{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/34a236e6e2e24d20bbbc0d1367276a24\" frameborder=\"0\" width=\"1670\" height=\"1252\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1252,"width":1670,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1252,"thumbnail_width":1670,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/34a236e6e2e24d20bbbc0d1367276a24-6e51381912deb54d.gif","duration":299.44,"title":"TickyTalky Demo: A Self-Improving Creator Strategy Agent","description":"TickyTalky is a self-improving AI content strategist for TikTok creators. In this demo, we show how the system backtests a creator’s real posting history with a Student, Dealer, and Grader loop: the Student generates ideas, the Dealer reveals historical posts one at a time with no look-ahead, and the Grader evaluates blind outcomes.\n\nThe demo shows cached analysis for @technically.david, including profile stats, green/red video verdicts, a warm-vs-cold learning curve, and a seven-day gameplan of recommended videos. We also explain the technical training system: DeepSeek-V4-Flash served from a single NVIDIA DGX Spark through patched vLLM, plus a NemoClaw/OpenShell recursive harness that converts failures into persistent Playbook lessons without retraining model weights.\n\nBuilt by Giga-Pradhi Team for the AITX Community x NVIDIA Claw Agent Hackathon.\n\nwww.TickyTalky.com"}