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Wednesday113 items

  2. NVIDIA Developer · Generative AIFirst-partyProduct release
    AI score74

    NVIDIA Releases OpenShell Reference Architecture to Provide Security and Governance for Large-Scale AI Agent Workloads

    NVIDIA has released the OpenShell reference architecture, using an open-source runtime to treat agent workloads as untrusted objects and grant access on demand. The architecture consists of four components: Gateway, Supervisor, Sandbox, and Policy Prover, which are responsible for sandbox lifecycle and policy management, credential access brokering, workload isolation, and formal verification of policy permissions, respectively. CoreWeave's measurements show that on Vera CPUs, sandbox startup is more than 3 times faster than on x86, agent loops complete 1.8 times faster, and a single rack can support more than 11,000 concurrent agent environments.

    A comparison of NVIDIA Vera (green) against x86 (gray). Left chart shows sandbox startup speed in sandboxes per second, with Vera more than 3x higher than x86. Right chart shows per-task relative performance across 20 agentic loop tasks, with Vera bars consistently above the x86 baseline of 1.0, reaching up to 2x on tasks such as cobol-modernization, fix-git, db-wal-recovery, and openssl-selfsigned-cert. Geometric mean advantage: 1.8x.

    Same story · 9 reports

    #Product updates#Agents#Safety / alignment

  11. Ayi AI NotesProduct release
    AI score67

    Open-source project LangAlpha v2.0 launches an investment research agent system that connects to multiple brokerages

    The open-source project LangAlpha v2.0 has released an investment research system that puts a virtual analyst team and four tiers of trading authorization into the same workspace, and can connect to brokerages such as Robinhood, Interactive Brokers, and moomoo. The four authorization tiers are no trading, manual approval for every trade, produce a strategy plan before acting, and fully autonomous. The system uses a Chief of Staff to split tasks among the semiconductor team, portfolio management team, and earnings report team for concurrent processing, and outputs strategy reports with backtests and checkpoints. The project already has more than 1,800 Stars in the open-source community, and the author recommends locking in per-trade approval first and then gradually delegating authority.

    Quoted postAYi · @AYi_AInotes

    English text is being prepared.

    #English text is being prepared.#Agents#English text is being prepared.

  12. X.PINProduct release
    AI score72

    DeepSeek Open-Sources Its Internal Inference Framework DeepInfer

    DeepSeek has open-sourced its internal inference framework DeepInfer, which it says powers the high-throughput, low-latency inference of DeepSeek-V4.1-Flash. The framework supports both Nvidia and Huawei Ascend hardware, and offers capabilities such as distributed inference, speculative decoding, SWA Bounded Replay, and structured output. DeepSeek also released deepseek-recipe, a production-grade tokenization component fully aligned with the official API.

    Quoted postDeepSeek · @deepseek_ai

    🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 https://t.co/JGAik1kgk0 1/4

    Same story · 29 reports

    #English text is being prepared.#English text is being prepared.#DeepSeek

  19. Latent SpaceProduct release
    AI score85

    DeepSeek open-sources the DeepInfer inference framework, processing 155 trillion tokens per day in a single cluster

    DeepSeek has open-sourced DeepInfer, its self-developed inference framework that supports DeepSeek-V4.1-Flash, and published production data: a single cluster processes 155 trillion tokens per day, 96% input cache hit rate, 400ms first-token latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also states that components such as KV cache management and load balancing are not yet fully open-sourced, while the vLLM team says that under a matching GB200 configuration, decoding throughput is within 6% of the results published by DeepInfer.

    Same story · 29 reports

    #English text is being prepared.#Reasoning#English text is being prepared.

  20. Ayi AI NotesProduct release
    AI score70

    Open-source project LangAlpha v2.0 launches: multi-agent investment research with brokerage order placement

    The open-source project LangAlpha v2.0 has officially launched, putting financial data, research tools, multi-agent collaboration, and brokerage connectivity into a single workspace, covering markets including US stocks, Hong Kong stocks, A-shares, Japan, South Korea, Germany, and India, as well as Crypto, gold, and commodities. It offers four tiers of trading authorization, ranging from no trading at all, to manual approval for every trade, to producing a strategy plan before acting, to full autonomy, and has already integrated brokerages such as Robinhood, supporting bring-your-own API keys, Skills and MCP plugins, and self-deployment. The author notes that the open-source community has already given it more than 1,800 stars, but financial volatility and goal drift in long tasks will be amplified by automation, so it is advisable to start by requiring approval for every trade.

    Quoted postLangAlpha Chinese/CN · @ginlixai

    English text is being prepared.

    #English text is being prepared.#Agents#English text is being prepared.