#OpenAI

AIMax Woolf reports that experiments using GPT-5.3-Codex to hyperoptimize batch prediction are going well. The post gives no benchmark scores, speedups, or further technical details.

AIMax Woolf reports that after GPT-5.2-Codex crashed and failed in his testing, GPT-5.3-Codex performed like a leap from Sonnet 4.5 to Opus 4.5 in coding. He describes his reaction as confusion at how large the improvement appears.

AIThe Codex app, a desktop app for agentic coding, launched with features including worktrees for parallel agent sessions, Skills, MCP connections, compaction, and automations. The author, who says they stopped opening their AI IDE for four days, now manages multiple Codex agents as a reviewer rather than writing code directly.

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.

AIOpenAI now lets ChatGPT Pro users give friends three months of ChatGPT Plus access, though gift cards are not available. Pro users who held that status as of December 1 will find their share link in email or as a notification in ChatGPT on the web.

AIOpenAI now lets users adjust specific ChatGPT characteristics, including warmth, enthusiasm, and emoji use. The settings are available under the Personalization section.

AIAndrej Karpathy's year-in-review lists the LLM paradigm changes he found most notable in 2025. He highlights Reinforcement Learning from Verifiable Rewards (RLVR), which drove most capability gains as labs ran longer RL training, and describes LLM intelligence as jagged, strong in verifiable domains and weak elsewhere. He also covers Cursor-style LLM apps, Claude Code running on the user's computer, vibe coding, and the case for a visual LLM GUI.

AIOpenAI's new ChatGPT Images is rolling out in ChatGPT starting today. The update offers more precise edits, stronger instruction following, and up to 4x faster generation while preserving lighting, composition, and likeness across edits.

AIOpenAI has published weights for a sparse model from Gao et al. 2025, used for qualitative results on bracket counting and variable binding, on Hugging Face under the openai/circuit-sparsity repository. The release includes a standalone Hugging Face implementation that loads the converted model and tokenizer with trust_remote_code and runs sample generation. The project is licensed under Apache License 2.0.

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.

AIChatGPT's weekly users in Latin America have nearly tripled year over year. The post credits ChatGPT Go with making it easier for more people in the region to use the service.

AIOpenAI is teaming up with Rappi to give people free access to Go in Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico, Peru, and Uruguay. The post is the third in a thread, so the initial terms and eligibility details are not included in this excerpt.

AIOpenAI has expanded access to ChatGPT Go across more of Latin America, offering an affordable way to use its most advanced AI tools. The post frames the move as bringing these tools to a wider group of users in the region.

AIAndrej Karpathy built hn-time-capsule, a tool that feeds each December 2015 Hacker News front-page article and its comment thread to GPT-5.1 Thinking for a retrospective analysis. The project, written with Claude Opus 4.5 in about three hours, processes 930 articles at a cost of about $58 and roughly one hour. Results include prescience and wrongness grades for commenters, and the project is hosted on his website with the intermediate data available for download.

AIARC Prize reports that the top Kaggle entry reached 24% on the ARC-AGI-2 private dataset at $0.20 per task, and that all winning solutions and papers are open source. The top verified commercial model, Opus 4.5 (Thinking, 64k), scored 37.6% at $2.20 per task, while a Poetiq refinement on Gemini 3 Pro reached 54% at $30 per task. The author argues that refinement loops are the main driver of 2025 progress, and says ARC-AGI-3 is planned for early 2026.

Why it matters: The post links 2025 competition results to a broader argument about refinement loops, showing how benchmark outcomes are being read as evidence of AI reasoning progress.

AIChip Huyen responded to Sam Altman's post saying ChatGPT now follows custom instructions to avoid em-dashes. The main post itself only reads "Sam!!!", so its reaction is conveyed mainly through the quoted context.

AIFactory has announced a partnership with Amazon Web Services and made its Droids agent platform available on the AWS Marketplace. Enterprise teams can use existing AWS Enterprise Discount Program commitments to buy Factory, with Droids accessible from CLI, Terminal UI, Web, Slack, Linear, and an IDE overlay. The source cites 31× faster feature development, 96.1%+ reduction in migration times, and 95.8% reduction in incident resolution times.

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.