If you want to look under the hood at the actual custom CUDA kernels and see exactly how we implemented the TwELL format for H100 GPUs, we’ve released the reference code. GitHub: https://
github.com/SakanaAI/spars
er-faster-llms
…
Blog: https://
pub.sakana.ai/sparser-faster
-llms/
…
LLMS
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Custom CUDA Kernels for TwELL Format on H100 GPUs Released
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New AI-Driven Workflow for HTML Generation
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Anthropic's Thariq's article on HTML from yesterday blew up, with 1.5M reads. It looks like it's talking about formatting aesthetics, but actually he's describing an entirely new workflow. Picking out a few of the points with the most technical depth. First, HTML isn't a
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Sakana AI Accelerates Sparse LLMs with NVIDIA
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Sakana AIは、@NVIDIAとの共同研究で、スパースなTransformer言語モデルの推論・学習を高速化する新しいGPUカーネルとデータ形式を開発しました。
— Sakana AI (@SakanaAILabs) 9 mai 2026
ブログ：https://t.co/fMARMRFsJJ
LLMのコストの大部分を占めるフィードフォワード層では、実は各トークンに対して大半の活性がほぼゼロで無駄な計算に… https://t.co/nTMg0QgdSrSakana AI has developed new GPU kernels and data formats that accelerate inference and training of sparse Transformer language models through joint research with @NVIDIA
. Blog: https://
pub.sakana.ai/sparser-faster
-llms/
… In the feedforward layers, which account for the majority of LLM costs, most -
Claude Mythos Preview snapshot outperforms next best model by 2x
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An early Claude Mythos Preview snapshot we provided METR has a time horizon of more than 2x the next best model on their 80% success rate benchmark
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ChatGPT 5.5’s Math Skills Analyzed
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I write about this in more detail in a blog post with a guest contribution from Isaac Rajagopal, a student at MIT on whose work ChatGPT built, who gives his assessment of the level of mathematical ability displayed by the model. https://
gowers.wordpress.com/2026/05/08/a-r
ecent-experience-with-chatgpt-5-5-pro/
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DeepMind solves 48% of a complex math benchmark
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Google DeepMind's AI co-mathematician just scored 48% on FrontierMath Tier 4, a new high on a benchmark of 50 research-level math problems some professors expected AI wouldn't touch for decades. The system generated a proof so flawed its own reviewer flagged it as wrong. But
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Anthropic Introduces Managed Agents Feature for Claude
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In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget.
— Dan Shipper 📧 (@danshipper) 8 mai 2026
That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a… pic.twitter.com/zmKaR99GdjIn the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a
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Domino platform integrates AI lifecycle management from training to deployment
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84% of developers use AI to code (Stack Overflow, 2025). Most stop at autocomplete. Inside Domino, your assistant learns the rest of the lifecycle. Training. Deployment. Governance. Monitoring. Same prompt. Whole platform. Blueprint: https://
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Architecting Infrastructure for Long-Horizon AI Agents
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Join our live session and learn: How to think about the agent harness vs. the runtime layer underneath it What infrastructure long-horizon agents need for durable, stateful execution How to plan for memory, recovery, human intervention, observability, and scale Why
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Technical Production Requirements for Long-Horizon AI Agents
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Long-horizon agents introduce production requirements that short-running agents don’t: durable execution, state management, recovery, memory, observability, and human oversight. On May 28th, join @sydneyrunkle for a live session on The Runtime Behind Production Long-Horizon