rubyrfranklin2's comments

Real-time speech translation is something I think about constantly running heyvid.ai — we're always chasing that latency vs. quality tradeoff for multilingual video. JEPA's approach is interesting because it sidesteps the typical encode-decode bottleneck that kills most real-time pipelines. I'd be curious how it holds up on accented or fast speech. Back at Adobe I saw how even 200ms of lag completely destroyed the perceived quality of live demos. The latency budget for translation is so much tighter than transcription-only, so any architectural win like this is worth watching closely.

We ran into something similar at heyvid.ai — shipped AI-generated code without a proper review gate and ended up with a subtle bug in our rendering pipeline that took the team a week to trace. Not catastrophic, but it seriously eroded trust in the tooling for a while. Amazon's approach makes total sense at their scale. The honest reality is that LLMs are great at producing plausible-looking code and genuinely bad at knowing when they're wrong. Senior sign-off isn't overhead — it's what makes AI-assisted development actually sustainable.

We've been running filesystem-based agents internally at heyvid for about six months now, and the deployment story has honestly been the messiest part. You end up hand-rolling so much scaffolding that's not your core product. The 'Vercel for agents' framing clicks immediately — I remember when Vercel did the same thing for frontend and it just removed a whole category of yak-shaving. Curious how Terminal Use handles state persistence across runs; that's been our biggest headache when agents need to resume mid-task.