tbh, i think we’re hitting a “Trust Wall.” as AI gets better at generating perfect landing pages and “perfect” video, humans are starting to crave the mistakes. the stutters, the bad lighting, and the awkward eye movements are actually becoming trust signals because they are harder for current real-time models to mimic without latency. i’ve been obsessing over this “Trust Gap” while building a project called Vouchy ( https://vouchy.click/ ). [Affiliation: i am the solo dev]. i wanted to see if i could use AI to solve for “human anxiety” rather than just replacing the human. i built a teleprompter system that helps people record video testimonials without freezing up, but the technical challenge was making it stay “human” enough to be believable. Technical Breakdown (Substance for Rule 3): The implementation relies on a specific synchronization between the MediaRecorder API and a custom React-based teleprompter hook. I used requestAnimationFrame for the scroll logic to maintain a consistent 60fps refresh rate, which is critical because browser-based video encoding is CPU-intensive. If the scroll jitters, the user’s reading flow breaks, and the video looks robotic. One benchmark we achieved was reducing the “AI Polish” (a text-to-text transformation engine) latency to under 1.2s by using Claude 3.5 Sonnet on Edge Functions. This avoids the cold-start overhead of traditional serverless setups and makes the UI feel “instant” for the user. A major technical limitation we are still fighting is “Pupil Gaze Vectoring.” When a user reads the teleprompter, the lateral movement of the eyes is a dead giveaway. We are researching post-processing models to correct the gaze, but the real lesson learned was that “raw” video—even with small reading errors—converts better than highly polished, filtered output. The “uncanny valley” is very real when people try to look too perfect on camera. submitted by /u/alazar_tesema
Originally posted by u/alazar_tesema on r/ArtificialInteligence