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Tibo (@thsottiaux) said on X that he reset usage limits for Codex and ChatGPT Work for the weekend, mentioning capacity for up to 100,000 Luna threads.
A social media post argues that well-defined goals and strict acceptance criteria are central to making AI-assisted work more reliable. The accompanying discussion also recommends setting boundaries, requesting intermediate outputs, and adding self-checks.
A recent industry view describes Codex as a strong software-development harness, but one that could look basic within months. The argument reflects a broader shift toward agentic workflows that depend on more capable models, tools, and infrastructure than a single laptop can provide.
A social-media post questions whether Claude Opus 5 is optimized mainly for leaderboard results and points to failures resembling problems seen in older models. The available material is mostly secondary reporting and speculation, with no clearly verifiable primary announcement establishing the broader claims.
OpenAI says GPT-5.6 Luna now costs $0.20 per million input tokens and $1.20 per million output tokens, an approximately 80% reduction. The company attributes the change to efficiency improvements and says Terra pricing will also fall by 20%.
A user says they now manage four computers running Claude Code and Codex from a phone or iPad, relying largely on voice input.
A social media post claims that an OpenAI employee used Codex to submit and ship a Temporary Chat improvement to roughly one billion users. The available evidence confirms Codex-assisted development workflows, but not this specific change or its rollout.
Kimi Slides is presented as an end-to-end presentation tool for research, outlining, design, editable charts, and export. The core workflow is supported by product materials and demonstrations, although public pages differ on which Kimi model powers the experience.
Available evidence does not support the absolute claim that Sol cannot build products, Opus 5 cannot code, and only Fable 5 excels at both. Sources disagree substantially on the models’ strengths.
OpenAI says an internal version of Astra, its next major model, produced new results on 10 long-standing problems in mathematics and theoretical computer science. The company estimates the token cost of finding the solutions at roughly $2,000 at Sol API rates.
A social media post questions whether Claude Code and Codex have both gone several days without new versions. The available evidence does not establish Claude Code’s status, while OpenAI’s Codex repository still lists recent releases.
A user describes splitting complex software tasks across models: Fable 5 produces the technical plan, Codex implements it, and Fable 5 reviews the result. The workflow also uses context compaction, prompt caching, and explicit goals to reduce handoffs and manage cost.
Community documentation and examples indicate that Claude Code can delegate work to subagents running in separate contexts and assign them different models. This enables teams to use stronger models for planning and less expensive models for scanning, summarization, or execution tasks.
OpenAI has introduced GPT-Live, a full-duplex voice model that can listen and respond at the same time while delegating search and deeper reasoning to a background model.
Columbia University is a private research university in New York City and a member of the Ivy League. Founded in 1754 as King’s College, it now includes undergraduate, graduate, and professional schools.
A user describes a personal workflow built around directing Claude Code and Codex from a phone or iPad while the agents run on several computers. Voice input, mobile terminals, and remote-control features make it possible to review progress and intervene without carrying a laptop.
OpenAI has released the open-source Codex Security CLI and TypeScript SDK for analyzing code security. The tools can scan repositories, validate and track vulnerabilities, verify fixes, and run security checks in CI/CD pipelines.
The post is an ambiguous social-media sentence that appears metaphorical. Available evidence does not establish a connection to an AI product, launch, or event.
OpenAI says GPT-5.6 Sol helped optimize the production infrastructure and inference stack used to run it. The work spans GPU kernels, routing, scheduling, caching, and model implementation; a secondary report attributes roughly 20% lower serving costs and more than 15% better token-generation efficiency to the changes.
OpenAI reports that retaining reasoning across turns and using context compaction raised GPT-5.6 Sol’s public ARC-AGI-3 score from 13.3% to 38.3%. The result depends heavily on the evaluation harness, so claims that the model is definitively state of the art require caution.