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two things ready to share from this weekend: 📖 interactive site teaching activegraph concepts blog: 💻 AG coder (open source) reference coding agent on activegraph GH: blog: still in progress: deep research agent, bounded self-improvement research, core packs...

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Yohei

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11,140 просмотров • 1 месяц назад •via X (Twitter)

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Antigravity can do more than ship code and you don’t even have to leave your editor. In this demo, the agent reads a blog post, extracts the core narrative, and builds a Google Slides deck from scratch, handling the research and initial build for you.
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Antigravity can do more than ship code and you don’t even have to leave your editor. In this demo, the agent reads a blog post, extracts the core narrative, and builds a Google Slides deck from scratch, handling the research and initial build for you.

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We are excited to share a research preview of our generative agent. The agent is being trained to solve the hardest tasks in 3D and beyond, using only keyboard and mouse actions. Join the waitlist: Our agent app runs on Windows or Mac, either locally or with one-click setup for a Windows VM. It’s still early days, but this paves the way for production-level workflows for the first time ever. Blog:

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This is the most insane application of MCP I've used. Context is king! Your coding agents thrive on high-quality context. Qodo Aware is a production-ready deep research agent that solves this. This is huge! Watch how I use it in Codex to retrieve context from large codebases.
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This is the most insane application of MCP I've used. Context is king! Your coding agents thrive on high-quality context. Qodo Aware is a production-ready deep research agent that solves this. This is huge! Watch how I use it in Codex to retrieve context from large codebases.

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🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️
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🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

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Open-sourcing Introspect: MIT-licensed Deep-Research for your internal data! Works with spreadsheets, databases, PDFs, and web search. Has a remarkably simple architecture – Sonnet agent armed with recursive tool calling and 3 default tools. Best for use-cases where you want to combine insights from SQL with unstructured data + data from the web. Launching a bunch of products built on top of of this soon, but wanted to open-source our core engine to give back to the community! 2 min demo below, links in comments

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71,512 просмотров • 1 год назад

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Excited to launch "Novix"🚀, our PhD-level AI-Scientist designed for autonomous scientific discovery. Novix revolutionizes research workflows through comprehensive capabilities spanning: deep research, innovative ideation, intelligent coding, advanced data analysis, automated experimentation, and paper writing. 🌐 Platform Access: 👉 Open-Source Foundation: 🚀 Accelerated Scientific Discovery Pipeline: From concept to publication-ready research with unprecedented efficiency ✨ Core Capabilities: - 🧠 Research Co-Pilot Intelligence: AI-powered ideation and hypothesis generation that collaborates with your research intuition - ⚙️ Autonomous Algorithm Innovation: End-to-end design, implementation, and validation of novel computational approaches - 📊 Intelligent Data Orchestration: Advanced analytics with automated insights discovery and compelling visualizations - 🔬 Scientific Reproducibility Engine: Automated verification and replication of research methodologies and findings - 📚 AI-Powered Deep Survey: Comprehensive literature synthesis and gap analysis across scientific domains We're building an AGI Level 4 innovation engine that empowers researchers, developers, and businesses to achieve breakthrough results in scientific innovation and discovery. From our open-source foundation to this production-ready platform, Novix represents a paradigm shift in how we reshape scientific discovery. 🎁 Launch Benefits - 🚪 Barrier-Free Access: Simply register and start exploring - 💰 Welcome Bonus: New users receive $5 in credits to experience the platform's full potential - 🎯 Enhanced Experience: Complete our user feedback survey to unlock a $20 Pro account with complete feature access We deeply understand the challenges of research work and genuinely hope Novix can serve as your trusted research companion. Join us in this exciting journey of AI-powered scientific discovery and help shape the future of research innovation!

Chao Huang

16,854 просмотров • 10 месяцев назад

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The Genesis Mission calls for new ways to accelerate scientific discovery. This is our contribution Multimodal search across 25M papers is a step toward science discovery that moves at the speed of curiosity. Releasing, - Visually indexed scientific paper dataset with open access 25M papers, 450M+ visually indexed pages. Total 175TB+. All on Deep Lake - Open-source scientific data agent that achieves 48% SOTA on Humanity's Last Exam with tools including the indexed scientific research dataset. Excited to see what discoveries you all uncover with this. Try it and share your most interesting findings.

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From lab to open-source: A new milestone for AI-driven education. 🎓 🤗 We’ve been closely following the MAIC project at Tsinghua University, and we’re thrilled to see it now open-sourced as #OpenMAIC. ✨ This isn't just another chatbot; it takes Multi-Agent orchestration to the next level by building a fully interactive classroom where AI instructors and peers collaborate in real-time. What makes it technically impressive: 🛠️ Complex Orchestration: Leveraging #LangGraph to manage spontaneous interactions—like #AI students "raising hands" during a live lecture. 🧠 Structured Planning: A dedicated "Plan Agent" that transforms raw PDFs into coherent, logically sequenced pedagogical flows. 💻 Beyond Text: A masterclass in GenUI implementation, featuring synchronized TTS, laser pointers, and real-time whiteboard demonstrations. 🥳 If you’re building complex, multi-modal #Agent workflows, this repo is a treasure trove of engineering insights. 🖥️Explore the project: 📰 Read the research:

OpenBMB

152,569 просмотров • 4 месяцев назад

Introducing ALE-Bench, ALE-Agent! Towards Automating Long-Horizon Algorithm Engineering for Hard Optimization Problems Blog: Paper: ALE-Bench is a coding benchmark primarily focused on hard optimization (NP-hard) problems. We developed this benchmark with AtCoder Inc., a leading coding contest platform company. What makes ALE-Bench unique is its focus on hard optimization problems that demand long-horizon and creative reasoning. It’s open-ended, in the sense that true optima are out of reach (NP-hard) and scores can continuously improve. We believe this benchmark has the potential to become one of the key benchmarks for reasoning and coding in the next generation. ALE-Agent is our end-to-end agent that we specifically designed for this challenging domain. In fact, our ALE-Agent has already built an impressive track record in the wild! In May 2025, our agent participated in a live AtCoder Heuristic Competition (AHC), alongside 1,000 other participants in real-time. AHC is considered to be one of the most challenging coding competitions in this domain. Our ALE-Agent achieved an impressive ranking of 21st out of 1,000 human participants in the competition (top 2%), marking a turning point for AI discovery of solutions to hard optimization problems with a wide spectrum of important real world applications such as logistics, routing, packing, factory production planning, power-grid balancing. We look forward to applying this technology to real industrial optimization opportunities. Building on the insights from this study, Sakana AI will continue to tackle the challenge of developing AI with even greater algorithm engineering capabilities. ALE-Bench Dataset: ALE-Bench Code: This research was conducted in collaboration with AtCoder Inc. (AtCoder). We are deeply grateful for their outstanding expertise and contributions in optimization and algorithms, which were invaluable in providing data, analyzing results, and enabling our AI agent’s participation in their contests.
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Introducing ALE-Bench, ALE-Agent! Towards Automating Long-Horizon Algorithm Engineering for Hard Optimization Problems Blog: Paper: ALE-Bench is a coding benchmark primarily focused on hard optimization (NP-hard) problems. We developed this benchmark with AtCoder Inc., a leading coding contest platform company. What makes ALE-Bench unique is its focus on hard optimization problems that demand long-horizon and creative reasoning. It’s open-ended, in the sense that true optima are out of reach (NP-hard) and scores can continuously improve. We believe this benchmark has the potential to become one of the key benchmarks for reasoning and coding in the next generation. ALE-Agent is our end-to-end agent that we specifically designed for this challenging domain. In fact, our ALE-Agent has already built an impressive track record in the wild! In May 2025, our agent participated in a live AtCoder Heuristic Competition (AHC), alongside 1,000 other participants in real-time. AHC is considered to be one of the most challenging coding competitions in this domain. Our ALE-Agent achieved an impressive ranking of 21st out of 1,000 human participants in the competition (top 2%), marking a turning point for AI discovery of solutions to hard optimization problems with a wide spectrum of important real world applications such as logistics, routing, packing, factory production planning, power-grid balancing. We look forward to applying this technology to real industrial optimization opportunities. Building on the insights from this study, Sakana AI will continue to tackle the challenge of developing AI with even greater algorithm engineering capabilities. ALE-Bench Dataset: ALE-Bench Code: This research was conducted in collaboration with AtCoder Inc. (AtCoder). We are deeply grateful for their outstanding expertise and contributions in optimization and algorithms, which were invaluable in providing data, analyzing results, and enabling our AI agent’s participation in their contests.

Sakana AI

237,195 просмотров • 1 год назад

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:
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🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

Swami Sivasubramanian

28,946 просмотров • 1 год назад

What if instead of autoresearch reflecting incremental progress from a single person, it reflected *hundreds* of researchers’ progress, updated live, with every run’s data point being interactive and reproducible? You could survey the full space of explored ideas, see which changes actually move the benchmark, and fold the most promising ideas into your next run. Labless ( is my attempt at this. It can support thousands of simultaneous training runs across contributors around the world, each learning & improving together to hillclimb on a fixed benchmark, validated using a standardized codebase scaffold. Importantly, all valid training runs auto-submit to Labless, so failed experiments are as visible and reproducible as the successes. We have an agentic API that lets you or your coding agent study every run submitted so far so you don't waste time investigating something someone already tried, and you can source novel ideas from other contributors. Labless already hosts our Nanopath challenge to build the best pathology foundation model that trains in 1 hour on a single GPU. We are hoping to expand the platform further and support a suite of open-source research challenges. If this resonates with you, if you think the path to research innovation lies in open-source collaborative hill-climbing, please reach out.
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What if instead of autoresearch reflecting incremental progress from a single person, it reflected *hundreds* of researchers’ progress, updated live, with every run’s data point being interactive and reproducible? You could survey the full space of explored ideas, see which changes actually move the benchmark, and fold the most promising ideas into your next run. Labless ( is my attempt at this. It can support thousands of simultaneous training runs across contributors around the world, each learning & improving together to hillclimb on a fixed benchmark, validated using a standardized codebase scaffold. Importantly, all valid training runs auto-submit to Labless, so failed experiments are as visible and reproducible as the successes. We have an agentic API that lets you or your coding agent study every run submitted so far so you don't waste time investigating something someone already tried, and you can source novel ideas from other contributors. Labless already hosts our Nanopath challenge to build the best pathology foundation model that trains in 1 hour on a single GPU. We are hoping to expand the platform further and support a suite of open-source research challenges. If this resonates with you, if you think the path to research innovation lies in open-source collaborative hill-climbing, please reach out.

Paul Scotti @ ICML

22,075 просмотров • 16 дней назад

Congrats to the Kimi.ai team! This is awesome. Great to see this level of research coming from open-source frontier model labs. I liked the paper so much I built a Rust implementation of it ;) Full AttnRes + Block AttnRes with two-phase inference, built using Burn (tensor library and Deep Learning Framework, in Rust, by Tracel AI). Runs on CPU, CUDA, Metal, wgpu. Includes an interactive TUI that trains a model live and visualizes depth attention evolving from uniform to selective in real time. Repo link and more on what is implemented in the comments.
0:43

Congrats to the Kimi.ai team! This is awesome. Great to see this level of research coming from open-source frontier model labs. I liked the paper so much I built a Rust implementation of it ;) Full AttnRes + Block AttnRes with two-phase inference, built using Burn (tensor library and Deep Learning Framework, in Rust, by Tracel AI). Runs on CPU, CUDA, Metal, wgpu. Includes an interactive TUI that trains a model live and visualizes depth attention evolving from uniform to selective in real time. Repo link and more on what is implemented in the comments.

abdel

95,237 просмотров • 4 месяцев назад

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.
1:21

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.

Guohao Li 🐫

223,208 просмотров • 1 год назад

My Researcher Agent - {Xinstein} was a HIT. It is performing very well. But now I want to take Xinstein to the next level. I am implementing the "Chain of Density" Summarization Technique. To gather the best context for the Agent to explain the concept even better. Here are a few ideas to build your next AI Agent in ChatGPT: 1. Add Writer Agent prompt structure after the summarization step. And you'll have your Blog Writer Agent. 2. Add "Doc Maker A+" Plugin in the end to save all your Research in Google Doc or PDF format. 3. Wait till DALL-E 3 and you'll be able to design infographics for your research, posts, blogs. Xinstein was 1 of 100 weird experiments I did with ChatGPT. Results: I think I have found a technique that erases hallucinations & assumptions from AI Agents. I am experimenting with this technique. Once I'll format it correctly, I might publish a paper on that. I call it the "Agent Benchmark Technique." This week was crazy! ✦ I have cracked the best offer for AI Automation Agency. ✦ I got solid breakthrough with HypeGenius. ✦ I got invited to speak in one of the biggest LLM Firm. ✦ I got another viral post after 25 days of gap. ✦ 100 days left in New Year: I have an exciting ANNOUNCEMENT for AI Hustlers, I will share that in Monday's Newsletter Edition. If you haven't tried Xinstein Agent in ChatGPT, try it. It'll sort your research with just ONE prompt. Here's a link: --- That's a wrap for today. Get Ready for Monday's Newsletter Edition. *Something coming to push you forward and make you take Action! Follow me CJ for daily AI breakdowns and Crazy AI experiments. Peace.
1:24

My Researcher Agent - {Xinstein} was a HIT. It is performing very well. But now I want to take Xinstein to the next level. I am implementing the "Chain of Density" Summarization Technique. To gather the best context for the Agent to explain the concept even better. Here are a few ideas to build your next AI Agent in ChatGPT: 1. Add Writer Agent prompt structure after the summarization step. And you'll have your Blog Writer Agent. 2. Add "Doc Maker A+" Plugin in the end to save all your Research in Google Doc or PDF format. 3. Wait till DALL-E 3 and you'll be able to design infographics for your research, posts, blogs. Xinstein was 1 of 100 weird experiments I did with ChatGPT. Results: I think I have found a technique that erases hallucinations & assumptions from AI Agents. I am experimenting with this technique. Once I'll format it correctly, I might publish a paper on that. I call it the "Agent Benchmark Technique." This week was crazy! ✦ I have cracked the best offer for AI Automation Agency. ✦ I got solid breakthrough with HypeGenius. ✦ I got invited to speak in one of the biggest LLM Firm. ✦ I got another viral post after 25 days of gap. ✦ 100 days left in New Year: I have an exciting ANNOUNCEMENT for AI Hustlers, I will share that in Monday's Newsletter Edition. If you haven't tried Xinstein Agent in ChatGPT, try it. It'll sort your research with just ONE prompt. Here's a link: --- That's a wrap for today. Get Ready for Monday's Newsletter Edition. *Something coming to push you forward and make you take Action! Follow me CJ for daily AI breakdowns and Crazy AI experiments. Peace.

CJ Zafir

44,626 просмотров • 2 лет назад

NEW: Harvey Co-Founder + Head of Applied Research on the *Token Reckoning* Valued at $11B, Harvey is on a mission to win the entire legal category, competing head-on against the trillion-dollar labs Coding agents hit Karpathy's "agents work now" inflection in late 2025. Harvey Co-Founder Gabe Pereyra (fmr Google Brain, DeepMind & Meta) argues legal is hitting its version of that curve right now. With both Gabe + Head of Applied Research Niko, we cover: - Open-sourcing LAB (legal agent benchmark): 1,200+ tasks across 24 practice areas, 75,000+ rubric criteria - Who's leading the leaderboard - Harvey is the largest embeddings consumer for some of the labs - Why every law firm has to be multi-model: conflict risk - The billable hour is coming back, this time for AI tokens FYI: Harvey Labs is the internal research group pushing the frontier of legal AI. Run by Niko (fmr multi-agent RL at Google Brain) & Julio Pereyra (fmr clerk + Big Law attorney), it partners with the labs, research community, & academia to bring frontier agent research into Harvey. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Gabe Pereyra (Co-Founder) & Niko Grupen (Head of Applied Research) (00:50) Inside Harvey's legal agent Benchmark (05:10) What happens after Benchmarking? (06:37) Why Harvey open sourced its research (09:21) Training models without client data (10:32) Google Brain vs. DeepMind (12:34) From Researcher to Founder (15:15) The Rise of the Inference Layer (18:38) The Agentic Shift (21:16) Harvey's 13 trillion tokens (23:48) AI's Biggest cost misconception (28:37) How Top AI founders learn (31:52) Learnings from Jensen Huang (34:14) How Harvey finds talent (35:41) Niko on Harvey's breakthroughs (36:38) Building a legal dataset from scratch (38:32) How to read AI Benchmarks (39:51) Niko's research playbook (40:51) The Opportunity beyond Benchmarks (41:45) Why Agent Harnesses matter (43:04) The Rise of Organizational AI
44:52

NEW: Harvey Co-Founder + Head of Applied Research on the *Token Reckoning* Valued at $11B, Harvey is on a mission to win the entire legal category, competing head-on against the trillion-dollar labs Coding agents hit Karpathy's "agents work now" inflection in late 2025. Harvey Co-Founder Gabe Pereyra (fmr Google Brain, DeepMind & Meta) argues legal is hitting its version of that curve right now. With both Gabe + Head of Applied Research Niko, we cover: - Open-sourcing LAB (legal agent benchmark): 1,200+ tasks across 24 practice areas, 75,000+ rubric criteria - Who's leading the leaderboard - Harvey is the largest embeddings consumer for some of the labs - Why every law firm has to be multi-model: conflict risk - The billable hour is coming back, this time for AI tokens FYI: Harvey Labs is the internal research group pushing the frontier of legal AI. Run by Niko (fmr multi-agent RL at Google Brain) & Julio Pereyra (fmr clerk + Big Law attorney), it partners with the labs, research community, & academia to bring frontier agent research into Harvey. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Gabe Pereyra (Co-Founder) & Niko Grupen (Head of Applied Research) (00:50) Inside Harvey's legal agent Benchmark (05:10) What happens after Benchmarking? (06:37) Why Harvey open sourced its research (09:21) Training models without client data (10:32) Google Brain vs. DeepMind (12:34) From Researcher to Founder (15:15) The Rise of the Inference Layer (18:38) The Agentic Shift (21:16) Harvey's 13 trillion tokens (23:48) AI's Biggest cost misconception (28:37) How Top AI founders learn (31:52) Learnings from Jensen Huang (34:14) How Harvey finds talent (35:41) Niko on Harvey's breakthroughs (36:38) Building a legal dataset from scratch (38:32) How to read AI Benchmarks (39:51) Niko's research playbook (40:51) The Opportunity beyond Benchmarks (41:45) Why Agent Harnesses matter (43:04) The Rise of Organizational AI

Molly O’Shea

86,280 просмотров • 27 дней назад

Download+Run agents in seconds with...curl??⚡️ Yup...Deep Agents are just folders so we can - package them up - download them - run them with just a couple commands & the deepagents-cli here I download an agent from our deepagents examples that comes with Skills for blogs/social media writing And in a minute, we get a full blog with our custom voice on the future of agentic coding 🤖 shoutout to Jonny Burger and remotion, edited this intro in minutes with their skill!!
0:55

Download+Run agents in seconds with...curl??⚡️ Yup...Deep Agents are just folders so we can - package them up - download them - run them with just a couple commands & the deepagents-cli here I download an agent from our deepagents examples that comes with Skills for blogs/social media writing And in a minute, we get a full blog with our custom voice on the future of agentic coding 🤖 shoutout to Jonny Burger and remotion, edited this intro in minutes with their skill!!

Viv

10,699 просмотров • 5 месяцев назад

Introducing EdgeBench, a benchmark designed to study how agents learn from environments over at least 12~72-hour runs. We find that performance follows a log-sigmoid function of environment interaction time with high precision. EdgeBench is built with three ingredients: - 🌍 Real & Diverse: 134 real-world tasks across 6 task categories, spanning scientific problems, professional knowledge work, software engineering, optimization, formal math, and games. - ⏳ Ultra-Long-Horizon: Each task supports 12–72 hours of agent work. Recorded human effort averages 57.2 hours. - 🔁 Informative Feedback: Agents receive real-world feedback for continuous improvement. After 38,000 hours of agent runs on EdgeBench, a scaling law for learning from environments emerges: - 📈 As agents interact with task environments over time, their aggregate performance is precisely fit by a log-sigmoid function. - 🧠 This phenomenon can be explained by an elegant theory of graph exploration. We are releasing an initial 51 of the 134 tasks, together with the full evaluation framework, to help advance long-horizon agent research. Check our blog & paper for more findings! Blog Paper GitHub Dataset Details below 👇🧵
0:52

Introducing EdgeBench, a benchmark designed to study how agents learn from environments over at least 12~72-hour runs. We find that performance follows a log-sigmoid function of environment interaction time with high precision. EdgeBench is built with three ingredients: - 🌍 Real & Diverse: 134 real-world tasks across 6 task categories, spanning scientific problems, professional knowledge work, software engineering, optimization, formal math, and games. - ⏳ Ultra-Long-Horizon: Each task supports 12–72 hours of agent work. Recorded human effort averages 57.2 hours. - 🔁 Informative Feedback: Agents receive real-world feedback for continuous improvement. After 38,000 hours of agent runs on EdgeBench, a scaling law for learning from environments emerges: - 📈 As agents interact with task environments over time, their aggregate performance is precisely fit by a log-sigmoid function. - 🧠 This phenomenon can be explained by an elegant theory of graph exploration. We are releasing an initial 51 of the 134 tasks, together with the full evaluation framework, to help advance long-horizon agent research. Check our blog & paper for more findings! Blog Paper GitHub Dataset Details below 👇🧵

Deyao Zhu

349,923 просмотров • 13 дней назад