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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...

Yohei

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11,140 views • 1 month ago •via X (Twitter)

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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.

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Guohao Li 🐫

223,208 views • 1 year ago

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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 views • 2 years ago

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

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 views • 27 days ago

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!!

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 views • 5 months ago

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 👇🧵

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 views • 13 days ago