ari
1.6K posts

ari
@Ari
technophile data junkie, believer in Jedi powers ✨ 🧠 designing products that improve the human story. OG. Posts deleted via SIM swap often...




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 :)






Opal, our no-code visual builder for AI workflows, just got a major upgrade. 🧠💎 We’ve added a new agent step that analyzes your goal, determines the best approach, and automatically calls the right tools — such as Veo for video or web search for research — to complete the task. We’re also adding new tools to make the agent even more capable: 💾 Memory – Remember info, like a user’s name or your style preferences across sessions. 🚀 Dynamic Routing – Let the agent choose the next best step using the “@ Go to” tool. 💬 Interactive Chat – Initiate user interactions to gather missing information or present options before moving on. Try it now → opal.google






Moya, a newly unveiled biomimetic humanoid robot, is designed to replicate human movement with 92% reported accuracy. It may serve as a daily companion or in healthcare and education.






Goldman Sachs is rolling out Anthropic’s AI model to automate accounting and compliance roles completely. Anthropic engineers have been embedded at Goldman for 6 months, co-developing systems that act like “digital co-workers” for high-volume, process-heavy tasks. The new setup uses an LLM-based agent that can read large bundles of trade records and policy text, then follow step-by-step rules to decide what to do, what to flag, and what to route for approval. Goldman says the surprise was that Claude’s capability was not limited to coding, and that the same reasoning style worked for rules-based accounting and compliance work that mixes text, tables, and exceptions. The bank expects shorter cycle times for client vetting and fewer lingering breaks in trade reconciliation, and slower headcount growth rather than immediate layoffs. --- cnbc .com/2026/02/06/anthropic-goldman-sachs-ai-model-accounting.html












