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    Jinfu Technology raises $44M for liquid-cooling expansion▼Jinfu Technology raises $44M to fund liquid-cooling expansion✉newsTechnology2 d ago

    Jinfu Technology has raised $44 million in new funding that it will use to expand its liquid-cooling business. The company supplies cooling technology increasingly needed for data centres and high-performance computing, where rising energy demands are driving demand for more efficient thermal management. The investment signals continued investor interest in infrastructure supporting AI-driven computing growth.

  2. 2
    Seoul National University Holdings Backs AI Startup Bystrata in Seed Round▼Seoul National University Holdings Makes Seed Investment in AI Startup Bystrata to Reduce GPU Dependency✉newsBusinessStartups1 d ago

    Seoul National University Holdings has made a seed investment in AI startup Bystrata, whose technology aims to reduce dependency on GPUs for artificial intelligence workloads. The move reflects growing interest among university-affiliated investors in efficiency-focused AI infrastructure as hardware costs and chip shortages weigh on the sector.

  3. 3

    Researchers report that hyperbolic wave attractors, structures that trap and concentrate light waves, could be used to build next-generation optical chips. By confining light more efficiently, the approach may improve photonic circuits used in computing and communications. Details of the research and its practical applications remain limited in the available reporting.

  4. 4
    Efficient Computer raises $97 million for low-energy chips▼Low-energy chip startup Efficient Computer closes on $97M in funding✉newsBusinessStartups14 min ago

    Efficient Computer, a startup developing low-energy chips, has closed $97 million in funding. The Pittsburgh-based company is working on processor technology designed to dramatically cut power consumption, and the fresh capital will support its efforts to bring energy-efficient computing to market.

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    TurboGPT trains tiny 22KiB transformer in 13 seconds●Show HN: TurboGPT: train 22KiB transformer in 13sYhnWarMiddle East418 h ago

    A developer known as lostmsu has released TurboGPT, an open-source project on GitHub that trains a compact 22KiB transformer model in roughly 13 seconds. The tool is drawing attention from machine learning enthusiasts interested in fast, lightweight training experiments that can run without large compute budgets.

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    Chip startup Efficient Computer has raised $97 million in funding at a $650 million valuation, according to Reuters. The Pittsburgh-based company is developing energy-efficient general-purpose processors aimed at dramatically lowering power consumption in computing. The funding round underscores continued investor interest in semiconductor startups as demand grows for more efficient chips to power data centers and AI workloads.

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    10x Genomics Launches Sentira Computational Platform for Biology▼10x Genomics Announces Sentira, a New Computational Platform to Turn Complex Biological Data Into Experimental Conclusions✉newsScienceBiology10 min ago

    10x Genomics has announced Sentira, a new computational platform designed to turn complex biological data into experimental conclusions. The company positions the tool as a way to help researchers interpret large-scale single-cell and spatial datasets more efficiently. Sentira is aimed at scientists working in genomics who need faster, more actionable analysis of intricate biological data.

  8. 8

    Chip startup Efficient Computer has raised $100 million in funding at a $650 million valuation. The deal underscores continued investor appetite for semiconductor companies, particularly those working on energy-efficient processor designs as demand for computing power grows. The funding is likely to fuel the company's engineering work and push its chip technology toward commercial deployment.

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    Developer calls for prompt caching in Jevons-style AI models●Please add prompt caching to Jev-style models https://emschwartz.me/please-add-prompt-caching-to-jev-style-models/ # SofMmastodonTechnologySoftware214 h ago

    Software engineer Evan Schwartz has published a blog post urging makers of Jev-style AI models — lightweight open models whose efficiency drives heavier overall usage, echoing the Jevons paradox — to add prompt caching. Caching previously processed prompts would cut redundant computation, lower latency and reduce serving costs. The post is being shared among AI and open-source engineering communities, where efficiency and inference costs are active topics of debate.

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