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
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AI generates precise mechanical iris design in CAD
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Use Case 4: Computer Aided Design of Mechanical Iris
— Sakana AI (@SakanaAILabs) 22 juin 2026
Can an AI generate precise, functional mechanical designs?
We tasked Fugu Ultra with creating a mechanical iris in CAD, similar to a camera aperture where multiple blades must move together to cleanly open and close a central… pic.twitter.com/Y27QDdcYipUse Case 4: Computer Aided Design of Mechanical Iris Can an AI generate precise, functional mechanical designs? We tasked Fugu Ultra with creating a mechanical iris in CAD, similar to a camera aperture where multiple blades must move together to cleanly open and close a central
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Free MIT guide to key concepts of computer vision
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A free MIT guide to key concepts of computer vision: https://bit.ly/43Tn1vW
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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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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 lacks image understanding; uses numpy for WandB charts
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3/4: One limitation worth noting: GLM 5.2 has no image understanding. While Opus and Fable can consistently identify trends in WandB charts, GLM resorts to writing numpy code to smooth and clean the raw WandB numbers before analyzing. For simpler runs like this example this is
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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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Announcement of new GPT-5.6, Pro, and bidirectional voice models this Thursday
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It looks like we are going to have a whole range of new GPT models this Thursday: GPT-5.6, 5.6 Pro, and a new bidirectional voice model. Initial tests of the voice model have been exceptional, this is exactly what I was hoping for two years ago!
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LangSmith LLM Gateway: Anonymizes Data and Controls Costs
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LangSmith LLM Gateway sits between your agents and LLM providers. It applies spending limits and anonymizes personal data (PII) before requests reach the model, stopping problems at the source rather than simply
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Data2Story: AI framework transforms raw data into verifiable news articles
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/3 Data2Story: an AI framework transforming raw data into verifiable, multimodal news articles. Data2Story orchestrates a virtual newsroom of specialized AI agents to analyze data, design multimedia assets, and write narratives. Crucially, it features an "Inspector" that