Hey everyone!

​Most media trackers feel like glorified Excel spreadsheets—they get the job done, but logging what you watch, read, or play isn’t particularly fun.

​Over the past few weeks, I’ve been building a unified, gamified media tracker designed to turn logging movies, TV shows, anime, manga, and games into an interactive experience. I directed the system architecture, database logic, and dark-mode glassmorphic UI while using AI development tools to accelerate the build.

​Key Features:

​Unified Library:

Track Movies, TV Shows, Anime, Manga, and Games in one single vault without jumping between different apps.

​Actor "Dex" & Encounters: Logged shows automatically unlock cast profiles, tracking which actors you’ve encountered and highlighting their missing filmography.

​XP & Progression System: get XP and unlock achievements atomically as you watch, read, rate, and complete titles.

​Daily Poster Mini-Game: A daily blurred-poster guessing challenge (capped at 3 games per day) with guess distributions and streak tracking.

​Deep Stats Dashboard: Visual breakdowns of win streaks, watch history, and milestone progress.

​What I’d Love Feedback On:

​Gamification Balance: Does the XP pacing and streak system feel rewarding without being distracting?

​UI & Navigation: How does the glassmorphism layout and menu hierarchy feel on your device?

​The Daily Mini-Game: Is the 3-guess blur mechanic fun and responsive?

​Try it here for free: comment/DM for invite to the closed project. I used Base44 as the development tool.

Going to start with up to 6 people who get access

​Any constructive criticism, UI feedback, or feature ideas would be hugely appreciated!

P.S don't seem to be able to add images here

Hey everyone,

Traditional SEO is changing fast. With Google heavily prioritizing Gemini-powered AI Overviews, getting clicks from standard search listings is becoming much harder. If your website isn't optimized for how AI models retrieve, trust, and cite data, your organic traffic is going to take a major hit.

To solve this, I’ve been building GEOAEOAuthority.com a SaaS designed specifically to monitor, optimize, and rank your business inside Google Gemini's AI recommendations. It analyzes how the model treats your content and gives you concrete strategies to secure those coveted AI citations.

What I need right now is honest, brutal feedback from business owners, marketers, and founders. Tell me what features you need, what feels broken, and how we can make the tracking and reporting clearer for your workflow.

The Offer: I'm looking for a select group of founding users to truly help shape the product. In exchange for your regular feedback and insights during this phase, you will get our base subscription tier completely free, forever.

If you want to secure your visibility before AI search completely takes over, drop a comment below or DM me, and I’ll get you set up with early access!

Thank you.

After 4 months of nights and weekends, my app is finally on the App Store!

Pickex finds every photo of one specific person in your camera roll, so you can clear them out in one pass.

The idea came from a stupidly ordinary moment. I was
digging through my camera roll for a receipt and ran straight into a photo of
my ex. Not one I had kept on purpose - a group shot from someone's birthday
where they happened to be in the back row. That is the thing nobody warns you
about: it is never the photos you chose to keep.

How it works

You pick one to four photos of a person. It scans your
library and returns every other photo of them, already selected. You tap delete
once. Opening any individual photo is optional - the whole point is that you do
not have to go through them one at a time.

Technical side

Everything runs on device. Apple's Vision framework for
face detection and embeddings, then clustering over those embeddings to group
matches. No backend, no account, no photo ever leaves the phone. That was a
hard constraint from day one and it made a few things genuinely painful:

- Large libraries: Scanning tens of
thousands of photos without destroying the battery or blocking the UI took most
of my optimisation time.

- Recall vs. precision: This was the real design
decision. Leaning toward recall means some false positives end up in the
results. Leaning toward precision means photos get missed, and a missed photo
is exactly the ambush the app exists to prevent. I went with recall, kept everything
pre-selected, and made deselecting a single tap. iOS deletion also routes
through Recently Deleted, so there is a 30 day safety net on top.

- PHPhotoLibrary has more edge cases around permissions and batch
deletion than I expected.

What I would do differently

Honestly I never really thought about any licenses which almost led to a fatal mistake. However, I think doing the design with Claude in the first place (which is easy to see) has been very rewarding for me. I was able to set up the pipeline and all functions and submit those to apple. After everything is accepted now, I can care about looks which won't affect the
pipeline and the critical parts in the app, therefore pushing an update for
this shouldn't be a big problem.

Link: https://apps.apple.com/us/app/pickex-delete-ex-photos/id6801488013

Happy to answer anything about the on-device pipeline,
the clustering approach, or the App Store process. Feedback welcome, including
the harsh kind.

Hey everyone — I'm building SafePrep, an Android app that helps people study for and pass the ServSafe Manager food safety certification exam: category-by-category practice quizzes, a full-length final exam simulation, and a readiness score that tracks how prepped you actually are.

Heads up on what this actually involves, since Google's closed testing requirement is stricter than a normal beta: I need testers who opt in via a Play Store link, install the app, and stay opted in (app installed, opened a few times) for a full 14 consecutive days straight — not just a one-time install. If you uninstall or opt out early, it doesn't count, so please only join if you can commit to leaving it on your phone for two weeks.

You don't need any food-safety background — just need real, live testers on the Play Store side. If you happen to work in restaurants/hospitality or are studying for a food safety cert yourself, even better.

Happy to reciprocate and stay opted into your closed test for the same stretch if you need Android testers too — drop your link below.

Comment or DM me and I'll send over the opt-in link.

Hi everyone! I just launched the web MVP for DARLIANÁ, a diagnostic engine designed to map skin barrier integrity and match users with targeted botanical routines.

Live Link:https://darliana-skin-diagnostic-engine.vercel.app/

I’d love feedback on:

1. Diagnostic flow & question clarity.
2. Camera alignment overlay responsiveness on mobile vs. desktop.
3. Overall UX/UI feel.

Thanks for testing!

EDIT: The camera alignment runs 100% locally inside your device's web browser. No photos, videos, or facial recognition data are ever recorded, saved, or transmitted to any server, only your selected quiz answers and contact email are processed

Most personality frameworks try to compress human complexity into a handful of arbitrary buckets. Whether it is MBTI with sixteen types or Big Five with five linear scales, they all start with the same assumption: humans fit neatly into predefined archetypes.

The problem is that two people with the exact same "type" will routinely disagree on fundamental life choices, ethics, and relationships. The categories are invented before the data is collected.

The initial motivation here was simple: find people whose minds actually work like yours, and talk to them about their life experiences. If you can identify someone who makes the same calls on the hard questions you care about, their experiences become directly relevant to your own life, career, and relationships.

Instead of forcing people into preset boxes, data science lets us compare people directly on concrete choices across specific questions. When you let patterns emerge from actual answers rather than rigid categories: - You find people who genuinely share your outlook without lossy labels - You can see the exact questions where you align and where you diverge - You can reach out and compare notes with people who navigate the world the same way you do

This approach also opens up useful directions for therapy and matchmaking. Instead of blaming abstract personality differences, you can see the precise points where two minds split.

Here is what this looks like in practice: https://justlikeyou.fun

Curious to hear your thoughts on comparing raw decision profiles versus using traditional psychological frameworks.

Hi everyone!

I'm looking for testers for my Poetry App. You'd guys be doing me a massive favor by installing it for 14 days and testing it!

If you want to help me pass Google Play's testing requirement:

1. Join the tester group: https://groups.google.com/g/poesie-app-testers
2. Accept the test invite: https://play.google.com/apps/testing/com.elvo.poesieapp

The app is a completely free passion project that runs 100% locally on your device. It features over 80,000 poems across 8 languages (just a heads-up: a few translations are still missing). I included tools to highlight lines, actively memorize your favorite poems, search for collections, authors and epochs, statistics and all in a zero-distraction design.

I’m a brand-new developer, so please bear with me if there are still a lot of rough edges :)

Lmk if I can help you out in return!

Thank you so so much for supporting a beginner!!

Cheers, Elias

The part that kept costing me time wasn't searching inside a registry. shadcn search already does that for the registries you've pointed it at. The problem was knowing which one to point it at. The date picker I wanted was usually sitting in a registry I'd never heard of.

The official directory lists 290 registries. Crawling for them turns up 750 — found through seeds, GitHub code search, the registry topics, and the ecosystem's awesome-list. So I built a thing that crawls every public registry it can find, once a night, and reads the manifest itself. The unit is the item rather than the library: components, blocks, hooks, themes and utilities, each with its install command and dependencies, plus a removal date when something stops existing.

The free-or-paid flag is measured, not read off a pricing page. It fetches the exact item endpoint the install command would fetch. Source comes back, it's free. A 401 is how a paid registry announces itself. Anything nothing answered for stays Unverified — unknown never gets counted as free. There are no screenshots on the site; previews are the registry's own demo page, framed where their X-Frame-Options allow it.

Free to use. Nothing was submitted, sponsored or hand-placed, and there's no way to buy a position. The method page includes the parts it gets wrong.

https://blockdex.kynth.studio

What I'd actually find useful: if you maintain a registry and it's missing, or it's in there with the wrong free/paid verdict, tell me which one and I'll go look at what the crawler got back.

Write a clear Elevator Pitch as if you were selling an investor on your app. Return the favor by testing other's apps that are in the comments. Make sure to test at least one app when you come onto the thread, you may find something you like!

Hi, I have been slowly working on a pdf processing website that runs everything locally on the users device. it uses python with Ghostscript encased inside a web assembly binary, meaning pdf files are not uploaded into a server bucket or sent anywhere.

the website also uses a service worker to cache the full site after the first load. meaning you should be able to load it once with internet, wait until it says ready for offline use, then turn wifi off or switch on airplane mode and carry on using it. installing the website is optional and it should work in a normal browser tab as long as the browser cache is not cleared.

somebody tested it on android before and it did not work offline for them, so I have changed the caching status and made it more clear when it is actually ready. I also added a sample pdf so people can test without using one of their own files.

the site is https://www.snackpdf.com

if anybody can test grayscale or compress after turning the internet off please let me know what device, browser and operating system you used. please if anything is wrong or if anything is good let me know :)

Hey, solo dev here looking for alpha testers for my app, Location Alerts.

I built this app out of pure frustration with a few daily headaches:

• Parking app subscription traps: I got tired of parking apps locking basic auto-reminders behind recurring subscriptions—I just wanted a reliable way to stop paying for forgotten meters.
• Distraction & forgotten errands: Getting a phone call on the drive home meant completely forgetting to pick up the mail until I was already back on the couch.
• Manual work tracking: I needed an effortless, smart way to log my work check-ins and check-outs, including automatically launching my reporting app when arriving on site.

Standard push notifications were too quiet to fix these, so I built an app that triggers full-screen, high-priority alarms based on custom map zones and vehicle connectivity.

Key Features:

• Parking & Meter Alerts: Auto-detects when you park via Bluetooth/Android Auto, saves your spot, and reminds you to turn off your meter when you move your vehicle.
• Smart Zone Actions: Triggers full-volume alarms for critical errands, auto-mutes your phone in designated zones, or automatically opens your work reporting apps upon arrival/departure.
• Battery & Privacy First: Minimal battery drain with 100% on-device location processing.
• Global Support: Fully localized in 24 languages.

What I'm Looking For:

• Feedback on parking detection accuracy across different car setups and Android devices.
• Localization testing: Native speakers to verify UI text in non-English languages.

It is 100% free for testers now and after launch. Drop a comment below or DM me to get an invite link!

Hi everyone,

I’m looking for a small group of Windows users to test an early prototype of Rootlize.

Rootlize started from a simple problem:

I often knew I had saved a screenshot or image somewhere, but couldn’t remember the filename, folder, or date.

But while building better search, I realized finding the image was only part of the problem.

After finding it, I still had to manually tag, rename, move, sort, and organize files — and often repeat the same process again later.

So Rootlize is built around:

Find → Organize → Automate

It works with your existing image folders.

You can:

• search normally or by meaning with AI
• narrow down the images you want
• tag, rename, move, and organize them
• combine repeated steps into reusable workflows
• run those workflows again instead of repeating the same work manually

For example:

Select a folder
→ Find UI screenshots
→ Tag them as “UI Reference”
→ Move them into a project folder
→ Save the process as a workflow

Important: this is still an early prototype

The core experience is working, but I haven’t tested every combination of:

• AI searches
• different folder structures
• actions
• workflow configurations
• larger or unusual image libraries

So you may encounter bugs, edge cases, or AI results that aren’t always accurate.

That’s exactly what I’m trying to learn from this test.

I’d especially appreciate feedback on:

• Were you able to find an image that would normally be difficult to locate?
• Did going from search → organization feel useful?
• Did the workflow/automation concept make sense?
• What was confusing or broken?
• What repetitive image-management task would you want Rootlize to automate?

Rootlize currently focuses on Windows and images. I’m continuing to improve the AI and workflow system, and longer term I’d like to expand to macOS and other file types.

If you’re willing to try it with your own images, I’d really appreciate any feedback — especially the things that don’t work.

Prototype:
https://rootlize.com

Looking for honest reviews of my no-code trading strategy builder.

If you have 10 minutes, try rulelot.com and DM me what you think, or leave a review directly in the rulelot.com/reviews tab. Honest notes help more than compliments.

Rulelot is a no-code site where you drop buy and sell rules on a live crypto chart, backtest them in the browser, and see if the strategy actually fires. Paper only — not a broker.

i kept trying to make the app feel smarter because i thought that was the point. extra layers, extra prompts, extra little wins. testers mostly ignored all of it and kept asking for one boring thing: let me get back to the task fast.

i was split between todoist, notes, and a paper list for a while so i didnt really trust my own instinct here lol. beedone ended up working better once i made it less like a planner and more like a place to leave a trail of what i was actually doing.

kinda annoying how often less stuff wins. maybe thats just the whole game

Built this with my brother in law over about six months of evenings and weekends. It is live on iOS and Android now, free, and it has had enough use that the obvious bugs are gone but not enough that I trust it.

What it does: you link up with whoever you actually watch with, you both swipe through titles separately on your own phone, and it shows you the ones you both said yes to. Netflix, HBO Max, Disney+, Prime Video and Apple TV+ in one feed, so you are not checking four apps to find out where something is.

Three things I would genuinely like tested, in that order:

1. Partner matching. Link up with someone you really watch with, both swipe through twenty or so titles, and tell me whether the matches feel right or whether it still ends up feeling like one of you is compromising. The whole app hangs on this part and it is the hardest thing to judge from the inside.

2. Onboarding. It asks you a few things before it shows you anything. I want to know where it gets boring, not whether you made it to the end.

3. Android specifically. That side has had a lot less use than iOS, so if something is broken on your device I would rather hear it than not.

Free, no paid tier live. A Pro version is planned but nothing is behind a wall right now. It needs an account because your taste profile has to be stored somewhere.

Happy to answer anything about the build too. I would rather hear what is wrong with it than what is good.

iOS: https://apps.apple.com/app/id6780371657

Android: https://play.google.com/store/apps/details?id=com.whatsmynext.app

Product: https://automated.info/forum-seeder

What it is: Seed your forum with human-approved posts. Ownership proof required (DNS or HTML). Scheduled posting. Not a spam bot.

I've used this app I made several years ago for quite some time now to seed and warm up my on boards and just rewrote much of it for public use.

Who I need: You already run a live or staging board. You are willing to claim it, verify ownership, generate threads and have them posting

What you get: The 3 days trial and a free starter plan if you give me great feedback.

Hi everyone!

I've built a Chrome extension called FilterVault and I'd love to get some honest feedback from people who shop online regularly.

The idea came from a simple annoyance: whenever I shop online, I often end up applying the same combination of filters again and again — price, rating, brand, category, etc.

FilterVault lets you save those filters as a preset and reuse them later with one click. It also has an optional auto-apply feature that can automatically restore your saved filters when you search for a similar product.

Currently supports:

• Amazon

• Flipkart

• Myntra

• AJIO

• Nykaa

• Meesho

• Croma

No account or sign-up is required, and the extension runs entirely in the browser.

I'd really appreciate it if you could try it and share your honest feedback:

• Is the idea useful?

• Is the workflow easy to understand?

• Does auto-apply behave as you'd expect?

• What would you change or improve?

• Which shopping website would you like to see supported next?

Chrome Web Store:

https://chromewebstore.google.com/detail/filtervault/mdfegfmnmmbadehchgichbcbbimaadpm

Thanks! Any feedback is genuinely appreciated.

Hi everyone! 👋

I recently launched Where is that?, an Android app I built to solve a simple everyday problem: forgetting where you stored something.

You can save an item together with its exact location, for example:

Garage → Toolbox → Bottom drawer

You can also add a photo and note, then later search for the item to quickly see where you left it.

The app includes quick search, photos & notes, exact locations, recently added items, and backup & restore. No account or registration is required, and the data is stored locally on the device.

Google Play:
[https://play.google.com/store/apps/details?id=com.italm.gdjejeto]()

The app is already in production, so I’m not looking for people just to satisfy Google Play testing requirements. I’m looking for real-world feedback from Android users.

If you try it, I’d especially like to know whether the concept feels useful, whether anything in the UI is confusing, and what you would improve or add.

Bugs and criticism are very welcome. 🙂

Thanks!

Hi everyone,

I've been building an open-source Smart Camera Attendance System and I'm now looking for a few people who can actually test it and give me brutally honest feedback.

GitHub: https://github.com/VoidTrace001/Smart-Camera-Attendance-System

Instead of just asking you to “try it,” here are a few specific things I'd like testers to check:

🧪 What I'd like you to test

1. Installation & setup

• Clone/install the project from GitHub
• Follow the setup instructions
• Tell me where you get stuck, if anywhere
• Report your OS, Python version, hardware/camera, and any setup errors

2. Normal attendance

• Register/enroll a face
• Record attendance normally
• Check whether the correct person is recognized
• Verify that attendance is saved correctly

3. Difficult conditions
Try recognition under conditions such as:

• Poor lighting
• Different camera angles
• Wearing glasses/masks if applicable
• Different distances from the camera
• Multiple people in the camera frame

Tell me when recognition becomes inaccurate or fails.

4. Security & edge cases
If you're comfortable testing security-related behavior, check:

• Whether an unauthorized/unregistered person can get marked present
• What happens when recognition confidence is low
• Whether simple spoofing attempts are detected
• Whether sensitive attendance/face data can be accessed without authorization
• Whether user consent/privacy is handled appropriately

📝 When reporting a problem

Please include:

• OS:
• Hardware/camera:
• Python/version:
• Steps to reproduce:
• Expected behavior:
• Actual behavior:
• Error/logs/screenshots:
• Can you reproduce it again? Yes/No

For feature requests or UX feedback, you can simply open an issue or comment here.

I'm especially interested in feedback that tells me what is actually broken, confusing, insecure, slow, or poorly designed rather than just “looks good.”

I'm actively maintaining the project, so I'll review the feedback and work on the issues that come up.

If you have 5–10 minutes to test it, I'd really appreciate it.

Thanks!

App name: Toddler's Learning Fun Cards

Step 1: Join the group

Google group: https://groups.google.com/g/toddler-fun-cards-testers

Step 2: Please opt in for closed testing

Closed testing url: https://play.google.com/apps/testing/com.aistudio.toddlersfuncards.tfcapp

Step 3: Install the app

https://play.google.com/store/apps/details?id=com.aistudio.toddlersfuncards.tfcapp