458 summarised stories about Large Language Models, each linking back to the original source. Browse all topics →
petals.dev 2 months ago 40
Petals enables users to run large language models like Llama 3.1 and Mixtral on consumer-grade hardware by distributing model layers across a peer-to-peer network similar to BitTorrent. The system achieves inference speeds of up to 6 tokens per second for Llama 2 (70B) and supports fine-tuning and custom model paths through PyTorch. This approach makes running billion-parameter models accessible to individuals without enterprise-grade infrastructure.
cacm.acm.org 2 months ago 8
A new benchmark called Beaver reveals that large language models perform substantially worse at text-to-SQL tasks when tested against real-world database schemas compared to idealized benchmarks. The benchmark accounts for schema rot and idiosyncratic data patterns typical of production data warehouses. This suggests current LLM-based SQL generation tools may be less reliable in actual enterprise deployments than their benchmark scores indicate.
The Batch 14 ● 7 sources
Alibaba released Qwen3.7-Max, a closed-weights large language model that ranks seventh on the Artificial Analysis Intelligence Index and produces 208 tokens per second, positioning it as the fastest reasoning model among Chinese LLMs. WhaleSpotter, an AI system using thermal imaging and neural networks, detects gray whales in real time and alerts ships to avoid collisions, with over 70 systems now deployed across vessels and ports after a decade of research at Woods Hole Oceanographic Institution. The shift reflects Alibaba's move toward monetizing frontier models while open-source tools like WhaleSpotter demonstrate practical AI applications for marine conservation.
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20 October 2026
The biggest throughline today wasn’t smarter models—it was how teams keep AI usable when it starts behaving less like software and more like an employee. At Fortune’s Most Powerful Women Summit, SharkNinja CIO Velia Carboni made the case for letting systems be “a little rogue,” then finding limits with guardrails, structured human oversight, and day-to-day harnesses. That security-by-process theme echoed across the rest of the day: Anthropic said its defenses against distillation attacks on Claude now cut stolen “reasoning” tokens by 10 to 100 times, while blocking distillers more often and banning many accounts; OpenAI rolled out chat-lock in ChatGPT with a four-digit PIN/Face ID and separate locked storage; and Datadog argued the SDLC bottleneck has shifted from code-writing to testing, pushing teams toward agent-generated, observability-tied test suites.
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