A small scope can still have solid boundaries.
For an AI-built MVP, I’d happily do matching or onboarding manually. I’d still check who can read each user’s data.
Which part of your product are you deliberately keeping manual?
@mlypniahov I’d separate infrastructure you can postpone from safeguards you need for the first user. Manual matching seems fine for an early interview platform; keeping each person’s interview data private still matters. What did you cut from the two-week version?
@meHirenKavad What happens when a push builds successfully but the new API fails its health check? Keeping the previous version serving while showing the failed deployment’s logs would make that workflow much easier to trust.
@InkCalc Does the AI see just the selected calculation, or the whole note? I’d want to preview that context before sending it, especially if a scratchpad mixes a quick unit conversion with client numbers.
I keep getting more app ideas than I can build.
A filter I want to try: can I help one person solve the problem manually before writing any code?
What is the smallest test you use to decide an idea deserves a weekend?
@YvonneYaoTech@Google The real-phone testing detail stood out. Can someone change one photo or its order after the preview without regenerating the whole film? Making a small correction easy seems especially useful when the goal is creating independently.
@AllianceDouble@ErnestoSOFTWARE Offline is a clear promise for a journal. How are you thinking about moving entries to a new phone? I’d want that recovery story to be just as clear as the privacy story before trusting it with years of writing.
@Eleniyan006 What should the assistant help someone do inside Toolkit that the existing screens cannot? That would be my first check before adding more chat features: one useful task someone can finish more easily.
A useful check for an AI-built app: what does it show when an API stops responding?
“No data” and “everything is fine” should look different.
Before adding another feature, try breaking one dependency and see what your user sees.
@shahriarmeharaz That answers my concern. Keeping the style independent of the dimensions sounds like a good fit for that workflow. Thanks for explaining, Shahariar.
@SomePsychagogue That clears it up. One test I’d add: forget a preference, start a fresh chat, then ask something that used to depend on it. I’d check saved summaries too, so a copy of the preference doesn’t quietly bring it back.
@shahriarmeharaz That’s the flow I had in mind. One thing I’d check with Save Style: switching between a tall mobile screenshot and a wide desktop one. Keeping the same look without having to fix the crop each time would make it much more useful for build updates.
@yannisazouz Saving the idea without having to finish the post sounds useful. I’d want that draft list to feel like a shelf I can pick from, not another backlog to clear. Thanks for sharing how you handle it.
@Goura_vk That makes sense. I’d also have it check the lockfile for the resolved version, then run a small test against the API it plans to use. Matching docs is a good start; seeing the call actually work is the next check.
@SomePsychagogue Keeping the memory separate from the model is the part that interests me. Can a user inspect and correct what Eidolon remembers? I’d want an old preference to be easy to change, rather than quietly carried into every new model.
@alexii_9 Would you show when each source last synced? I’d want to know the difference between “everything is fine” and “Sentry hasn’t updated yet.” Curious how you’re handling that in the single answer.