• 0 Posts
  • 23 Comments
Joined 7 years ago
Cake day: January 21st, 2020
  • leanleft@lemmy.mltoTechnology@lemmy.world•Nvidia has been in talks to acquire Hugging Face for more than $13 billionEnglish
    7·
    2 days ago

    i just read this: https://reddit.com/comments/1w01y1f

    With this move Nvidia is not only acquiring the HuggingFace platform, but they might also effectively acquire the copyright to the llama.cpp project, together with the entire team behind it.

    In February 2026 the llama.cpp team was employed by HF in order to continue working on llama.cpp and the ggml library.

    This includes:

    • Georgi Gerganov
    • Xuan-Son Nguyen
    • Aleksander Grygier
    • Victor Mustar
    • Lysandre
    • Julien Chaumond

    Now with the acquisition, llama.cpp’s future looks a lot less certain given Nvidia’s poor track record with open-source.

    This is still rather speculative at this stage, but it’s definitely possible for the llama.cpp project to change in the future: either by switching to a different license, or by having staff redirected to other projects within the larger company.

    Even when a project is open-source the copyright owner has complete control over it, and they can change licensing as they wish.

    This has happened before with projects like Redis, Minio, and others.

    Source:

    https://huggingface.co/blog/ggml-joins-hf

    Edit:

    The original announcement from Feb 2026 from Gerganov gives a few more details:

    https://github.com/ggml-org/llama.cpp/discussions/19759

  • leanleft@lemmy.mltoTechnology@lemmy.world•How China gets better bang for its buck than America in AI. Its investment lags far behind America’s. Its models do notEnglish
    64·
    27 days ago

    i distilled this article

    Summary of the article “How China gets better bang for its buck than America in AI” (Aug 3 2026)

    • U.S. AI spending is massive – Bloomberg Intelligence estimates U.S. data‑centre capital outlays could exceed $740 billion in 2026, with Nvidia alone negotiating a $250 billion financing deal for a $500 billion data‑centre run by OpenAI. Alphabet announced a $205 billion AI budget.

    • China spends far less – Chinese tech firms are projected to invest less than one‑tenth of the U.S. amount in data centres. Yet their models perform only slightly behind U.S. equivalents. For example:

      • K3 (Moonshot AI) scores ≈ 95 % of Anthropic’s Fable 5 on common benchmarks while being 70 % cheaper to run.
      • Alibaba’s newly released model ranks among the world’s best on certain metrics.

    • Why Chinese spending is efficient

      1. Lower input costs – Land, construction, equipment and labour are cheaper in China.
      2. Model distillation – Chinese labs often train models using outputs from expensive U.S. models, reducing the compute needed.
      3. Hidden spending – Some expenditures on high‑end chips are masked as “cost‑saving” techniques that make inferior hardware achieve higher performance (e.g., DeepSeek’s efficiency tricks).

    • Export restrictions limit Chinese capital use – U.S. bans on advanced AI chips (Nvidia designs, TSMC manufacturing) prevent China from buying the most powerful hardware.

      • Chinese firms are pushed toward domestic alternatives (Huawei, SMIC).
      • Sanctions also block access to cutting‑edge chip‑making equipment, forcing costly work‑arounds and capping production capacity.

    • Domestic demand constraints – Chinese enterprises spend < 10 % of what U.S. firms spend on IT, despite China’s GDP being two‑thirds of the U.S. (or a third larger in PPP terms). This throttles revenue prospects for AI providers, curbing their willingness to invest heavily.

    • Strategic focus differs – The Chinese Communist Party emphasizes diffusing AI across the economy, not pursuing a race toward artificial general intelligence (AGI). Fewer than ten Chinese firms target AGI, compared with dozens of U.S. players.

    • Investor attitudes – Chinese investors have historically punished over‑spending on AI, whereas U.S. investors once rewarded aggressive budgeting. This cultural difference keeps Chinese AI budgets modest.

    • Potential bottlenecks for China – Despite restraint, China may face compute shortages:

      • ByteDance experiences ten‑hour processing times for some videos.
      • Alibaba Cloud, Zhipu AI, and Moonshot’s K3 have long waiting lists or quickly sell out capacity.
      • Over‑restriction could stifle growth if AI services cannot meet user demand.

    Overall takeaway: China achieves comparable AI performance to the U.S. while spending a fraction of the capital by leveraging cheaper resources, model‑distillation techniques, and a strategic focus on wide‑scale diffusion rather than raw computational power. However, export bans, limited domestic chip capacity, modest corporate demand, and cautious investors together create both an efficiency advantage and a risk of under‑provisioned infrastructure.

  • leanleft@lemmy.mltoToday I Learned@lemmy.world•TIL you can get much better online prices using a public library computerEnglish
    1·
    28 days ago

    go to https://www.bonappetit.com/ and watch the price suddenly jump!

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    goddamnit. all i wanted was a muffin !
    why are you counting my pockets so aggressively ?

  • leanleft@lemmy.mltoTechnology@lemmy.world•Don't Contribute Anything Relevant in Web Forums Like RedditEnglish
    1·
    1 month ago

    Analogy

    Imagine a restaurant where diners place orders through a discreet slot on the kitchen door.

    • The web user is the diner who slips a note into the slot asking for a specific dish.
    • The web‑service backend is the chef who receives the note, prepares the meal, and slides it back out the slot for the diner to collect.

    Now, the diner adds a bizarre request: “Please serve the food, but I never want you to know you ever cooked it.” In practice, the chef can’t fulfill this—once he’s touched the ingredients and used the stove, his hands are inevitably stained, and the kitchen’s heat tells the story. Likewise, when a web user asks a server to fetch data, the server must process the request, so it inevitably knows it handled that request, even if it can’t tie it back to the individual’s identity.

    Ad‑blockers, cookies, IP addresses, user‑agents, and browser signatures are like the various “fingerprints” a diner might leave on the restaurant’s doorstep:

    • Cookies are tiny crumbs the diner drops on the floor while walking to the slot. The chef (or later staff) can later sweep them up and infer that the same person visited before.
    • IP address is the diner’s shoeprints on the hallway carpet leading to the kitchen—an easy way to trace where the diner entered from.
    • User‑agent / browser signature is the unique style of the diner’s napkin (its color, fold, and logo). Even if the napkin is discarded, the pattern tells the chef which brand of napkin was used.
    • Ad‑blockers are like the diner wearing a cloak that blocks the kitchen staff from seeing the crumbs he drops, trying to keep the chef from noticing the usual “tipping” (ads) that would normally be left behind.

    Just as no chef can truly be blind to the fact that he cooked a dish, a web service inevitably knows it processed a request, and the “fingerprints” left behind (cookies, IP, user‑agent, etc.) let it—or any intermediary—recognize or track the diner unless the diner takes strong steps (like using a cloak or wiping the floor) to hide those traces.

    https://f-droid.org/packages/com.fauxx.full