Zop Dev

Running models locally is great for privacy, but I'm curious about the hardware side. What's the minimum spec to run something like Llama 70B usably? For teams thinking about replacing cloud AI APIs with this, the hardware cost tradeoff matters a lot.

13k GitHub stars is hard to ignore. The auto-adapt to layout changes part is what I want to understand better, does it detect the change and re-train the robot automatically, or does it flag it for manual review?

The Fastlane + GitHub Actions setup is a nice touch. One thing I've seen catch teams off guard though: multiplatform builds eat through Actions minutes pretty fast. Any tips on keeping build costs reasonable at scale?

Business videos use case sounds useful, but what's the licensing on the output? Can I use generated videos commercially without restrictions, or are there usage limits tied to the plan?

The SEO-friendly pages angle is what I want to understand better. Is it static pages with proper schema, or does it depend on the product getting backlinks organically? Because "SEO-friendly" means very different things depending on how it's built.

The charts and tables from voice part is what got my attention. Most writing tools stop at text. how that works in practice, do you describe the data out loud and it builds the chart, or is there a separate flow for that?

Voice logging beats typing for sure. But what happens when you say "a bowl of dal"? Does it ask how big the bowl was, or just pick a default serving size? Because if I still have to remember portion sizes in my head, that's kind of the same problem.

Facial recognition for personal galleries is clever. Saves photographers a lot of manual sorting. One question though, how do attendees opt out if they don't want to be recognized? Handling that well could make you stand out from every other event photo tool.