@omarsar0 the 256k no-overflow run is the real signal. you still get +9.2 when nothing hits the wall so the policy is learning which stale exploration to drop so resume doesnt rewalk dead ends, not just surviving context length
@ChristosTzamos fixed weights only move the hard part to the write path. if noisy overwrites bury the cell the learned indexer was trained to hit, growing memory never surfaces the new skill at inference
@idavidrein the failure mode is grading the agent's own diary. does Harbor seal trajectories on a write-once path the sandbox cant truncate, or is the eval still trusting whatever the agent leaves on disk?
@levelsio when build speed outruns ideas the bottleneck is intake. keep a queue of paid-user pains from support and churn notes and only open a build slot when one clears, otherwise you just ship faster into nowhere
@wen_kaiyue readiness gate is carrying this. drop it and partial rollouts feed the policy bad critic advantages so the run stalls. the 2.5x FLOPs save only kicks in after that per-problem error check flips
@ArtificialAnlys Claude Code vs Codex vs Antigravity on one bar chart mixes scaffold skill with model skill. the 68 vs 63 race is partly which harness you bolted on