1. Home
  2. Detail

@aicatsmeowmeow: kneeding dough meow meow #cat #aicat #cuteanimals #catsoftiktok #cute

aicatsmeowmeow
aicatsmeowmeow
Open In TikTok:
Region: US
Sunday 08 November 2026 01:37:32 GMT
79
2
0
0

Music

Download

No Watermark .mp4 (1.82MB) Watermark .mp4 (0MB) Music .mp3

Comments

There are no more comments for this video.
To see more videos from user @aicatsmeowmeow, please go to the Tikwm homepage.

Other Videos

On Black Friday sale‼️ Don’t wait until Xmas prices bring this ⬆️‼️🤎🤍 #anastasiabeverlyhills #nudelipcombo #christmasgift
On Black Friday sale‼️ Don’t wait until Xmas prices bring this ⬆️‼️🤎🤍 #anastasiabeverlyhills #nudelipcombo #christmasgift
#ياحلالي
#ياحلالي
#StormArena #FreeFire
#StormArena #FreeFire
Apply RETINOL like this! 🔥 Using the Dr. Althea Pure Retinol 0.15% Cream with 1,500ppm of retinol~! Who wants to try this new launch? 👇 #stylekorean #stylekorean_global #kbeauty #koreanskincare #skincare
Apply RETINOL like this! 🔥 Using the Dr. Althea Pure Retinol 0.15% Cream with 1,500ppm of retinol~! Who wants to try this new launch? 👇 #stylekorean #stylekorean_global #kbeauty #koreanskincare #skincare
#giftboost #рекомендации
#giftboost #рекомендации
I pointed the crawler at my own prompts instead of the web. There are 195 prompts, and each one is treated as a decision point rather than plain text. Every prompt is evaluated through a structured set of questions: - Is it relevant? - Is it grounded? - Is it ambiguous? - Does it require approval? Each evaluation produces a probability score. The routing logic is intentionally deterministic—it has no opinions of its own. - p ≥ 0.95 → route directly into the execution/code path without invoking an LLM. - p < 0.95 → escalate to Claude Sonnet 5.5, the only model responsible for reasoning. - Anything that appears to require human approval is automatically handed off to me. In one execution: - 168 of 175 prompt decisions were resolved automatically. - 133 terminated within deterministic if branches. - 24 were escalated to Sonnet. - 11 ultimately required human approval. One interesting observation: the token
I pointed the crawler at my own prompts instead of the web. There are 195 prompts, and each one is treated as a decision point rather than plain text. Every prompt is evaluated through a structured set of questions: - Is it relevant? - Is it grounded? - Is it ambiguous? - Does it require approval? Each evaluation produces a probability score. The routing logic is intentionally deterministic—it has no opinions of its own. - p ≥ 0.95 → route directly into the execution/code path without invoking an LLM. - p < 0.95 → escalate to Claude Sonnet 5.5, the only model responsible for reasoning. - Anything that appears to require human approval is automatically handed off to me. In one execution: - 168 of 175 prompt decisions were resolved automatically. - 133 terminated within deterministic if branches. - 24 were escalated to Sonnet. - 11 ultimately required human approval. One interesting observation: the token "ask" received an approval-confidence score of 0.544. Since the system lacked sufficient confidence, it deferred the decision instead of hallucinating. The average confidence across the entire run was 0.925. Everyone is crawling the web for additional context. The real context has been sitting inside the prompt all along—it just hasn't been interpreted correctly. #xuhuong #AI

About

Legal