Show IH: I launched an MVP to solve a problem I personally hated
While validating product ideas, I kept running into the same frustrating problem:
Understanding why users hate an app means manually reading hundreds of repetitive App Store and Google Play reviews.
Across iOS.
Across Android.
For your own app.
And competitors.
It’s honestly miserable.
So I built AppRoast
AppRoast — paste any app and get an AI-powered roast based on real app reviews.
The interesting part isn’t summarization.
It’s pattern detection:
🔥 recurring complaints
😍 what users actually love
⚡ quick wins for developers
📱 iOS vs Android differences
📊 sentiment from real ratings
Right now it’s intentionally a small MVP.
I’m validating whether people actually care before investing heavily into the full vision.
To keep things fast (and free), AppRoast currently analyzes a focused set of recent reviews while still surfacing strong patterns quickly.
The bigger vision isn’t just “AI summaries.”
I’m exploring things like review monitoring, sentiment alerts, competitor tracking, automatic reports, and historical analysis — basically making app feedback something founders don’t have to manually babysit.
Would genuinely love feedback from other founders/builders here:
Would you actually pay to stop reading app reviews manually?
And if yes — what would matter most?
• monitoring?
• alerts?
• competitor tracking?
• deeper review analysis?
Curious if this problem resonates with anyone else building products.
Solid wedge. The 'why do users hate this app' problem is real and most founders dodge it because reading reviews is brutal. One thing that would move this from interesting MVP to obvious purchase: do not just show the patterns, tie them to a competitor benchmark. 'Your app has 3x the auth complaints of the category leader' is a screenshot a PM forwards to their boss to justify a sprint. Right now the output is descriptive. The version that sells is comparative.
Thanks for the post - you’ve hit the nail on the head.
In the paid plans, users will be able to monitor their own apps and connect direct competitors to each monitored app.
As you mentioned, the reports will provide comparative analysis, trend insights, and actionable suggestions on what to improve so your app can outperform competitors — or where to avoid making the same mistakes.
The "problem I personally hated" origin is the strongest signal an MVP can have. You're not guessing at the pain.
The monitoring and alerts angle is where I'd focus next. One-time roasts are useful but don't create retention. If AppRoast can tell a founder "your top complaint shifted from 'crashes on iOS' to 'onboarding confusion' this month" — that's something they'd pay for recurring.
Competitor tracking feels like the highest-value feature to validate first. Founders care deeply about what users say about their competitors, often more than what users say about their own apps.
This is genuinely one of the most useful comments I've received — thank you.
You nailed it on retention. One-time roasts are a hook, but monitoring is where recurring value lives. The 'your top complaint shifted this month' use case is exactly what I want to build toward.
Competitor tracking was always a core priority — in the paid plans, users will be able to monitor their own apps and connect direct competitors to each monitored app. The reports will provide comparative analysis, trend insights, and actionable suggestions on what to improve so your app can outperform competitors — or where to avoid making the same mistakes.
But the way you framed it clarified something: founders caring more about competitor pain than their own is a powerful acquisition angle I hadn't fully thought through. That changes how I'll position it going forward.
That acquisition angle writes itself as a headline too — "Find out why users hate your competitor's app." People click that faster than "analyze your own reviews." Good luck with the monitoring build.
I came here to see if anyone had suggested this! The social listening aspect of this is gold.
This is a strong MVP because the pain is very specific. App reviews are technically public feedback, but in practice they are too noisy to use: repeated complaints, platform differences, buried feature requests, and competitor signals all get lost unless someone manually reads hundreds of reviews.
The bigger opportunity is not “AI roast.” It is turning messy app-store feedback into a product decision layer for mobile founders. Monitoring, competitor tracking, sentiment shifts, and quick-win detection all point toward something founders could actually keep running weekly, not just use once.
That is also where the naming becomes important. AppRoast is catchy for the MVP, but it may trap the product in a funny one-time roast frame. If the bigger vision is review intelligence and competitor feedback monitoring, Beryxa.com would feel more serious and SaaS-grade than a name built around roasting.
Love the positioning 😂
“Your users are angry. Here’s why.” immediately made me try it.
The roast framing is fun, but underneath there’s a pretty useful founder tool here.
For a small MVP, I’d test whether the first screenshot/one-liner makes the hated workflow obvious before adding features. With Kinetic Override, “Android 15+ no-root macro recorder for repeated taps/swipes/long-press loops” works better than the broader “automation app” label because users can instantly self-select.
This is a great concept!
The automated approach to understanding user frustration is the right frame. We hit the same wall in regulatory monitoring - manually tracking congress.gov and aggregating newsletters consumed 45 min/day before we built goffer.ai to handle it. The pattern that made it click: rather than trying to read everything, we defined specific triggers - keyword and sponsor combinations fire Gmail labels, floor votes trigger SMS. Stopped synthesizing, started getting alerted when the threshold we cared about crossed.
The 'founder hated this problem' origin tends to produce better products because your instinct for what's broken is calibrated from lived experience, not user research abstractions. What's the specific reading fatigue you're solving - the volume problem or the synthesis problem?
Love this approach because some of the best MVPs come from problems founders personally experience. Starting with a real pain point usually gives stronger validation and a clearer direction for the product.
Foundersbar helps startups turn these founder frustrations into structured MVPs focused on solving real user problems first.
This definitely resonates.
The real pain isn’t “summarizing reviews” — it’s finding repeated patterns across messy feedback fast enough to actually act on them.
For founders, I think the most valuable parts would be:
I’d personally pay more for “tell me what users keep complaining about before it becomes a bigger problem” than a one-time summary.
You nailed the priority order - and honestly, that's exactly how I think about AppRoast too.
The one-time roast is almost a demo tool. Something fun and shareable that shows what the product can do - easy to try, easy to post on socials. But the real value is elsewhere.
The actual product I'm building is:
"Really cool idea! I'm building TraderTracking — a trading journal app — and reading user reviews manually is definitely a pain point I relate to. For your question about what matters most: I'd say monitoring + alerts would be the killer combo. As a solo dev, I don't have time to check reviews daily, but I'd definitely pay to get notified when users report a recurring issue. The competitor tracking angle is also interesting — knowing what users complain about in rival apps could help shape your own roadmap. Good luck with the MVP!"
Honestly the interesting part here is probably not the AI summarization itself, it’s the pattern aggregation across large volumes of feedback.
Most founders can manually read 20 reviews.
Almost nobody wants to read 2,000 repetitive reviews across iOS, Android, and competitors just to figure out the same 3 recurring pain points.
I could especially see the monitoring + competitor tracking angle becoming valuable once the historical trends start compounding over time.
Exactly — summarization is the easy part, pattern detection at scale is the actual hard problem worth solving.
20 reviews? Anyone can read those. But when you're trying to extract the same 3 recurring complaints from 2,000 repetitive reviews across iOS, Android, and 2 competitors — that's where the manual approach completely breaks down.
The compounding value over time is what I'm most excited about. A single roast gives you a snapshot. But 6 months of monitoring gives you a trend — 'this complaint appeared in March, peaked in June, and is now declining after your last update.' That's the kind of signal that actually changes product decisions.
That's the product I'm building toward.
Exactly. The summarization itself is quickly becoming commodity.
The more interesting layer is:
signal extraction over time.
One bad review means almost nothing.
But seeing the same complaint repeatedly emerge across:
starts becoming real product intelligence.
Especially when you can track:
when a complaint appears,
how fast it spreads,
and whether updates actually reduced it months later.
That’s where things get much more interesting than simple AI summaries.
nice one