$STONK
Cooling DownSnapshot Window: 2026-09-18 17:50 UTC · ← Back to Crypto Overview
Social Momentum Summary
Total Engagement - Comments: 13, Retweets: 1, Likes: 12, Impressions: 85
Verbatim Community Citations & Social Evidence 2 source posts analyzed
Agent memory is no longer just context. Persistent skills can change future behavior, so treat them as executable state rather than harmless notes. @SentientAGI ’s new EvoSkill v2 is built around exactly that idea: agents learn from failed runs by writing reusable skills for
AI visual note: **Image caption:** A bar chart titled "Every score went up when we fixed the test," comparing DeepSeek V4 Flash's performance on broken vs. fixed spreadsheet tests across three conditions—No coaching (48→73), Frontier coach Claude Opus 5 (60→89), and worker self-coaching (59→86)—out of 360 attempts. **Connection to the post:** The chart illustrates the core premise of EvoSkill v2—that agents learning from failures (even without external coaching) can lift their own scores by ~27 points (59→86), almost matching what a frontier coach achieves (60→89)—reinforcing the argument that persistent, self-written skills are executable state worth treating as first-class.
One of the strongest parts of this thesis is the builder behind it. ~ @convik_ai / $CONVIK is built by @unseenfleet , who publicly lists previous AI development experience at @elizaOS and work at @enter_delta . That background fits the product: an autonomous agent that researches
AI visual note: A '$CONVIK' poster with the tagline 'CONVICTION IN MOTION' featuring a penguin character in a cap standing on a futuristic cityscape with glowing cubes.