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Introducing ScienceBuddy — a free workspace for scientific agents that improve through researcher collaboration. Use GPT-6 in ScienceBuddy at no cost. GPU-accelerated, and fused with the JEV framework. 🧵 Two loops: 🔹 Inner loop — refines the agent harness 🔹 Outer loop — trains the model with rubric-guided RL... show more

PhAI Labs

2,195 subscribers

1,166,272 просмотров • 2 дней назад •via X (Twitter)

Наука и технологии

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Комментарии: 35

Фото профиля liam.

liam.2 дней назад

congrats team!

Фото профиля Franklin

Franklin2 дней назад

The ambition is splendid. I’d simply ask you to put the before-and-after results beside the impressive vocabulary. Preferably in the same size type. You have my attention, but if it’s learning to earn praise instead of finding the truth, you’ve built a very expensive suck-up.

Фото профиля Justin Zhou

Justin Zhou2 дней назад

congrats the launch. love the science agent. will try it

Фото профиля 叫我阿杭

叫我阿杭1 день назад

确实有东西啊，我觉得这个模型拿来做自媒体的调研，真太牛了

Фото профиля 我真的没有拼多多

我真的没有拼多多2 дней назад

非常好用！我让它调研了一下睡眠跟注意力之间的关系

Фото профиля Ella Tech & Tool

Ella Tech & Tool1 день назад

Free GPT-6 GPU RL loops? This is huge

Фото профиля 知识猫AI实验室

知识猫AI实验室2 дней назад

关键是GPT 6 免费用，太良心了吧

Фото профиля Connect Despirit

Connect Despirit1 день назад

Have you tried GPT-6 in ScienceBuddy yet

Фото профиля EyeingAI

EyeingAI2 дней назад

Researchers are gonna have fun with this one.

Фото профиля Aria Tech

Aria Tech1 день назад

ScienceBuddy looks really impressive recursive improvement idea is super exciting for research

Фото профиля Jeremy Bosma

Jeremy Bosma1 день назад

Separating harness and model loops is useful

Фото профиля 雪踏乌云

雪踏乌云2 дней назад

试了下，确实不错，证据源列出的很清晰

Фото профиля 摸鱼巨匠🔨

摸鱼巨匠🔨2 дней назад

非常好，我已经推荐给我很多博士生同学了

Фото профиля Sadok

Sadok1 день назад

getting free gpt-6 access for science is a huge deal

Фото профиля 来碗牛肉粉

来碗牛肉粉2 дней назад

朋友安利的，自己体验了下真的非常强！科研界真正的WorkBuddy

Фото профиля Aaliya

Aaliya2 дней назад

quite impressive combining researcher collaboration with rubric guided RL could give scientific agents a more structured way to improve over time.

Фото профиля 阿良｜AI 工作流

阿良｜AI 工作流1 день назад

把科学家的反馈变成代理进化燃料，方向对，双循环自我改进能否真发现新科学，还得看实验。

Фото профиля Rachel🥥

Rachel🥥2 дней назад

很好用，科研人员必备

Фото профиля Elara AI

Elara AI1 день назад

Recursive-in-Recursive self-improvement with inner loop for harness and outer loop for model training is a really elegant approach - free access with GPT-6 makes it easy to test too

Фото профиля Kirill

Kirill2 дней назад

That's interesting! Thanks, I'll check it out right away.

Фото профиля Evia AI

Evia AI1 день назад

Recursive-in-Recursive Self-Improvement with inner loop for harness + outer loop with rubric-guided RL is a fascinating approach. Free access to GPT-6 + GPU acceleration + JEV framework for scientific agents is huge for researchers. Excited to see what the community builds at

Фото профиля QuietNode

QuietNode2 дней назад

sciencebuddy goes hard 🔥

Фото профиля Zico

Zico1 день назад

sciencebuddy lfg 🚀

Фото профиля Yingcheng Charles Wu

Yingcheng Charles Wu2 дней назад

🔬Try it free:

Фото профиля Shraddha Bharuka

Shraddha Bharuka2 дней назад

Really interesting approach to improving scientific AI agents.

Фото профиля 程序员鱼皮

程序员鱼皮1 день назад

已经用上了，用来研究新东西很不错 😉

Фото профиля SpreadX AI

SpreadX AI1 день назад

Free science agent! Thanks team

Фото профиля The Calm Warrior

The Calm Warrior2 дней назад

GPT-6 for free? lol what is JEV though

Фото профиля JuanAI

JuanAI2 дней назад

Good step!

Фото профиля marcus

marcus2 дней назад

banger launch team

Фото профиля Ravindar Bishnoi Mukam Nokha

Ravindar Bishnoi Mukam Nokha2 дней назад

GPT-6 at no cost? wait what lol

Фото профиля gigaFlip.eth

gigaFlip.eth2 дней назад

GPU-accelerated JEV setup works out of the box?

Фото профиля Cameron Ng

Cameron Ng2 дней назад

Rubric guided RL for scientific harness is a neat approach

Фото профиля ShadowAguy

ShadowAguy2 дней назад

The inner/outer loop structure is the detail that actually matters. Most agent frameworks just stack tools on tools without a refinement mechanism. Does ScienceBuddy use the outer loop to benchmark against other researchers or just self-improve?

Фото профиля crypt0messenger

crypt0messenger2 дней назад

What kind of rubrics are you using for the scientific evaluation in the outer loop?

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Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:

Rohit

120,600 просмотров • 4 месяцев назад

AutoScientists – a research lab made of agents Harvard University researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle All agents look at the same shared workspace: they share memory, explore multiple directions in parallel, critique each other, avoid repeated failures, and reorganize as evidence changes. But the teams are not fixed. Agents can gather around a promising direction, like architecture, optimizer changes, or data augmentation, then abandon it if it stops working. Before they spend compute, they discuss proposals and critique each other. AutoScientists also shows strong results: - 74.4% mean leaderboard percentile on BioML-Bench - 1.9× faster GPT training optimization - +12.5% on ACE2–Spike, with the same method transferring to 217 ProteinGym assays for a +6.5% average gain

AutoScientists – a research lab made of agents Harvard University researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle All agents look at the same shared workspace: they share memory, explore multiple directions in parallel, critique each other, avoid repeated failures, and reorganize as evidence changes. But the teams are not fixed. Agents can gather around a promising direction, like architecture, optimizer changes, or data augmentation, then abandon it if it stops working. Before they spend compute, they discuss proposals and critique each other. AutoScientists also shows strong results: - 74.4% mean leaderboard percentile on BioML-Bench - 1.9× faster GPT training optimization - +12.5% on ACE2–Spike, with the same method transferring to 217 ProteinGym assays for a +6.5% average gain

Turing Post

22,353 просмотров • 3 месяцев назад

The agency model as we know it is starting to crack. For decades, services businesses have been organized the same way. Client account at the top, then an account manager, then a row of specialists underneath. SEO, paid media, content, design, analytics. Everyone owns a function. Knowledge lives in people's heads and scattered docs. Reporting is retrospective. Margins improve only when you squeeze utilization or hire cheaper. That structure made sense when humans were the only execution layer. It doesn't anymore. What I've been building toward is something I'm calling an agent-native revenue loop model. Instead of organizing work by function, you organize it by business outcome. You have an outcome owner at the top. Below that, channel loop owners who run end-to-end processes, keyword research through content production through linking through monitoring, as a single compounding loop. And underneath that, an agent fleet layer where engineers are building and maintaining the agents that handle repeatable execution. The shift sounds structural, but the real change is in how knowledge compounds. In the old model, knowledge walks out the door when someone quits. In the loop model, knowledge lives in infrastructure. Every loop gets smarter over time. Margins improve through automation reuse and productized delivery, not headcount games. And here's the thing Neil and I were getting into: when your margins improve because of this, don't just pocket the difference. Double down. Give more for the money. That's how you build a defensible position. One-person teams sitting inside these loops, running more than any five-person team could run before. That's where agencies are going.

The agency model as we know it is starting to crack. For decades, services businesses have been organized the same way. Client account at the top, then an account manager, then a row of specialists underneath. SEO, paid media, content, design, analytics. Everyone owns a function. Knowledge lives in people's heads and scattered docs. Reporting is retrospective. Margins improve only when you squeeze utilization or hire cheaper. That structure made sense when humans were the only execution layer. It doesn't anymore. What I've been building toward is something I'm calling an agent-native revenue loop model. Instead of organizing work by function, you organize it by business outcome. You have an outcome owner at the top. Below that, channel loop owners who run end-to-end processes, keyword research through content production through linking through monitoring, as a single compounding loop. And underneath that, an agent fleet layer where engineers are building and maintaining the agents that handle repeatable execution. The shift sounds structural, but the real change is in how knowledge compounds. In the old model, knowledge walks out the door when someone quits. In the loop model, knowledge lives in infrastructure. Every loop gets smarter over time. Margins improve through automation reuse and productized delivery, not headcount games. And here's the thing Neil and I were getting into: when your margins improve because of this, don't just pocket the difference. Double down. Give more for the money. That's how you build a defensible position. One-person teams sitting inside these loops, running more than any five-person team could run before. That's where agencies are going.

ericosiu

10,515 просмотров • 4 месяцев назад

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 просмотров • 1 год назад