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Zeeshan
Eigen Labs
12K posts
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Zeeshan
Eigen Labs
@zeeshan_utd
DevRel APAC @eigencloud | Views are my own | Hoops 🏀
Bengaluru, India
t.me/zeeshan8281
Joined February 2021
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  • Pinned
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    Zeeshan
    Eigen Labs
    @zeeshan_utd
    Feb 12
    Introducing News Analyst — a fully verifiable AI engine powered by @eigencloud suite > Uses EigenAI with wallet-signed grants. Temperature locked to zero with fixed entropy seeds. No hallucinations, only deterministic truth. > Every analysis is dispersed as a blob to EigenDA.
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  • user avatar
    Zeeshan
    Eigen Labs
    @zeeshan_utd
    1h
    AI’s app layer grew 12x in two years. It still barely changed where the money goes @apoorv03 estimates that ~75% of the ~$350B in new annualized AI revenue went to semiconductors Applications are scaling. Scarce compute still owns the economics Source: Apoorv Agrawal’s
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    Zeeshan
    Eigen Labs
    @zeeshan_utd
    3h
    Most multi-agent systems rely on one orchestrator Economy of Minds replaces it with auctions, payments and bankruptcy I reviewed how incentives become part of the learning algorithm, and where the market analogy breaks
    Article cover image
    Article
    What If the Best Agent Orchestrator Is a Market?
    Economy of Minds turns auctions, payments and bankruptcy into a coordination mechanism for AI agents. The results are compelling, but this is not yet an open agent economy. Most multi-agent systems...
  • user avatar
    Zeeshan
    Eigen Labs
    @zeeshan_utd
    21h
    Cursor is open-sourcing the MoE kernel it uses to train models across tens of thousands of GPUs MoK runs up to 2.37x faster than public baselines and raised Cursor’s end-to-end training throughput by 1.41x. A production breakthrough is now available for other labs to build on
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    Cursor
    @cursor_ai
    22h
    We're open-sourcing Mixture-of-Kittens (MoK), our MoE training megakernel for NVL72s. It fuses all Mixture-of-Experts communication and computation into a single, fully deterministic kernel, and runs up to 2.37x faster than the strongest public baselines.
    Bar chart: Mixture-of-Kittens reaches up to 2.37Ă— higher MXFP8 forward throughput than the fastest public baseline on GB300 NVL72s, across Kimi K2.7, GLM 5.2, Qwen 3.5-397B-A17B, and DeepSeek V4 Pro.
  • user avatar
    Zeeshan
    Eigen Labs
    @zeeshan_utd
    21h
    NVIDIA is turning autonomous driving from a closed, vertically integrated race into a shared development surface Alpamayo gives OEMs, startups and researchers a reasoning foundation they can inspect, fine-tune and deploy instead of rebuilding the stack from scratch This is how
    user avatar
    Jensen Huang
    NVIDIA
    @JensenHuang
    23h
    Today, we’re launching Alpamayo 2 Super, our frontier open reasoning model for autonomous vehicles. Beyond seeing, Alpamayo understands and reasons through the complex world - thinks before it acts. It’s a powerful backbone for robotaxis, trucks, shuttles, delivery vans,
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