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Amazon fined $2.5B for using deceptive methods to sign up consumers for Prime

The Federal Trade Commission has secured a historic order with Amazon.com, Inc., as well as Senior Vice President Neil Lindsay and Vice President Jamil Ghani, settling allegations that Amazon enrolled millions of consumers in Prime subscriptions without their consent, and knowingly made it difficult for consumers to cancel. Amazon will be required to pay a $1 billion civil penalty, provide $1.5 billion in refunds back to consumers harmed by their deceptive Prime enrollment practices, and cease u

FTC Secures Historic $2.5B Settlement Against Amazon

The Federal Trade Commission has secured a historic order with Amazon.com, Inc., as well as Senior Vice President Neil Lindsay and Vice President Jamil Ghani, settling allegations that Amazon enrolled millions of consumers in Prime subscriptions without their consent, and knowingly made it difficult for consumers to cancel. Amazon will be required to pay a $1 billion civil penalty, provide $1.5 billion in refunds back to consumers harmed by their deceptive Prime enrollment practices, and cease u

Show HN: Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput

LLM Inference for Large-Context Offline Workloads oLLM is a lightweight Python library for large-context LLM inference, built on top of Huggingface Transformers and PyTorch. It enables running models like gpt-oss-20B, qwen3-next-80B or Llama-3.1-8B-Instruct on 100k context using ~$200 consumer GPU with 8GB VRAM. No quantization is used—only fp16/bf16 precision. Latest updates (0.4.0) 🔥 qwen3-next-80B (160GB model) added with ⚡️1tok/2s throughput (fastest model so far) (160GB model) added wit

Smollm3: Smol, multilingual, long-context reasoner LLM

SmolLM3: smol, multilingual, long-context reasoner Published July 8, 2025 Update on GitHub Base model: https://hf.co/HuggingFaceTB/SmolLM3-3B-Base Instruct and reasoning model: https://hf.co/HuggingFaceTB/SmolLM3-3B Small language models are becoming increasingly important as users seek capable models that can be deployed efficiently. The community has produced a fascinating range of capable small models, each pushing the boundaries of what's possible at this scale. With SmolLM3, we're excit