• 1 Post
  • 35 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.mltoPrivacy@lemmy.ml•Effective Web Tracking Methods in 2026: ExplainedEnglish
    216·
    6 days ago

    LLM answer:

    
- Tracking Pixels and Web Beacons
- Email-Open Tracking
- Email Link-Click Tracking
- Advertising Impression Tracking
- Conversion Tracking
- Social-Media Pixels
- Analytics Tags and Tag Managers

- IP Address and Network Tracking
- IP-Based Geolocation
- ISP, ASN, and Organization Identification
- Household and Network-Level Matching
- VPN, Proxy, and Tor Detection
- Network Reputation and Fraud Scoring

- URL and Referral Tracking
- UTM Parameter Tracking
- Advertising Click-ID Tracking
- Affiliate and Referral-ID Tracking
- Email Campaign-ID Tracking
- Link Decoration
- Redirect-Chain Tracking
- Cross-Site Identifier Propagation
- HTTP Referer Header Tracking

- HTTP Header and Cache Tracking
- User-Agent Tracking
- Client Hints Tracking
- Accept-Language Tracking
- ETag Tracking
- Cache-Control and Last-Modified Tracking

- Browser Storage Tracking
- First-Party Cookies
- Third-Party Cookies
- LocalStorage and SessionStorage Tracking
- IndexedDB Tracking
- Cache Storage Tracking
- Shared Storage Tracking
- Service-Worker Storage Tracking
- Evercookies and Identifier Respawning
- Supercookies
- HSTS Supercookies
- Favicon Cache Tracking
- TLS Session-Resumption Tracking

- Browser Fingerprinting
- Browser, Version, and Operating-System Fingerprinting
- Screen, Viewport, Color-Depth, and Device-Pixel-Ratio Fingerprinting
- Time-Zone, Language, Locale, and Keyboard-Layout Fingerprinting
- Installed-Font and Font-Rendering Fingerprinting
- Canvas Fingerprinting
- WebGL and WebGPU Fingerprinting
- AudioContext Fingerprinting
- GPU and Graphics-Driver Fingerprinting
- CPU Core, Device-Memory, and Hardware-Concurrency Fingerprinting
- Touchscreen, Input, and Device-Capability Fingerprinting
- Camera, Microphone, and Media-Device Fingerprinting
- Browser Extension, Plugin, MIME-Type, and PDF-Viewer Fingerprinting
- CSS Feature and Media-Query Fingerprinting
- Browser API Availability Fingerprinting
- WebRTC and Local-IP Exposure
- Network Information API Fingerprinting
- TLS, JA3, and JA4 Fingerprinting
- HTTP/2 and HTTP/3 Protocol Fingerprinting
- DNS Resolver Fingerprinting
- Clock-Skew and Performance-Timing Fingerprinting
- Sensor Fingerprinting
- Motion, Orientation, and Ambient-Light Sensor Fingerprinting
- Speech-Synthesis Voice Fingerprinting
- Emoji and Text-Rendering Fingerprinting
- Math, Floating-Point, Error, and Exception Fingerprinting
- Browser Automation and Bot-Detection Fingerprinting
- Combined and Probabilistic Fingerprinting

- Account and Login-State Tracking
- Logged-In Account Tracking
- Login-State Detection
- Single Sign-On Tracking
- OAuth and Social-Login Tracking
- Embedded Social-Widget Tracking
- Account-Recovery Identifier Matching

- Cross-Device and Identity-Graph Tracking
- Login-Based Device Linking
- Hashed Email and Phone-Number Matching
- Probabilistic Device Matching
- Data-Broker Identity Resolution
- Offline-to-Online Data Matching
- CRM and Customer-Data Matching

- Server-Side Tracking
- Server-Side Analytics
- Server-Side Tag Management
- Server-to-Server Event Tracking
- Advertising Conversion APIs
- Purchase, Order, and Lead-Event Sharing

- Behavioral Analytics and Session Replay
- Mouse-Movement Tracking
- Scroll-Depth Tracking
- Click and Hover Tracking
- Keystroke and Form-Interaction Tracking
- Heatmap Tracking
- Rage-Click Detection
- Dwell-Time and Engagement Tracking
- Form-Abandonment Tracking

- Push-Notification Tracking
- Web Push Subscription Identifiers
- Mobile Push Tokens
- Notification Engagement Tracking

- Mobile-App Tracking
- Mobile Advertising IDs
- App Instance and SDK Identifiers
- App-Install Attribution
- Deep-Link Tracking
- Mobile Device Fingerprinting
- Mobile Location Tracking

- Location Tracking
- GPS Location
- Wi-Fi Network Location
- Bluetooth Beacon Location
- Cell-Tower Location
- Nearby Device and Network Discovery

- Embedded Third-Party Content Tracking
- Embedded Video Tracking
- Embedded Map Tracking
- Comment-Widget Tracking
- Payment-Widget Tracking
- CAPTCHA and Anti-Bot Widget Tracking
- Third-Party JavaScript Tracking

- DNS and Domain Cloaking
- CNAME Cloaking
- First-Party Subdomain Tracking
- Domain Alias Tracking
- DNS Query Tracking
- DNS Prefetch Tracking

- Advertising and Attribution Tracking
- Real-Time Bidding Tracking
- Ad-Exchange and Demand-Side Platform Identifiers
- Retargeting and Remarketing
- Frequency-Capping Identifiers
- View-Through Attribution
- Multi-Touch Attribution
  • 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·
    26 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.