After examining the new Sakana Fugu model, which, as they present it, rivals Fable 5, I want to comment that this is in itself not a new independent model, but an orchestrator trained to orchestrate several different models (e.g., Opus 4.8, GPT
AGENTS
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2015 prediction on AI assistants and agents
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2015 https://youtu.be/K5a1uthRHf8 Everything is there AI assistants, agents, expertise in your pocket etc…
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Fugu Ultra tested on 50 weeks of stock data
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Use Case 2: Financial Time Series Prediction
— Sakana AI (@SakanaAILabs) 22 juin 2026
Can an AI agent navigate sequential, no-look-ahead market decisions?
Just for fun, we tested Fugu Ultra on 50 weeks of historical data for an anonymized equity (STOCK_X). Starting with $10,000, the agent processes weekly market data… pic.twitter.com/BcqCfUfw2dUse Case 2: Financial Time Series Prediction Can an AI agent navigate sequential, no-look-ahead market decisions? Just for fun, we tested Fugu Ultra on 50 weeks of historical data for an anonymized equity (STOCK_X). Starting with $10,000, the agent processes weekly market data
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Autonomous ML Research: Fugu Ultra improves GPT model via AutoResearch
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Use Case 1: Autonomous ML Research
— Sakana AI (@SakanaAILabs) 22 juin 2026
Can an AI autonomously improve another AI’s training recipe?
We tasked Fugu Ultra with improving a small GPT model using AutoResearch. Over 14 hours on a single H100 GPU, Fugu ran > 100 experiments. It iteratively edited the training code, ran… pic.twitter.com/Gp96FEQ797Use Case 1: Autonomous ML Research Can an AI autonomously improve another AI’s training recipe? We tasked Fugu Ultra with improving a small GPT model using AutoResearch. Over 14 hours on a single H100 GPU, Fugu ran > 100 experiments. It iteratively edited the training code, ran
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That moment you realize a startup with zero employees exists
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That 'oh sh*t' moment when you realize the 0-person startup is freaking real: https://t.co/6kzwtaqdrK pic.twitter.com/9MP2fApaeb
— Charly Wargnier (@DataChaz) 22 juin 2026That 'oh sh*t' moment when you realize the 0-person startup is freaking real:
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Watch every session from Interrupt, the agent conference by LangChain
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Watch every session from Interrupt, the agent conference by LangChain.
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Play with SkyRL via OpenResearch.sh or autoarxiv with GLM 5.2
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4/4: You can play with this yourself! Visit http://
OpenResearch.sh (
http://
openresearch.sh) or change ‘arxiv’ to ‘autoarxiv’ on the official SkyRL paper https://
autoarxiv.org/abs/2511.16108 and use the GLM 5.2 model to iterate on the repo! -
GLM 5.2 agent ablation demos on continual learning papers
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2/4: We’ll be sharing a couple other fun and more complex demos this week where the GLM 5.2 agent conducts ablations on recent continual learning papers like SDPO. This can hopefully give you a sense of what these models can and cannot do when it comes to assisting in the
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SkyRL async RL training with autonomous research agent
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1/4: A couple notes on the implementation. The async RL training itself is powered by SkyRL, with the research agent’s goal being resolving setup issues (in this case a libnuma dependency) and analyzing runs autonomously.
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Sandboxes: serverless speed and persistent state
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Sandboxes must achieve 2 things at the same time… The speed of a serverless function. You cannot make an agent wait 2 minutes for a VM to start. It must offer the persistent state of a complete machine. Agents are not