I'm building DineClip, a web app that turns restaurant photos into promotional videos, with help from AI coding tools. The screenshot below shows the photo-input workspace.
DineClip’s photo-input workspace. This is an interface screenshot, not a generated video.
The hardest part has been creating something differentiated that meets a real user need. For video generation, that becomes a practical question: which model can produce a result realistic enough for the product?
I've been integrating and tuning different video-generation models. That work has been difficult, and it has also made me think more carefully about when to keep adjusting the implementation and when to give an existing approach more time.
Model selection involves product decisions
Getting an API call to work and getting a useful product result need separate validation.
For a restaurant video app, I need to care about more than whether a video is generated. The result needs to look credible and support what the user wants to communicate.
Food, people, and the restaurant setting are concrete things to inspect. These are evaluation criteria to use as I continue testing, not evidence that I've completed a controlled model comparison or found a clear winner.
I'm still working through the integration. I want each adjustment to address a specific problem, rather than changing the approach whenever another model or suggestion appears.
More context should help AI make a better judgment
Alongside model integration, I've been learning SEO and collecting keyword results, search-intent examples, and website analytics. I share that information with AI to give it a fuller picture of the project.
I've had to make one working principle explicit: more context does not always call for another change.
Sometimes, new information adds background. It can explain an earlier decision or give us more confidence in continuing with the existing plan.
If the AI and I still consider that plan reasonable, we can keep executing it and allow time for feedback.
I made this point during our recent SEO discussions. We had already shipped page improvements that needed observation. New search results could inform our next decision without requiring another immediate round of page edits.
I don't want to add work simply to make a discussion look productive.
Three questions I want answered before another change
I'm turning that principle into three questions for future development discussions:
- Which judgment does this new information change? If it only adds background, record it first.
- What specific problem would the proposed change solve? Ideally, it should address an observable output issue, a usability problem, or a reason the existing approach no longer holds.
- Is there enough evidence to act now? If the current plan is still waiting for feedback, continuing and observing can be a reasonable decision. The same questions apply to model integration. A concrete problem in a generated result gives me something to investigate. Without evidence that the approach needs replacing, I can keep the current choice for now.
This is a working practice I'm developing as I build. I don't yet have measurements showing how much time it saves.
Separating suggestions from actions
For now, I want to sort AI suggestions into three groups: act now, wait for validation, and keep as an option.
A clear problem in model output may need immediate attention. SEO performance on recently updated pages needs observation. A newly discovered content direction can remain an option.
I'll keep giving AI more context and working on the product. What I want from each discussion is a clearer reason to make a change, or a clearer reason to leave the current approach in place.
When you build with AI, what makes a new piece of information strong enough to trigger a change? When do you decide to let the existing approach run longer?
Writing disclosure: The project experiences and opinions are my own. AI helped organize the draft and translate it into English.
