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Join us at the MIT Media Lab for the ScienceClaw Hackathon, building the internet of agents for science. AI is becoming a collaborator in the real world - designing materials, creating instruments, running experiments, and connecting its capabilities with those of other agents. AI adapts as a problem unfolds...

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Markus J. Buehler

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26,536 görüntüleme • 6 gün önce •via X (Twitter)

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Markus J. Buehler profil fotoğrafı
Markus J. Buehler6 gün önce

Learn more and sign up:

Genius💡💹🧲 🤖 profil fotoğrafı
Genius💡💹🧲 🤖6 gün önce

no cap three days building agents with a small team sounds fun

The space of possibility profil fotoğrafı
The space of possibility6 gün önce

Thank you being one of the leaders in science

Markus J. Buehler profil fotoğrafı
Markus J. Buehler6 gün önce

🙏 we have a great team here!

Aban Mandal profil fotoğrafı
Aban Mandal5 gün önce

IMO agents should exchange versioned experiment traces, not just successful recipes Will ScienceClaw score systems on reuse of negative results, or only on the final working artifact?

Markus J. Buehler profil fotoğrafı
Markus J. Buehler5 gün önce

Yes that’s a key part of the approach

Schrodinger's Cockatoo profil fotoğrafı
Schrodinger's Cockatoo6 gün önce

Do hackers get access to any special robots, microelectronics or fab? (Like they did at hard mode?)

Markus J. Buehler profil fotoğrafı
Markus J. Buehler6 gün önce

We're working (and looking for) that and are interested in partnering with companies, academic labs, national labs, etc. - so hopefully there will be some resources available!! Stay tuned.

Schrodinger's Cockatoo profil fotoğrafı
Schrodinger's Cockatoo6 gün önce

Cool! How competitive do you think acceptance to the hackathon will be?

Saeed Anwar profil fotoğrafı
Saeed Anwar5 gün önce

Internet of agents for science is the right frame. What's the biggest coordination problem when multiple researcher agents share the same experimental environment?

TuTao profil fotoğrafı
TuTao6 gün önce

This is close to what I’m building: physics needs formulas and constraints to actually hold; chemistry needs molecular representations—especially SMILES—to become structure the model can use, not just text it can parrot. “One agent’s finding becomes another’s starting point” only matters if it hooks into simulators and lab tools. Curious how physics/chem tasks get split into shareable capabilities.

LYPHOX profil fotoğrafı
LYPHOX5 gün önce

The hard part of agent-run science isn't the hypothesis. It's reproducibility: instruments drift, runs get retried, and the record of what actually happened becomes the real deliverable.

Chen profil fotoğrafı
Chen6 gün önce

yeah. i keep underestimating how long the second half takes

Stanley Russell profil fotoğrafı
Stanley Russell5 gün önce

Researchers studying cryptocurrency economics often focus on unresolved challenges that limit real-world adoption under resource constraints.

Jenkins Ruben profil fotoğrafı
Jenkins Ruben5 gün önce

The long-term impact of blockchain architecture will depend on governance, regulation, and responsible innovation from a systems engineering perspective.

Austin Armstrong profil fotoğrafı
Austin Armstrong4 gün önce

Most 'agent stacks' are orchestration cosplay. Show me the KPI that moved.

Julian C profil fotoğrafı
Julian C4 gün önce

Connecting AIs as lab partners sounds nuts. Any robot drama yet?

DeNovoLabs profil fotoğrafı
DeNovoLabs5 gün önce

Agent loops compound at the rate of the slowest measurement in them. Protein and materials work runs on week-long cycles, so the instrument track is the interesting one. Are any of the instruments teams build meant to close a same-day loop?

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Santiago

230,647 görüntüleme • 1 yıl önce

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

127,724 görüntüleme • 1 yıl önce

🚀 Global Launch: Jarvix, Your Context OS is now live! Meet the next generation of personalized AI built around your context, your taste, and your way of thinking. #JarvixOS transforms how you work with AI by understanding your patterns, carrying your context across agents, and helping you move from ideas to action. Step into a new era of AI collaboration: • Discover your AI-native archetype • Build your personal AI model from your evolving AI interactions • Never start from zero - your context moves with you across sessions • Coordinate Codex, Claude Code, Gemini, Hermes, OpenClaw, and more as one connected AI team. • Turn insights into your next move, with you always in control Instead of adapting yourself to every agent, Jarvix carries the context you choose, helps you understand what it reveals, and brings the right agents together when you decide to act. Your AI tools shouldn’t work like separate assistants. They should work like one team. That’s the future of AI: Not more agents. One shared context. One Jarvix.
1:34

🚀 Global Launch: Jarvix, Your Context OS is now live! Meet the next generation of personalized AI built around your context, your taste, and your way of thinking. #JarvixOS transforms how you work with AI by understanding your patterns, carrying your context across agents, and helping you move from ideas to action. Step into a new era of AI collaboration: • Discover your AI-native archetype • Build your personal AI model from your evolving AI interactions • Never start from zero - your context moves with you across sessions • Coordinate Codex, Claude Code, Gemini, Hermes, OpenClaw, and more as one connected AI team. • Turn insights into your next move, with you always in control Instead of adapting yourself to every agent, Jarvix carries the context you choose, helps you understand what it reveals, and brings the right agents together when you decide to act. Your AI tools shouldn’t work like separate assistants. They should work like one team. That’s the future of AI: Not more agents. One shared context. One Jarvix.

Jarvix

1,690,718 görüntüleme • 1 ay önce

Greg Brockman on what solving Navier-Stokes says about the problems AI can take on next: "We actually used 10,000 agents to solve the Navier-Stokes problem." "It's both an important problem for what it is, has significant implications and applications to fluid dynamics, to how you think about ocean currents, all these things." "But for what it represents, of new knowledge created by AI and it unlocking a whole wave of scientific discovery, medicines, all of those things. They're on the table now." "There's something really amazing to think about what can happen through the power of AI that is able to really help solve problems." Greg Brockman benahorowitz.eth
0:50

Greg Brockman on what solving Navier-Stokes says about the problems AI can take on next: "We actually used 10,000 agents to solve the Navier-Stokes problem." "It's both an important problem for what it is, has significant implications and applications to fluid dynamics, to how you think about ocean currents, all these things." "But for what it represents, of new knowledge created by AI and it unlocking a whole wave of scientific discovery, medicines, all of those things. They're on the table now." "There's something really amazing to think about what can happen through the power of AI that is able to really help solve problems." Greg Brockman benahorowitz.eth

a16z

44,850 görüntüleme • 11 gün önce

Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on What starts as a prompt becomes a reusable unit of intelligence. As more agents are created and used, the system compounds - better agents, better outputs, more value across the network. This is how intelligence scales: not from a single model, but from a network of specialized agents.
1:43

Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on What starts as a prompt becomes a reusable unit of intelligence. As more agents are created and used, the system compounds - better agents, better outputs, more value across the network. This is how intelligence scales: not from a single model, but from a network of specialized agents.

OptimAI Network

12,708 görüntüleme • 5 ay önce

We're only year 3 of a decade (if not multi-decades) long transformation of work. 3 years ago we bet on building an horizontal platform for work with agents, a chance to invent a new operating system for companies, from scratch, with AI as a fundamental premise. Many people considered us crazy for going after that, praising verticalized AI products as the winning strategy. But here's the thing: the time horizon of tasks successfully handled by agents has been predictively increasing form minutes to hours and will in all likelihood reach the equivalent of days and weeks of human work equivalent in the coming quarters. This is were verticalized and/or single-player AI falls short. Single-player tools, one person, one agent, confined to your machine is the wrong architecture for what's coming. We're shifting from using AI to produce things, to managing fleets of agents that do the producing. 3 years ago I wrote[1]: "ChatGPT is the Pong of LLMs. [...] Imagine, one day we'll get the DOOM, Civ, Red Alert, and Counter Strike of LLMs. Let alone multiplayer modes." Weeks long tasks in companies are inherently collaborative and mechanically spanning multiple teams. The new bottleneck in harnessing agents within organizations is coordination: multiple humans and multiple agents need to work together, with shared context, shared tools, shared goals. Agents that can hand work off to other agents or surface decisions to the right person at the right time. Humans who can review, steer, and step in without losing the thread. Teams that can run parallel workstreams and actually stay aligned. This is Multiplayer AI, and that's what we've been building at Dust. Across Datadog, Clay, Persona, 1Password, Doctolib and 3,000+ organizations globally, we've watched teams figure out what this looks like in practice. 300,000+ agents deployed. 70% weekly active. 240%+ NRR. Today we're announcing a $40M Series B with Abstract, Sequoia, Snowflake, and Datadog to accelerate our vision. Designing the right interfaces for multiplayer AI is the next frontier. Join us to redefine work by defining multiplayer AI.
1:54

We're only year 3 of a decade (if not multi-decades) long transformation of work. 3 years ago we bet on building an horizontal platform for work with agents, a chance to invent a new operating system for companies, from scratch, with AI as a fundamental premise. Many people considered us crazy for going after that, praising verticalized AI products as the winning strategy. But here's the thing: the time horizon of tasks successfully handled by agents has been predictively increasing form minutes to hours and will in all likelihood reach the equivalent of days and weeks of human work equivalent in the coming quarters. This is were verticalized and/or single-player AI falls short. Single-player tools, one person, one agent, confined to your machine is the wrong architecture for what's coming. We're shifting from using AI to produce things, to managing fleets of agents that do the producing. 3 years ago I wrote[1]: "ChatGPT is the Pong of LLMs. [...] Imagine, one day we'll get the DOOM, Civ, Red Alert, and Counter Strike of LLMs. Let alone multiplayer modes." Weeks long tasks in companies are inherently collaborative and mechanically spanning multiple teams. The new bottleneck in harnessing agents within organizations is coordination: multiple humans and multiple agents need to work together, with shared context, shared tools, shared goals. Agents that can hand work off to other agents or surface decisions to the right person at the right time. Humans who can review, steer, and step in without losing the thread. Teams that can run parallel workstreams and actually stay aligned. This is Multiplayer AI, and that's what we've been building at Dust. Across Datadog, Clay, Persona, 1Password, Doctolib and 3,000+ organizations globally, we've watched teams figure out what this looks like in practice. 300,000+ agents deployed. 70% weekly active. 240%+ NRR. Today we're announcing a $40M Series B with Abstract, Sequoia, Snowflake, and Datadog to accelerate our vision. Designing the right interfaces for multiplayer AI is the next frontier. Join us to redefine work by defining multiplayer AI.

Stanislas Polu

3,206,609 görüntüleme • 4 ay önce

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.
8:17

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,666 görüntüleme • 3 ay önce