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Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)
Leading today: Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II). Also in this issue: Securing our frontier model training environment; OpenAI and Poland’s NASK partner to advance AI safety and security. 16 stories in total, selected automatically from 64 candidates across the day's feeds.
| 1 | openai.com Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)Learn how OpenAI scaled Habitat with reliable multi-tenancy, global caching, and Azure Cosmos DB to deliver 12× faster reads for ChatGPT. |
| 2 | openai.com Securing our frontier model training environmentOpenAI explains how we strengthened containment for frontier model training and evaluation, including offline environments and high-compute red-teaming. |
| 3 | openai.com OpenAI and Poland’s NASK partner to advance AI safety and securityOpenAI and Poland’s NASK are partnering on AI safety, security, and technical research to strengthen safeguards and support a more resilient digital ecosystem. |
Moonshot AI's Kimi K3.1 Pro scores 55 points on the Artificial Analysis Intelligence Index, an 11-point jump over its predecessor that puts it in fourth place. Only three Anthropic and Meta models rank higher.
Read →Anthropic says it has an internal AI model that outperforms and is better aligned than Mythos 5.1.
Read →Design platform Canva has released Canva Craft, its first in-house image generation model.
Read →Trending on Hacker News (140 points, 154 comments).
Read →Moonshot AI released Kimi K3.1 Pro, a 2.8T-parameter, 104B-active MoE model with 1M-token context. It scores 65.7% on Terminal-Bench 4.0, costs $2/$10 per million tokens, and open weights arrive by Nov 4, 2026.
Read →Google Cloud announced Agent Compute Engine, a compute service that gives every AI agent its own hardware-isolated microVM with no credentials inside.
Read →
A Blog post by Ai2 on Hugging Face
Read →Trending on Hacker News (115 points, 38 comments).
Read →Test-time scaling has emerged as a key inference strategy for reasoning models, where a model generates a large number of candidate solutions in the hope of finding at least one correct solution.
Read →Wrong automatic routing of an IT support ticket can cost more than human triage. We evaluate cost-aware abstention as an offline, cost-conditional readiness assessment: select a routing policy on calibration data under e...
Read →Meta-reinforcement learning aims to train agents that adapt quickly to unseen tasks from limited interaction, but this requires representing task evidence over long interaction histories.
Read →Tool-using language model agents rely on environmental facts to decide whether an action is permitted.
Read →
Trending on Hacker News (56 points, 53 comments).
Read →The AI industry, condensed into a five minute read. Free, and you can leave whenever.
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