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QuickReset AI tracks OpenAI's public Codex usage resets by monitoring Tibo's announcements and historical patterns, letting you see when resets happen and get email alerts before your allowance refreshes. It separates confirmed schedules, public signals, and historical forecasts so you can plan around rate limit resets without guessing.
- Tracks 26 weeks of Codex reset history with source-linked announcements from @tibo, showing direct resets (immediate refills) and banked resets (saved credits) separately
- Generates a daily forecast based on recent reset intervals and public signals, currently showing 35% chance for the next reset
- Sends email alerts when resets approach so you can use remaining allowance before limits refresh
- Only reports public bonus events and cannot trigger, grant, or sell resets — forecasts are estimates, not guarantees
Doug Boneparth tweets that his year is unfolding exactly as he intended. The brief post reflects his confidence in meeting current goals.
- Doug Boneparth tweeted a single line: "My year is progressing as planned."
- The tweet contains no specifics about which goals, projects, or milestones are on track.
- The vagueness appears deliberate, inviting speculation or signaling confidence without details.
This explains how to use a “premortem” prompt with AI—telling it your plan already failed six months later—to force it to list failure scenarios and warning signs. It then ranks the most likely and dangerous failures, reveals hidden assumptions, and suggests plan adjustments.
- Asking AI "is this plan solid?" produces biased cheerleading because it's trained to be affirming, not critical
- The fix is a "premortem" prompt: telling the AI the plan already failed six months from now and asking it to explain why, which surfaces failure scenarios and early warning signs
- Kahneman considers premortems his top decision-making tool, and companies like Google, Goldman Sachs, and P&G use them before major launches
- A follow-up synthesis step has the AI rank the most likely failures, name the biggest hidden assumption, and rewrite the plan to close those gaps
The article unpacks the long history of “world models,” from Schmidhuber’s 1990 proposal through Sutton’s Dyna and Ha & Schmidhuber’s 2018 implementation, to LeCun’s JEPA framework and the new LeWorldModel paper. It explains LeCun’s real contribution—predicting in latent space with SIGReg to prevent collapse—while calling out overhyped funding claims and noting limitations and prior art.
- SIGReg replaces the usual grab-bag of anti-collapse heuristics with a simple sketched Gaussianity test, projecting embeddings onto random lines and pushing them toward normality—loss is just L_pred + λ·SIGReg.
- LeWorldModel is tiny (15M params, ViT-Tiny + Transformer) and trains on one GPU in hours, with no stop-gradients, teacher networks, or pretrained backbones.
- Latent-space CEM planning hits ~1 second versus DINO-WM's 47 seconds, a 48x speedup from compression.
- The core ideas (predicting in latent space, imagining futures to plan) trace back to Schmidhuber's 1990 work and Sutton's 1991 Dyna, making LeCun's contribution more of a refinement than an invention—undercutting the hype around the funding round.