第99期 | What Parameter Golf taught us about AI-assisted research
今日摘要
OpenAI Blog：Teams use Codex with GPT-5.5 to ship production systems and turn research ideas into runnable experiments.
OpenAI Blog：Parameter Golf brought together 1,000+ participants and 2,000+ submissions to explore AI-assisted machine learning research, codin…
OpenAI Blog：Learn how AutoScout24 Group uses Codex and ChatGPT to speed development cycles, improve code quality, and expand AI adoption.
X Andrej Karpathy：This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated fi…
OpenAI Blog：ChatGPT adoption surged in Q1 2026, with fastest growth among users over 35 and more balanced gender usage, signaling broader main…
总结 + 观点：How enterprises scale AI: from early experiments…｜中文观点：对 How enterprises are scaling AI，更该看它能不能改善多步骤…
总结 + 观点：Join the OpenAI Campus Network—connect student c…｜中文观点：OpenAI Campus Network: Student club interest…
总结 + 观点：OpenAI launches DeployCo, a new enterprise deplo…｜中文观点：更值得关注的是 OpenAI launches DeployCo to help busi…
总结 + 观点：How OpenAI runs Codex securely with sandboxing,…｜中文观点：Running Codex safely at OpenAI 更值得从实际采用价值来判断，…
总结 + 观点：OpenAI expands Trusted Access for Cyber with GPT…｜中文观点：从 Scaling Trusted Access for Cyber with GPT-5…
How NVIDIA engineers and researchers build with Codex
标签：#ai_engineering_blogs #core
作者：
原文：Teams use Codex with GPT-5.5 to ship production systems and turn research ideas into runnable experiments.
What Parameter Golf taught us about AI-assisted research
标签：#ai_engineering_blogs #core
作者：
原文：Parameter Golf brought together 1,000+ participants and 2,000+ submissions to explore AI-assisted machine learning research, coding agents, quantization, and novel model design under strict constraints.
AutoScout24 scales engineering with AI-powered workflows
标签：#ai_engineering_blogs #core
作者：
原文：Learn how AutoScout24 Group uses Codex and ChatGPT to speed development cycles, improve code quality, and expand AI adoption.
This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your...
标签：#x_profiles #extended
作者：
原文：This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) nitter.net/zan2434/status/2046982… There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML. Thariq (@trq212) x.com/i/article/205279610060… https://nitter.net/trq212/status/2052809885763747935#m
How ChatGPT adoption broadened in early 2026
标签：#ai_engineering_blogs #core
作者：
原文：ChatGPT adoption surged in Q1 2026, with fastest growth among users over 35 and more balanced gender usage, signaling broader mainstream AI adoption.
How enterprises are scaling AI
标签：#ai_engineering_blogs #core
作者：
原文：How enterprises scale AI: from early experiments to compounding impact through trust, governance, workflow design, and quality at scale.
链接：https://openai.com/business/guides-and-resources/how-enterprises-are-scaling-ai
OpenAI Campus Network: Student club interest form
标签：#ai_engineering_blogs #core
作者：
原文：Join the OpenAI Campus Network—connect student clubs worldwide, access AI tools, host events, and build an AI-powered campus community.
链接：https://openai.com/index/openai-campus-network-student-club-interest-form
OpenAI launches DeployCo to help businesses build around intelligence
标签：#ai_engineering_blogs #core
作者：
原文：OpenAI launches DeployCo, a new enterprise deployment company built to help organizations bring frontier AI into production and turn it into measurable business impact.
链接：https://openai.com/index/openai-launches-the-deployment-company
Running Codex safely at OpenAI
标签：#ai_engineering_blogs #core
作者：
原文：How OpenAI runs Codex securely with sandboxing, approvals, network policies, and agent-native telemetry to support safe and compliant coding agent adoption.
Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber
标签：#ai_engineering_blogs #core
作者：
原文：OpenAI expands Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber, helping verified defenders accelerate vulnerability research and protect critical infrastructure.
链接：https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber
Parloa builds service agents customers want to talk to
标签：#ai_engineering_blogs #core
作者：
原文：Parloa leverages OpenAI models to power scalable, voice-driven AI customer service agents, enabling enterprises to design, simulate, and deploy reliable, real-time interactions.
Advancing voice intelligence with new models in the API
标签：#ai_engineering_blogs #core
作者：
原文：Explore new realtime voice models in the OpenAI API that can reason, translate, and transcribe speech, enabling more natural and intelligent voice experiences.
链接：https://openai.com/index/advancing-voice-intelligence-with-new-models-in-the-api
Testing ads in ChatGPT
标签：#ai_engineering_blogs #core
作者：
原文：OpenAI begins testing ads in ChatGPT to support free access, with clear labeling, answer independence, strong privacy protections, and user control.
Introducing Trusted Contact in ChatGPT
标签：#ai_engineering_blogs #core
作者：
原文：Introducing Trusted Contact in ChatGPT, an optional safety feature that notifies someone you trust if serious self-harm concerns are detected.
链接：https://openai.com/index/introducing-trusted-contact-in-chatgpt
Simplex rethinks software development with Codex
标签：#ai_engineering_blogs #core
作者：
原文：Simplex boosts software development with ChatGPT Enterprise and Codex, reducing design, build, and testing time while scaling AI-driven workflows.
Better decisions at scale: How mathematical optimization delivers where intuition fails
标签：#engineering_ai_infra_blogs #extended
作者：
原文：In this post, we introduce mathematical optimization, explain how it fits within the broader AI landscape, and showcase real-world success stories where the Innovation Center has partnered with customers to deliver concrete results.
End-to-end encrypted ML inference with Amazon SageMaker AI and FHE
标签：#engineering_ai_infra_blogs #extended
作者：
原文：This blog has previously discussed FHE for ML inference in the post Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing, but this post goes a little further. That previous post showed how to implement FHE-based inference 'from scratch' by hand-crafting a linear-regression algorithm using a low-level library called SEAL. Instead, this post shows a much more flexible and higher-level approach based on concrete-ml, a high-level library built specifically for FHE-based inference. It supports several common types of models 'out of the box' and is even API compatible with the well-known ML library scikit-learn.
Amazon Quick ARNs: Cross-account migration and namespace permissions
标签：#engineering_ai_infra_blogs #extended
作者：
原文：In this post, we cover the structure of Amazon Quick ARNs and provide a practical mental model for working with them. By the end, you can look at an ARN and immediately understand what it means for your migration strategy, diagnose permission issues faster, and design multi-tenant architectures with confidence.
Evaluate your Amazon Nova Sonic voice agent at scale, no microphone required
标签：#engineering_ai_infra_blogs #extended
作者：
原文：In this post, we walk you through the Nova Sonic Test Harness, an open source framework that we built to solve both problems. It serves as a rapid iteration tool for tuning system prompts and tool configurations (run a conversation, see results, adjust, repeat) and as a comprehensive evaluation framework for validating voice agent quality at scale. It runs complete multi-turn conversations with Amazon Nova Sonic automatically, evaluates them using LLM-as-judge techniques, and can even detect cases where the model’s audio output doesn’t match its text output (audio hallucinations). No microphone required.
The Open Source Community is backing OpenEnv for Agentic RL
标签：#ai_engineering_blogs #core
作者：
原文：The Open Source Community is backing OpenEnv for Agentic RL