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  • The Role of Memory in AI Agent Workflows
    The Role of Memory in AI Agent Workflows

    By

    @s0n_ia_

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    La memoria es el verdadero multiplicador en cualquier workflow recurrente con agentes de IA. No se trata solo de seguir instrucciones sino de acumular conocimiento sobre cada creador, cada error pasado y cada preferencia de estilo a lo largo del tiempo. Cuanto más tiempo lleva

    → View original post on X — @s0n_ia_

  • AI Tool Development: Managing Parallel Feature Dependencies

    By

    @jessyseonoob

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    C'est une bonne pratique de pouvoir génerer le pré-prompt pour savoir ce que l'outil sais faire, et de comment il le fait. Bravo. Le truc qui me fait toujours peurs, c'est qu'il parte en parrallèle pour créer des fonctionnalités, et que t'as dépendances entre elles et qu'il n'a

    → Voir le post original sur X — @jessyseonoob

  • Simple AI Scripts vs LLM-Driven Agents Infrastructure

    By

    @marek_rosa

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    I don't think so. The AI is a pretty simple script. Yes, pathfinding may need some computation, but that's it. Ofc, if we then go next level -> use LLM driven AI agents, then that would require some server, GPU, cloud, etc. But that's an optional and nice to have addition.

    → View original post on X — @marek_rosa

  • Sakana AI Announces Strategic Partnership and Investment from Mitsubishi Electric
    Sakana AI Announces Strategic Partnership and Investment from Mitsubishi Electric

    By

    @hardmaru

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    We’re excited to announce a strategic partnership and investment from Mitsubishi Electric 🇯🇵⚡ By combining their deep domain knowledge and vast amounts of data in the manufacturing sector with Sakana AI’s technology, we’re taking a significant step toward bringing Agentic AI into physical industries. Following our ongoing work in the finance and defense sectors, we view physical AI in manufacturing as our third major pillar. We look forward to working closely with their team to solve complex, real world challenges and deploy these systems in core industries. businesswire.com/news/home/2… Sakana AI (@SakanaAILabs) 三菱電機株式会社より出資を受けました。 prtimes.jp/main/html/rd/p/00… 本パートナーシップを通じて、三菱電機が培ってきた製造現場のドメインナレッジや膨大なデータと、Sakana AIの最先端のAI技術を掛け合わせます。これにより、製造業を中心とする基幹産業の高度化に資するソリューション提供を進めて いきます。 私たちは、これまで取り組んできた金融や防衛領域に続く第3の柱として、フィジカルAIを含む製造領域でのAI活用を位置付けています。今後も、日本の強みを生かした革新的なAI技術の社会実装に取り組んでまいります。 三菱電機専務執行役の武田聡氏からは、今回の出資が実社会での具体的な課題解決におけるAIの可能性を拡げる重要な一歩になることを確信している、とのお言葉をいただきました。 — https://nitter.net/SakanaAILabs/status/2037118877612859545#m

    → View original post on X — @hardmaru, 2026-03-26 11:04 UTC

  • Promptable World Events: Dynamic Simulation Control for Enterprise Training

    By

    @ronald_vanloon

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    Then there is “promptable world events.” Inside the simulation, you can dynamically: → Change the weather
    → Modify objects
    → Introduce or alter characters
    → Trigger new scenarios in real time Think about the enterprise implications: → Training embodied agents
    →

    → View original post on X — @ronald_vanloon

  • Huawei AgentInfer: Optimizing AI Agent Inference Performance
    Huawei AgentInfer: Optimizing AI Agent Inference Performance

    By

    @jiqizhixin

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    Struggling with slow AI agents that get bogged down in long tasks? Huawei just unveiled AgentInfer. This unified framework co-designs inference architecture and the overall system to tackle systemic inefficiencies. It intelligently balances large and small models, optimizes

    → View original post on X — @jiqizhixin

  • AI Scientist V1 Completed Before o1-Preview, Models More Capable Now

    By

    @_yutaroyamada

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    The AI Scientist V1 was completed months before o1-preview and reasoning models were released. The models have clearly gotten much more capable since then. Very excited for where things are headed for AI and automated research! Sakana AI (@SakanaAILabs) The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: nature.com/articles/s41586-0… Blog: sakana.ai/ai-scientist-natur… When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle. From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible. Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process. Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature! This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement. Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable. Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team! @_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune — https://nitter.net/SakanaAILabs/status/2036840833690071450#m

    → View original post on X — @_yutaroyamada, 2026-03-26 08:53 UTC

  • OpenAI Skills and Sub-Agents: What’s Missing?

    By

    @reach_vb

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    to a large extent this is all already possible with openai/skills paired with sub agents no? anything that’s missing?

    → View original post on X — @reach_vb

  • Agent Accountability: Governance, Identity, and Auditability Requirements

    By

    @haroldsinnott

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    Great point, @Jetsonhacks , accountability doesn’t disappear with agents, it shifts to the person or organization deploying them. That’s exactly why governance, identity, and auditability need to be built in from the start, especially in regulated environments where privacy and

    → View original post on X — @haroldsinnott

  • AVO: Agentic Variation Operators for Autonomous Evolutionary Search
    AVO: Agentic Variation Operators for Autonomous Evolutionary Search

    By

    @jiqizhixin

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    AVO: Agentic Variation Operators for Autonomous Evolutionary Search Paper: https://
    arxiv.org/abs/2603.24517
    v1
    … Our report: https://
    mp.weixin.qq.com/s/JGqF9Z1up9nR
    q_owePisbQ
    …

    → View original post on X — @jiqizhixin