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  1. TechCrunch · AI78

    Jensen Huang’s exit puts Nvidia’s support for open source in question

    Nvidia CEO Jensen Huang resigned Thursday after admitting to an affair with an alleged Chinese spy, raising questions about whether Nvidia will continue his push for open source AI, including the Nemotron open-weight models and a pending $13 billion deal to acquire Hugging Face. Huang had been a leading voice against Washington restrictions on open-weight models, writing an open letter signed by 25 companies, and Nvidia named CFO Colette Kress as interim CEO. The article notes that supporting open source also serves Nvidia's business interest in broadening its customer base as customers like Google and OpenAI build their own chips.

    Why it matters: It reports a major leadership change at Nvidia and examines how it could affect open source AI policy and Nvidia's open-model strategy.

  1. Hacker News AI78

    Revolut swaps Claude for GLM open-weight model, cutting AI coding bill by 90%

    Revolut has moved its roughly 2,000 software engineers off Anthropic's Claude Code and OpenAI's Codex as default coding tools onto GLM, an open-weight model from Chinese lab Z.ai that Revolut hosts itself, bringing its bill to about a tenth of what it paid Anthropic and OpenAI. David Abitbol, who runs AI operations and infrastructure at Revolut, said the company serves GLM from about 56 Nvidia B200 GPUs at a blended cost of around $0.30 per million tokens, and reported roughly 60% shorter coding time and 70% shorter lead time in a sample of about 2,000 engineers, though the comparison does not isolate the model change from other causes. Abitbol said the 90% saving excludes cloud, staff and monitoring costs and depended on heavy tuning, with out-of-the-box inference "terrible" until time to first token fell from about 14 seconds to 1.7 seconds on the same GPUs.

    Why it matters: It shows a major European fintech running a self-hosted Chinese open-weight model as its engineering default, with concrete cost and latency figures and caveats worth noting.

  1. Cloudflare Blog74

    Cross-request KV cache exposure in the GLM-5.3 Workers AI model, and our remediation with SGLang

    Cloudflare reports that from August 27 to September 9, 2026, 155 responses from the GLM-5.3 Workers AI model (@cf/zai-org/glm-5.3) to prompts longer than 681,280 tokens contained data from other customers' requests, caused by a prefill KV pool smaller than the 1,048,576-token context window combined with an SGLang bug that let rejected over-length requests proceed through disaggregated prefill/decode handoff. The model was taken offline on September 9 and restored the same day after a corrected configuration and an engine patch, and Cloudflare says it found no evidence of deliberate exploitation. Cloudflare recommends operators of disaggregated SGLang upgrade to v0.5.22 or later and verify KV pool size against context length on every node.

    Why it matters: A detailed first-party incident report that is useful for anyone operating disaggregated LLM inference, with concrete configuration checks and upstream patch references.

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