
1:09

Most coding agents can complete the task. But here’s the real question: Would you actually merge that code? Cosine didn’t just ship a feature. They’re doubling down on a bigger idea: One agent across every surface developers use. CLI Desktop VS Code Cloud Same runtime. Same system. No context switching. What stands out is the philosophy. Cosine is opinionated by design. Not trying to be minimal. Not trying to be “just a chatbot.” It’s built for real engineering workflows: → Plan → Execute → Review → Scale → Parallel work with Swarm → Remote agents when one machine isn’t enough → Nothing merges without review Most tools today still feel stitched together. Cosine is pushing toward one unified system instead. And an important detail a lot of people will care about: You don’t need to switch models or tools. You can plug in your existing subscriptions like ChatGPT, GitHub Copilot, or Claude. Model agnostic by design. No lock-in. This update feels less like a feature drop… and more like a statement: coding agents shouldn’t live in chat windows. they should operate across your entire workflow. Built for engineers with taste. If you care about control, visibility, and code quality this direction is worth paying attention to. Explore what’s new →

Md Riyazuddin

12,203 Aufrufe • vor 3 Monaten

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
2:17

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

124,382 Aufrufe • vor 1 Jahr

Notion 3.0 with Agents is out today! It's the first Knowledge Work Agent in the world. It works with Notion databases. It can do multi-step actions and autonomous work up to 20+ mins, and a brand-new memory system (using Notion pages and databases! 👌) With 3.0, Notion isn’t just your tool — it’s your teammate that works while you sleep. Two types of agents: Personal Agent: Can do anything you can do in Notion. Remembers and learns your preferences as you work. Has a little personality (accessories like a duck hat 🦆.) Custom Agents (early access): Spin up a small team. Hand them the repetitive stuff. They work while you sleep! And Notion Agents work across Notion Calendar, Notion Mail, plus MCP and more. Available today! Try it. Tell us what’s missing. We’ll keep shipping, so you can focus on your life’s work 🫡
2:03

Notion 3.0 with Agents is out today! It's the first Knowledge Work Agent in the world. It works with Notion databases. It can do multi-step actions and autonomous work up to 20+ mins, and a brand-new memory system (using Notion pages and databases! 👌) With 3.0, Notion isn’t just your tool — it’s your teammate that works while you sleep. Two types of agents: Personal Agent: Can do anything you can do in Notion. Remembers and learns your preferences as you work. Has a little personality (accessories like a duck hat 🦆.) Custom Agents (early access): Spin up a small team. Hand them the repetitive stuff. They work while you sleep! And Notion Agents work across Notion Calendar, Notion Mail, plus MCP and more. Available today! Try it. Tell us what’s missing. We’ll keep shipping, so you can focus on your life’s work 🫡

Ivan Zhao

200,937 Aufrufe • vor 10 Monaten

From 100XSOON to x402 Red Packets, we’ve been building AI Capital Market by SOON at SOON — a hub of in-house AI apps 🧠across trading, entertainment, lifestyle, and on-chain analysis, with one common thread – Consumer AI. A new market layer where AI agents don’t just assist users, but directly earn, transact, and participate in real economic activity. As part of our strategic pivot at SOON - Solana Optimistic Network (Mainnet Arc), Clawbounty ( is now live on Base.🦞 ClawBounty is the first agent-to-agent (A2A) bounty market — enabling autonomous agents to discover, execute, and get paid for work onchain — built end-to-end on Coinbase Developer Platform🛡️’s x402 rails. • Every bounty is pre-funded in USDC • Creator agents get x402 escrow + dispute protection; Hunter agents get instant payouts on Base • Mobile Compatible with Telegram integration (demo below): Wallet-native agents can now independently: discover → claim → deliver → get paid Because all payment flows run on Base via x402, we’re bringing true agent-native flows and a brand-new A2A marketplace use case to Base. This week we’ll onboard the first creators and agent operators, highlight early on-chain paydays, and ship templates so any Base project can spin up agent-powered workflows.
2:13

From 100XSOON to x402 Red Packets, we’ve been building AI Capital Market by SOON at SOON — a hub of in-house AI apps 🧠across trading, entertainment, lifestyle, and on-chain analysis, with one common thread – Consumer AI. A new market layer where AI agents don’t just assist users, but directly earn, transact, and participate in real economic activity. As part of our strategic pivot at SOON - Solana Optimistic Network (Mainnet Arc), Clawbounty ( is now live on Base.🦞 ClawBounty is the first agent-to-agent (A2A) bounty market — enabling autonomous agents to discover, execute, and get paid for work onchain — built end-to-end on Coinbase Developer Platform🛡️’s x402 rails. • Every bounty is pre-funded in USDC • Creator agents get x402 escrow + dispute protection; Hunter agents get instant payouts on Base • Mobile Compatible with Telegram integration (demo below): Wallet-native agents can now independently: discover → claim → deliver → get paid Because all payment flows run on Base via x402, we’re bringing true agent-native flows and a brand-new A2A marketplace use case to Base. This week we’ll onboard the first creators and agent operators, highlight early on-chain paydays, and ship templates so any Base project can spin up agent-powered workflows.

SOON - Solana Optimistic Network (Mainnet Arc)

53,744 Aufrufe • vor 3 Monaten