Oleg

⌥C as the trigger is the kind of choice that either becomes muscle memory in a day or never gets used. "Local" is the part that's going to drive most of the adoption here — Raycast has the shortcut space but the cloud dependency. Curious what the model footprint looks like at rest.

One-API-for-6-platforms is the fight Segment fought on the analytics side — hard-won. Question: how do you handle the platform-specific quirks that don't fit a unified schema (TikTok's spark ads, LinkedIn's matched audiences, etc.)? Do you expose platform-raw fields or force normalization?

The "thinking" angle is the interesting part here — most image models still feel like they just render a prompt. Curious what the latency hit would be vs standard DALL-E 3 or Gemini's nano-banana for the same prompt on something compositionally complex (3+ subjects + spatial relationships).

Hey Product Hunt 👋 I’m Oleg, co-founder of Fluenta. I’ve spent years building companies across different industries — some worked, many didn’t. I’ve lived through stalled launches, long stretches with no clear idea where to pivot, investors not replying, co-founders leaving under pressure, and the feeling that every new startup means rebuilding everything from zero. What always frustrated me is...

awesome product guys so how hard it is to set up the training environment for the very deep industry specific bottlenecks, rejections, etc?