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Wednesday

Tuesday

  1. Google Developers Blog

    Google integrates TorchTPU into TorchTitan to optimize model training for TPUs

    Google integrates TorchTPU into Meta's TorchTitan; Cloud TPU can now train PyTorch models directly through the native backend torch.device("tpu"), without rewriting them into JAX. The TorchTitan TPU branch provides pretraining recipes for Llama 3 70B/405B, Qwen 3 8B/32B/235B MoE, and FLUX.1 12B, and replaces standard implementations with Pallas kernels such as Splash Attention. On FLUX, SimpleFSDP brings TorchTitan to 99% performance parity with JAX MaxDiffusion.

    Torch Titan Header Image

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    #engineering#Product updates#Open-source ecosystem#English text is being prepared.
  3. Fortune Tech

    Daytona raises a $60 million Series B at a $1 billion valuation

    Daytona, which provides cloud sandboxes for AI agents, has raised a $60 million Series B at a $1 billion valuation, led by FirstMark, bringing its total funding to about $91 million. Its customers include Anthropic, OpenAI, Nvidia, Cursor, Cognition, Meta, and Cohere, and CEO Burazin said the company's revenue run rate grew 25-fold between January and September. Daytona says it can provision a new sandbox for an agent within 60 milliseconds by pre-distributing snapshots of configured machines to servers' physical disks.

    loud sandboxes for AI agents, has raised a $60 million Series B at a $1 billion valuation, led by FirstMark, bringing its total funding to about $91 million. Its customers include Anthropic, OpenAI, Nvidia, Cursor, Cognition, Meta, and Cohere, and CEO Burazin said the company's revenue run rate grew 25-fold between January and September. Daytona says it can provision a new sandbox for an agent within 60 milliseconds by pre-distributing snapshots of configured machines to servers' physical disks.

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    #hot-signal#Industry news#Agents#English text is being prepared.
  6. Shao Meng

    MIT Course MAS.S62: Two Lectures on the Evolution of Harnesses and Multi-Agent Systems

    Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the entire harness as a file system and provides about 400 times the amount of feedback information of previous methods. The multi-agent section presents five empirical findings, including that MAS wins 81% on decomposable tasks and loses 70% on tasks with sequential dependencies, and that CORAL achieves new SOTA on 8 tasks, with multi-agent evolution improving over single-agent by 4%-20%.

    y to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the entire harness as a file system and provides about 400 times the amount of feedback information of previous methods. The multi-agent section presents five empirical findings, including that MAS wins 81% on decomposable tasks and loses 70% on tasks with sequential dependencies, and tha

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    #hot-signal#Tutorials / practice#Agents#English text is being prepared.

Monday

  4. SiliconANGLE

    Tenstorrent Completes $150 Million Series E Funding at a $6.2 Billion Valuation

    AI chip company Tenstorrent has completed a $150 million Series E funding round co-led by Trace Capital and Innovation X, at a valuation of $6.2 billion. The company centers on its RISC-V-based Tensix Neo accelerators and Ascalon CPU, paired with the TT-Forge, TT-Metalium, and TT-LLK open-source software stack, with products spanning Galaxy servers, TT-QuietBox workstations, and Blackhole cards. This round of funding will be used to expand Galaxy server production capacity, support RISC-V ecosystem research, and grow its global team.

    ed a $150 million Series E funding round co-led by Trace Capital and Innovation X, at a valuation of $6.2 billion. The company centers on its RISC-V-based Tensix Neo accelerators and Ascalon CPU, paired with the TT-Forge, TT-Metalium, and TT-LLK open-source software stack, with products spanning Galaxy servers, TT-QuietBox workstations, and Blackhole cards. This round of funding will be used to expand Galaxy server production capacity, support RISC-V ecosystem research, and grow its global team.

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    #hot-signal#Industry news#English text is being prepared.#Open-source ecosystem