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4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src
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ai-infrastructure

topic5 events
industrySEP 11 10:00 UTC

OpenAI outlines storage platform behind ChatGPT's 1 billion users

OpenAI published an engineering account of how its storage system, Habitat, grew from an internal Python library into a distributed platform spanning multiple regions. The company says the system now handles roughly 22 million requests per second while supporting more than 1 billion ChatGPT users. The post describes the architectural changes made to keep pace with that growth.

WHY IT MATTERS ↘As frontier model quality converges, the ability to serve billions of users at tens of millions of requests per second increasingly determines cost per interaction and uptime, making bespoke storage and serving infrastructure a competitive moat rather than a back-end detail. For practitioners, it signals that data-layer architecture—not just model design—is now a primary constraint on scaling AI products, and that OpenAI is publishing this to set expectations for what production-scale deployment requires.

infrastructure/openai/chatgpt/scaling
papersSEP 10 04:00 UTC

New benchmark tests if LLMs can engineer the AI infrastructure that powers them

A new arXiv paper introduces Φ-Bench, a benchmark that measures how well large language models can help develop and optimize the computing infrastructure used to run AI systems. The authors argue that existing benchmarks do not adequately cover these infrastructure-engineering tasks, which go beyond typical code generation. The work aims to gauge whether LLMs can realistically contribute to the specialized systems engineering that underpins their own operation.

benchmark/evaluation/llm/infrastructure