Ground Zero

The @tensorqt episode is here. This is special. Just being terminally online on this app - post ideas, meet mutuals, raise funds, and build a frontier company. This has been Francesco's journey building infra for autonomous research at Paradigma. 0:00:00 - INTRO 0:01:03 - Dropping out of PhD, Saturation in Academia, Building from EU 0:13:15 - Autoresearch, Idea behind Flywheel, ex-OpenAI Co-founder 0:19:18 - Evals for Autoreserch and Research Taste 0:25:00 - How a Research Lab uses Flywheel? 0:31:15 - Thesis and Autoresearch Adoption in Top 5 Labs 0:41:00 - Working around with Flywheel, Combinatorial Explosion 1:02:03 - Science as Public Graph, Deriving New Research Loop, Quick Fire 1:06:23 - Toughest Autoresearch Challenge as of Today, Future Directions 1:11:53 - Automating Science: Sakana and Deepmind, Trust Problem? 1:21:07 - Business Model, Automating R&D, Model Capabilities 1:27:20 - Dropout of college to live on frontier!

The Never Ending Lore of Harness⚡️ In our upcoming episode, we are hosting @Vtrivedy10 (Product Lead, Langchain). Viv is working on open source agents & harnesses and been posting incredible takes / articles on autonomous agents, evals and harnesses. Drop your questions for him on anything above + AI in general.

The @tensorqt episode is here. This is special. Just being terminally online on this app - post ideas, meet mutuals, raise funds, and build a frontier company. This has been Francesco's journey building infra for autonomous research at Paradigma. 0:00:00 - INTRO 0:01:03 - Dropping out of PhD, Saturation in Academia, Building from EU 0:13:15 - Autoresearch, Idea behind Flywheel, ex-OpenAI Co-founder 0:19:18 - Evals for Autoreserch and Research Taste 0:25:00 - How a Research Lab uses Flywheel? 0:31:15 - Thesis and Autoresearch Adoption in Top 5 Labs 0:41:00 - Working around with Flywheel, Combinatorial Explosion 1:02:03 - Science as Public Graph, Deriving New Research Loop, Quick Fire 1:06:23 - Toughest Autoresearch Challenge as of Today, Future Directions 1:11:53 - Automating Science: Sakana and Deepmind, Trust Problem? 1:21:07 - Business Model, Automating R&D, Model Capabilities 1:27:20 - Dropout of college to live on frontier!

The Paradigm of Autonomous Research⚡️ In our upcoming episode, we are hosting @tensorqt from Paradigma. They are building infra for autonomous research (Flywheel) which treats knowledge as a directed-acyclic-graph over flat doc. Drop your questions around auto-research, flywheels, AI agents for science + everything around.

The Arcee AI Podcast is here! In this episode, @latkins and @stochasticchasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo

The Arcee AI Podcast is here! In this episode, @latkins and @stochasticchasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo

The Arcee AI Podcast is here! In this episode, @latkins and @stochasticchasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo

The Arcee AI Podcast is here! In this episode, @latkins and @stochasticchasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo

The Arcee AI Podcast is here! In this episode, @latkins and @stochasticchasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo

The Arcee AI Podcast is here! In this episode, @latkins and @stochasticchasm join us to discuss the story of Trinity models and everything frontier. I can say, this talk has been one of the most amazing and technical conversations we've had on Ground Zero. 0:00:00 - Intro 0:00:59 - Varun's transition from SWE to Pre-Training Lead 0:04:20 - Trinity Manifesto, Openclaw Ecosystem 0:12:15 - Arcee's Post-Training to Pre-Training Pivot 0:23:45 - Varun's first Pre-Training run (you can just do things!) 0:27:33 - Saturation in Pre-Training?, Mid-Training 0:37:00 - Tweaking the Training Architecture, Adam vs Muon, Evals 01:09:07 - Inference Engineering, Quick Fire, Post-Training Recipe 01:18:02 - Alpha in RL Envs, Harness Design 01:23:00 - American Open Source is trailing Chinese Competitors, Trinity Adoption 01:29:25 - Hiring at Arcee, Advice to 20yo

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autor… Part code, part sci-fi, and a pinch of psychosis :)