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OpenAI / ChatGPT

Follow GPT models, ChatGPT and Sora products, company strategy, and personnel at OpenAI.

46 top picks · 23 in the past 30 days · chosen from 640 items collected

Latest pick

Top picks archive · Page 3

Top picks 41–46 of 46

May 6

May 6Wed
  1. OpenAI Alignment Research BlogAI score62

    OpenAI finds accidental chain-of-thought grading in several RL runs but no clear monitorability loss

    AIOpenAI reports that its automated system found accidental chain-of-thought grading in RL runs for several released models, including GPT-5.4 Thinking and GPT-5.4 mini. Its analysis found no clear reduction in CoT monitorability, though the company says subtler effects cannot be ruled out. OpenAI says it still avoids grading CoTs during RL and has fixed the affected reward pathways.

    Why it matters: The post shows how accidental chain-of-thought grading was detected and tested, giving a concrete method for checking monitorability risks in RL training.

Apr 30

Apr 30Thu
  1. OpenAI Alignment Research BlogAI score79

    OpenAI's Auto-review lets Codex agents act without constant human approval

    AIOpenAI released Auto-review in Codex, which replaces user approval at the sandbox boundary with a separate agent that approves or denies boundary-crossing actions. In internal deployment, Codex sessions stopped for human approval about 200x less often than in manual mode, and Auto-review approved around 99% of escalated actions. The post also states that Auto-review is not a guarantee of security and cannot protect against model scheming.

    Why it matters: The post explains how Auto-review replaces human approval at the sandbox boundary, with internal deployment figures and stated limits that help readers judge the tradeoff for coding agents.

Apr 21

Apr 21Tue
  1. Nick TurleyAI score67

    ChatGPT Images 2.0 launches with better instruction following and dense text rendering

    AINick Turley announced ChatGPT Images 2.0 as a major advance in image generation, citing better adherence to detailed instructions, rendering of dense text, and more accurate understanding of the world. He said the model can spend extra time planning and refining outputs for tasks needing more accuracy and clarity, and that users have generated over 1 billion images with ChatGPT.

    Why it matters: The post names concrete gains in instruction following, dense text rendering, and optional extended thinking for image output, which helps readers gauge practical scope.

Jan 7

Jan 7Wed
  1. Nick TurleyAI score72

    OpenAI launches ChatGPT Health for connecting medical records

    AIOpenAI is launching ChatGPT Health, a dedicated and private space where users can securely connect apps and medical records. The launch starts with a small group of users from the waitlist, with access expanding over the coming weeks.

    Why it matters: The post names the access path and a dedicated space for health records, which matters for judging how sensitive data would be handled.

Dec 11, 2025

Dec 11, 2025Thu
  1. Nick TurleyAI score78

    OpenAI introduces GPT-5.2 in ChatGPT for professional work

    AIOpenAI is introducing GPT-5.2 in ChatGPT, describing it as its most advanced model series for professional work. GPT-5.2 Thinking is positioned for tasks such as building spreadsheets and presentations, writing and reviewing production code, and analyzing long documents. The post says it beats or ties industry professionals on well-specified knowledge work tasks spanning 44 occupations 70.9% of the time on GDPval, and GPT-5.2 Instant, Thinking, and Pro begin rolling out to all tiers, starting with paid plans.

    Why it matters: The post links the model's professional-work focus to GDPval results across 44 occupations, showing how the claimed capability was measured.

Sep 11, 2024

Sep 11, 2024Wed
  1. Cognition Blog (Devin, Windsurf)AI score60

    Cognition tests OpenAI o1 models in Devin's coding agent benchmark

    AICognition tested OpenAI's o1-mini and o1-preview in a simplified Devin-Base agent, comparing them with GPT-4o on its internal cognition-golden benchmark. The chart reports Devin-Base scores of 25.9% with GPT-4o, 34.6% with o1-mini, and 51.8% with o1-preview, versus 74.2% for the production Devin. The post also describes the benchmark's realistic environments, simulated users, and agent-based evaluation.

    Why it matters: The post explains how Cognition evaluates coding agents with autonomous, environment-based tests, which shows how base-model swaps are measured in practice.