If you’ve got something working but need another opinion, drop it here.
Share the link, what it does, who its for and the main thing you want feedback on. Landing page, onboarding, pricing, design, actual product - anything is fine.
Dont just dump a link though. Give enough context for people to actually help.
If you post yours, try review someone else’s aswell. Honest feedback but dont be a dick lol.
I’m building Kaviqa because normal flight search usually treats the airport you enter as fixed.
The current beta checks whether positioning to another airport or splitting the trip into separate tickets changes the fare. It then shows the possible saving beside the extra travel time and self-transfer risk.
I’m looking for people who have a real international trip in mind. Run one search at https://www.kaviqa.com and tell me:
- Did it return a route you would genuinely investigate?
- What result or number did you not trust?
- What obvious alternative was missing?
- What would the tool need to show before you would consider a separate-ticket option?
I’m also researching a future cash-and-points comparison, but that is not live yet. I want to know what data you would need before trusting a mixed cash and award itinerary.
I built the product. This is a beta-research request, not a neutral recommendation.
I’m an avgeek building Kaviqa. The beta currently searches for positioning flights and split-ticket options, then shows the saving beside the added travel time and booking risk.
I need people who actually book international flights to try a real route and tell me where it falls apart. I care much more about what is missing or untrustworthy than whether it looks nice.
A few things I’m trying to learn:
- Which itinerary did you expect to see but could not find?
- Which result would you refuse to book, and why?
- What part of your normal flight comparison is still manual?
- If a tool compared a cash leg with an award leg, what information would you need before trusting it?
The cash-and-points piece is planned, not live. I’m trying to validate it before I build it.
I built the site and this is product research, not a neutral recommendation.
I’m an avgeek and one of those people who can happily spend hours comparing routes. I’m building Kaviqa because most flight searches show the obvious itinerary and stop there.
Right now it checks positioning flights and split-ticket options, then puts the saving beside the extra time and booking risk. The next part I’m exploring is trip-level cash and points combinations.
Frequent flyers usually have their own weirdly effective system. What part of planning are you still doing manually every time? What sends you into five tabs, a spreadsheet or several airline apps before you can make a decision?
I built the tool, so this is openly product research. There is no link or sign-up pitch. I want to understand what would genuinely save frequent flyers time, including the stuff normal flight searches completely ignore.
I’m an avgeek building a flight search tool, and the points side is the bit I don’t want to bullshit my way through.
The tool currently checks positioning flights and split tickets for cash fares. What I want to build next is a proper trip-level comparison: cash for one leg, an award seat for another, transfer partners, taxes and fees, mixed cabins and the risk of separate tickets.
Before I build it, what do you still have to calculate or compare manually every time you use points? Which bit takes five tabs, a spreadsheet or some sketchy notes on your phone?
I built Kaviqa, so this is openly product research. There is no link or sign-up pitch here. I genuinely want to know what would save experienced points users the most time.
I’ve been building Kaviqa, a flight search that checks positioning flights and split tickets instead of only searching the obvious route. I’m too close to it now and need people who will actually try to break it.
If you have five minutes, search a real trip you might take and tell me:
Did the search finish and give you results?
Was the cheaper route, extra travel time and self-transfer risk clear?
Did anything feel confusing or broken, especially on mobile?
Would you trust the result enough to investigate the fare yourself? If not, what stopped you?
If you find a bug, please include the route, device and browser so I can reproduce it. No need to be nice. Confusing or broken results are the useful ones right now.
Today is the first day Claude has come to the point for me to change my codex subscription from 5x to 20x. Claude cannot do a single thing. It only makes errors and messes the code base up so much more and it literally admits to doing it aswell. Its not like im giving it complex tasks im spoon feeding it telling it exactly what I want changed and it cant even do that. Anthropic I always backed used to think it was the best by far and Opus 5 seemed alright for the first couple days but it keeps getting progressively worse every single day. It cannot follow a single instruction and starts doing its own thing and gets absolutely everything wrong. On top of that just before I upped my codex subscription I told it to do a simple task it took 15min and then I saw everything was wrong and it even said it messed stuff up but I asked it what did you exactly change and mess up it came back with a report of 8 major things and most didnt even have anything to do with the task.
From a Anthropic die hard fan boy I honestly cannot use it anymore at all.
Not what you’re planning to build or what’s nearly ready. What did you actually get in front of people this week?
Could be a full product, one feature, a landing page or even something that completely failed lol.
Drop what you shipped, what stack you used and the main thing you learnt from it. Links are fine but actually give some context instead of just dumping them.
Ill go through everything and give proper feedback aswell.
Before an agent sends something, edits data, deploys code or performs another expensive action, I make it return a short readback:
- what it thinks the goal is
- what it is about to change
- which assumptions it is making
- what it is deliberately not touching
- what it is uncertain about
This catches a different class of mistake than testing the final output.
The output can be technically correct while the agent has solved the wrong problem.
A 20-second readback is often more useful than adding another 2,000 words to the prompt.
Screenshot only. No pitch or link yet.
Let people guess what the product does and who it is for.
If nobody gets close, your onboarding might have a problem.
The benchmark comparison looks strong, but I care more about how it feels in real use.
If you have tried Grok 4.6, what are you using it for and how does it compare with GPT-5.6, Fable 5 or Grok 4.5?
Anything it is noticeably better or worse at?
I fell into this trap because automating everything felt productive.
Then I realised I was spending hours automating tasks that I did not even do regularly.
Automate the shit you repeatedly waste serious time on.
Everything else might just be productive procrastination.
Google Flights is still showing 5 Oct 2026 at A$1,577 one way in business, while 6 Oct is A$3,121. The nonstop options on 6 Oct start at A$3,274.
This is a date shift, not a separate-ticket trick. Checked for one adult at 01:30 CEST on 12 Aug 2026. Currency is AUD because my Google Flights account is set to Australia. Fares move, so verify the exact date before booking.
I started r/KaviqaTravel for people who genuinely enjoy finding unusually good fares and smarter ways to book. It is global and covers cash fares, points, positioning flights, split tickets, business class and the deals people still cannot believe they found. If you have ever found a ridiculous fare or learned a booking trick the hard way, come share it: r/KaviqaTravel
Some of the hardest parts end up looking completely boring from the outside.
A button works instantly, the agent remembers the right thing, or the output comes back in the correct format. Nobody sees the hours of broken logic behind it.
What part of your product looks simple but was an absolute nightmare to get working?
What kept breaking, and what finally fixed it?
If the story needs more than a comment to explain, genuinely make it a Build Log post. Those behind-the-scenes stories are usually way more interesting than the polished product pitch.
AI demos always look great until a real user does something you never expected.
What's the most embarrassing thing your AI product has done in front of an actual user?
Not a hypothetical edge case. I mean the moment where it hallucinated something insane, broke the workflow, exposed bad logic, forgot obvious context, or confidently did the exact opposite of what it was meant to do.
What happened, how did the user react, and what did you change afterward?
If you want to mention what you built, go for it. Just give us the actual painful story too lol.
Alright, I want to see what everyone here is actually building.
If you have a working AI product, even if it is still rough, make a standalone post in the sub with the Roast My MVP or Build Log flair.
Include what it does, who it is for, the link and the one thing you most want honest feedback on.
I want to test as many as I realistically can and give useful feedback. No need for a polished launch or a perfect landing page. I would rather see something real and unfinished than another vague coming soon page.
What have you got?
People call almost every new AI product a wrapper now, but that feels way too lazy.
If a product takes a model and adds a genuinely useful workflow, better UX, proprietary data, integrations, reliability and solves a specific problem, is it still just a wrapper?
Most software is built on top of somebody else's infrastructure anyway.
So where is the actual line for you? What makes an AI product feel defensible instead of something anyone could recreate in a weekend?
I keep seeing the same loop.
Someone builds a product, barely anyone uses it, and instead of talking to users or figuring out distribution they add another feature.
It makes sense because coding feels productive and marketing feels awkward. AI makes it even easier to stay in that loop because you can build the next feature in a few hours and convince yourself you're making progress.
At what point do you stop building and admit the actual problem is getting people to care?
Have you ever caught yourself coding around a distribution problem?
