A few things I’m noticing while trying to get users for my own app:

The biggest change is not that distribution became harder. It’s that building became so much easier that users now assume your app is probably one of many.

That creates a new kind of friction.

You are not only competing with other products. You are competing with the user’s expectation that trying another app will probably not be worth the effort.

Because of that, I’ve stopped taking most positive reactions seriously.

“Looks cool” is usually politeness.

“I’d use this” is not validation.

A good conversation followed by no signup is still a no.

The only meaningful signal is when someone votes with their time: clicks, signs up, comes back, shares it, or puts effort into using it.

I’ve also found that channel fit matters much more than I expected.

Instagram has been almost useless for my non-entertainment product. TikTok is still unclear to me. YouTube may work, but the competition for attention is brutal.

Reddit has been the biggest surprise.

Not because Reddit is some secret growth channel, but because the audience is already separated by problem and interest. When the product genuinely matches the discussion, people are much more willing to engage and click.

The other thing I’m changing my mind on is audience size.

A large audience outside your niche can be close to worthless.

I worked with 300+ influencers in blockchain, and even there I saw large accounts increasingly struggle to convert attention into action. Attention has become much more fragmented than follower counts suggest.

So the problem I’m trying to solve now is very specific:

Not “how do I improve the product?”

Not even “how do I get feedback?”

It is: what is the hook and distribution path that gets the first relevant group of people to actually enter the product?

I don’t think I have the answer yet.

But I’m spending much less time interpreting compliments and much more time looking at actual behavior.

So far, Reddit is giving me the clearest signal.

Hi!
I Launched a resume optimizer a bit ago resumefit.ca and I'm at the point where I need real users for feedback please.

it tailors your resume to a specific job without lying about your experience — no invented skills, no fake keyword-stuffing, and it'll actually tell you if you're missing a required license/certification instead of hiding it.

I built it myself, never done anything like this before! Now I need to find out where it actually breaks for real people.
If you try it and something feels off — the tone's weird, it missed something obvious, whatever, Please tell me. I need real user feedback

resumefit.ca if you want to poke at it.

a limited number of people who reply to this post I will send a code for 5 free credits so you can poke around and tell me what you think.

The mission I've set for myself is to build a kids learning app for parents that hate kids learning apps (like me). No games, no attempt to increase time on platform (i.e. addict kids to screens), just simple digital or paper worksheets generated by AI grounded in real curriculum and standards they teach in schools with little animal emoji rewards to earn along the way (basic PBIS, also done in schools).

Does the landing page communicate any of that well?

https://waddlelearning.com/

At one in the morning, I was scrolling and thinking about how annoying currency conversion can be at times. Purchased an Apple gift card with Bitcoin and it was successful. No strange boundaries, no waiting.

so it makes me realise that, in the appropriate situation, cryptocurrency can be rather useful. Is anyone else experiencing similar sporadic moments of usefulness?

I have a weird habit. Every couple months I'd open Wikipedia and manually check if certain people were still alive. Old actors, musicians, whoever I grew up watching. It felt stupid to keep doing it manually so I automated it.

mortivox.com. You paste any Wikipedia link, enter your email, done. It watches the page 24/7 and sends you an email the moment someone edits a death date in.

Stack is FastAPI on Render, Postgres on Supabase, GitHub Actions polling the Wikipedia RecentChanges stream. One thing that got me: Wikipedia's API blocks server-side requests from Render's IP range entirely. Spent a few hours confused about that. Fixed it by fetching the person preview photos client-side using the origin=* CORS parameter instead of routing through the backend.

Works with any language Wikipedia, not just English.

Honest question for anyone who'd actually use this: would email work for you or would you want something else, RSS, push notification, etc.?

I work in the media, and this is a solo project I've been building in evenings and weekends. The whole aim of Inkline is to cut through the noise of news and surface what actually matters.

Just pushed what I'm calling the Editorial Update: a hand-verified directory of 800+ publications across 50+ countries, unified search, and an "Also covered by" feature that shows which other outlets picked up a story - handy for spotting how a narrative is spreading. There are daily briefs too if you want the morning-scan version.

Let me know what you think!

For the past 3–4 days, I've been sharing the progress of my biggest project, Flapflop, here on Reddit and collecting feedback. I've received a lot of honest advice, and it has genuinely helped shape the platform.

A little about me: I'm 16 years old. I've always loved building things and creating projects. I also enjoy entrepreneurship, and I've even earned money through e-commerce. But Flapflop is the biggest project I've ever worked on.

Flapflop is a social platform built entirely around video games and indie game developers. You can share clips, gaming news, screenshots, use temporary Instant posts, challenge other players through Duels, and if you're an indie developer, create a community for your game where players can follow updates and discuss it together.

The main reason I'm writing this update is because something really cool happened over the last few days. People actually started joining, giving feedback, and becoming the platform's first members.

One decision I made from the beginning was not to fill the platform with fake users or fake posts just to make it look active. I wanted the community to grow naturally with real people, even if that meant starting small.

It's still early, but little by little, it's growing.

If you'd like to be one of the first members, build one of the first communities, or simply be part of this journey, I'd love to have you with us.

Website: flapflop.com

Hi everyone!
I recently launched my women’s fashion brand, NEE EDIT.
At NEE EDIT, we create pieces made for golden hours, weekend escapes, café dates, and unforgettable summers. Effortless outfits that make you feel confident, feminine, and always camera-ready.
If this sounds like your style, I’d love for you to check out my Instagram: @nee.edit. Thanks for stopping by! 💛

In 11 days (Aug 2) the EU AI Act starts requiring every AI chatbot that EU visitors can use to disclose it's an AI. Turns out almost nobody is ready, most people I talked to assumed the whole law got delayed. Only the high risk parts did, the chatbot part still lands Aug 2.

So I built Disclosed. It's one script tag that renders a small "AI assistant" badge plus a first message notice on top of whatever chat widget you already run (Intercom, Crisp, Chatbase, custom, doesn't matter). It picks the visitor's language, EU statutes care about that, and this is the part I think actually matters: it logs a tamper evident record every time the disclosure is shown, and exports an evidence PDF mapped to the statute. Showing the notice takes 5 minutes, proving you showed it when someone asks in 2027 is the hard part.

It also covers California's bot law (in force since 2019, most US founders have never heard of it) and Utah's AI act, which literally gives you a safe harbor from fines if you disclose upfront.

There's a free check at disclosed.sh/check, six questions, tells you if the law applies to you and what you're missing. No signup.

Would love feedback, especially on the evidence/proof angle. Is "compliance you can prove" something you'd pay for, or does everyone just want the cheapest badge that makes the problem go away?

You paste a Google Maps link, it builds a full website for that business. Real photos from the listing, opening hours, reviews, location, even the brand color detected from the place's own photos. Nothing to write, no forms to fill.

Hardest part was the data: Maps listings are messy. User photos include parking lots and blurry receipts, so we filter for actual food and interior shots. And copy comes from facts like hours and attributes, not rewritten review text, because that always reads fake.

Built it after noticing restaurant owners signing up for our app landing page tool and trying to force their business through it. They just wanted a website without hiring anyone.

Drop questions if you're curious how any of it works.

I wanted a native screenshot and annotation tool that works entirely offline, without accounts, telemetry, subscriptions or cloud uploads.

OpenSnapX is built with Swift and AppKit. It currently supports area, window and display capture, OCR, scrolling capture, editable history, redaction and non-destructive annotations.

It also includes optional support for AI agents through a local MCP connection. When explicitly enabled, an agent can discover capturable windows, capture a window without bringing it into focus, and extract its text using on-device OCR. Communication stays on the Mac through a local Unix socket—there is no Internet server or cloud processing. Agent support is disabled by default.

OpenSnapX is still an early work in progress and currently needs to be built from source, so I’m primarily looking for technical testers and contributors.

Pricing: Completely free and open source under GPL-3.0.

GitHub: https://github.com/alan13367/OpenSnapX

I’d especially like feedback on the local agent integration: what screenshot or OCR workflows would be useful to you?

What would OpenSnapX need before you would consider using it instead of macOS Screenshot, Shottr or CleanShot X?

I originally built Recon as shared memory for teams, but every conversation ended up being about trust.

Where is the data stored? Who can access it? What exactly is Recon collecting?

The questions made sense, but people were being asked to trust a shared system before they had actually used it or seen why it was useful.

So I changed the starting point.

I built a free Mac app that lets one person use Recon locally and see what it is learning from their work.

The part I care about most is not just remembering which file or ticket exists. I want it to remember why something was done, what else was tried, why an option was rejected, and when that decision might need to change.

Then, when I come back to the work later, I can ask Recon what happened instead of piecing it together again from code, tickets, and old conversations.

The Mac app is free and doesn’t require signup. I’m looking for a few people to try it and tell me where it gets confusing or fails to remember something useful.

Try for free -> https://askrecon.com/download/mac

Hey r/SideProject 👋 I'm a solo dev building Whiskium — think "Vivino, but for whisky." Sharing the build honestly and asking for a gut-check on one decision.

The core loop: point your camera at any bottle → it identifies it (barcode + on-device OCR) → an "AI palate" that runs entirely on the phone learns from every review you write and scores how well each whisky matches your taste. No round-trip to a server for the matching — it's all local, works offline.

Stack:

• Flutter (iOS first, Android in Sept)
• Supabase (Postgres + RLS, Edge Functions, storage)
• RevenueCat for monetization (this is a Shipaton entry)
• On-device Google ML Kit for OCR/barcode; a small local model for the palate matching

Some decisions I'm happy with:

• Offline-first: you can review a bottle mid-tasting with no signal; a local write-queue syncs when you're back online. Whisky gets drunk in basements and bars — felt essential.
• Hybrid monetization instead of a hard paywall: free tier gets limited scans; hit the cap and you can watch a rewarded ad for +5 scans (the ad pays ~100x the marginal cost of the AI call it unlocks), or subscribe to remove ads + go unlimited. All tracked in one RevenueCat dashboard.

The decision I keep second-guessing 👇 — this is where I'd love your take:

I'm starting the catalog from ZERO. I could've scraped an existing whisky database to look full on day one, but (a) they're proprietary and copying them is a legal/ethical mess, and (b) they don't reflect what people actually drink and can find locally. So the DB grows only from what real users scan/review/photograph. Classic cold-start UGC problem — the app is genuinely a bit empty at launch, on purpose.

My bet is that a tight community (I'm seeding it with a real whisky club) beats a big scraped-but-generic dataset. But I go back and forth on whether that's principled or just slow suicide for a consumer app.

So, honestly:

1. How would you bootstrap a UGC database from zero without faking it?
2. Rewarded-ad-for-usage vs. straight paywall — which would make you respect the app more (or less)?
3. On-device AI as a feature — genuinely compelling, or a gimmick users won't care about?

Roast the idea, the stack, the monetization — all of it. Building in public and the honest feedback is worth more than downloads right now. 🥃

https://whiskium.vercel.app/

A comfort first weather app with a small bunny named Felt who tells you how the day will actually feel.

New update release: heat advisories from the National Weather Service in plain words (Felt can read them out loud), the Comfort Bar showing your whole day as one curve, one tap to add the best window to your calendar, and Ask Felt, where one confirm books it.

Happy to answer anything about how the comfort score works.

https://reddit.com/link/1v3qwk2/video/4xpy0swr4ueh1/player

I wanted a clean writing editor that handles grammar checking and local AI transformations without sending my text to cloud servers, requiring account logins, or asking for a monthly subscription like Grammarly.

I've had two previous attempts at this project that were pretty lacking, literally just single-file Python scripts running in a terminal.

"Third time's a charm," they say, so I built Lexicon a completely free, open-source desktop app that runs 100% on your PC.

• Github: https://github.com/AashishH15/Lexicon

There's still lots more to add and update, but I'd love to hear your thoughts on the app!

I just wanted to show that there's still place for cool animations and interactive effects in modern web

This one took me 2 days to implement, but I'm happy how it turned.

What do you think?

Not sure if I built it or Claude did, but it's done! I built this because every time I visit whatismyipaddress.com, I get bombarded with ads. You can check it out at ipgiraffe.com. Any feedback or suggestions would be greatly appreciated.

This started as something to work on after my day job.I figured I'd hook up an odds feed, save some events, make a bet slip and call it a day.That part actually wasn't too bad. Then I tested a live match.

Odds changed while the bet slip was open. One selection got suspended. Another one was still available. My backend happily accepted everything because I hadn't even considered that those things could happen between opening the page and clicking the button.I spent more time deleting code this weekend than writing it. After that I ended up reading about how companies handle this stuff. I always assumed the difficult part was getting the data. Doesn't look like it anymore.

Now I'm wondering if every real-time project eventually reaches this point where you stop adding features and start chasing weird edge cases instead.

Anyone else have a side project that completely changed once real data got involved?

So my first waitlist now has 32 sign-ups, which is great, but now I would like to know the average conversion rate for app waitlists. Anyone that has some experience?

Hi everyone! 👋

A while ago, financial stress and the sheer amount of mental clutter (tracking budgets, split-payments, loans) got overwhelming. I wanted a finance app, but every single tool on the market forced me to create an account, connect a bank, or upload sensitive data to cloud servers I don't control.

To make things harder: I don't come from a traditional software engineering background, and I couldn't afford expensive cloud backend infrastructure. So, I turned that limitation into my strongest advantage. Working shifts in a factory by day, and collaborating with AI tools (like GitHub Copilot) by night, I built Nexmy.

Nexmy is a 100% offline-first personal finance app for Android.

What makes it different:

Zero Cloud / Zero Accounts: Your data never leaves your phone. Everything is locally encrypted.

BNPL & Split-Payment Tracking: Perfect for managing installment services like Klarna and PayPal.

Smarty (Beta 1.0): A lightweight, on-device AI assistant that answers your financial questions locally without internet.

Simulators & Tools: Compound interest, mortgage amortization, and clean local PDF reports.

I built this out of pure necessity to regain my own peace of mind, and we just wrapped up our Product Hunt launch. Now, I want to share it with this community.

Nexmy is completely free with no ads. I would be genuinely honored if you tried it out and gave me your raw, honest feedback or feature ideas!

📲 Google Play Store: https://nexmy.app

Thanks for reading, and let's chat in the comments! 🚀

Validated the port against the C++ engine they just open sourced, added a number of modern tweaks like mobile view, text sizing, etc.

Built as TypeScript, with Durable Objects as the network layer in place of IRC handling. So far it runs happily inside of free tier. Has been a fun retro dive.

Live rooms running at comics.remsky.art and surprisingly a handful of folks wandering in lol

I built a tool that reads Reddit threads on a topic and pulls out the structured data (who, what, where, when, how-many) for each thing, organizes them into groups and renders a wiki.

As a test example, I used it to track major natural disasters over the past two years:

https://www.wiki.bot/w/natural-disasters-tracker

Right now it's bot populated but the plan is that people can just post in the discussion section for each "wiki" and the bots read the post and update the page. That interactive layer is next but I wanted some feedback before going there.

Does this format and UX work for you? Is it clear what I'm building or are there some gaps im not seeing...?

I built Routeveil because route transitions in React Router usually end up scattered across page components or tied to one specific animation setup.

Routeveil keeps the animation logic around the router instead. Each navigation can choose its own effect, while persistent UI like headers and footers stays mounted.

rn it includes:

- 8 page transitions

- 12 full-screen overlay transitions

- typed transition-specific options

- declarative and programmatic navigation

- same-page transition previews

- cursor-aware origins

- reduced-motion support

- custom transition definitions

Example:

import { RouteveilLink } from 'routeveil/react-router'

<RouteveilLink

to="/projects"

transition="tunnel"

transitionOptions={{

origin: 'cursor',

color: '#000000',

}}

>

Projects

</RouteveilLink>

Live demo:

https://routeveil.dev

Source:

https://github.com/milkevich/routeveil

I’d especially appreciate feedback on the API and whether the transition options feel intuitive.

I’ve been building Echo, one adaptive model powered entirely by a pool of open-weight models, including GLM-5.2, Kimi K2.7, and many others.

The idea started with an experiment I couldn’t stop thinking about:

A perfect oracle that always knew how to use the models in the pool beat the state of the art on every benchmark we tested.

Echo is my attempt to make that practical.

Rather than simply routing each prompt to one model, Echo dynamically assembles and coordinates the models needed for each request, then exposes everything through one endpoint.

On our first internal task mix, Echo consistently outperformed the best individual model in its own pool and reached Fable-level results at roughly 1/3 of the total inference cost.

It already matches the state of the art on some coding benchmarks. There are still gaps on others, and improving that consistency is one of our main focuses now.

You can use it through the chat or plug its OpenAI-compatible API into OpenCode and other compatible tools.

New accounts include $10 in free inference credits.

I’d love people here to try difficult prompts and tell me where it works surprisingly well or where it still falls short.

Try Echo:
https://echo.tracerml.ai

Evaluation methodology, results, and limitations:
https://echo.tracerml.ai/eval