r/alphaandbetausers

I built an app for people to swipe on topics, match with someone who disagrees, and get scored on civility.

The idea was that if you were constantly an asshole, your civility score would follow you.

But I added a side feature called toxic mode where there was no civility score and people could just argue.

Every user went straight to toxic mode.

That taught me two things.

First, users do not always care about the product you think you built. They care about the part that creates the strongest reaction.

Second, my funnel was way too long for something that needed two people online at the same time. Ad to site to app store to install to onboarding to swiping to matching to finally chatting was just way too much friction.

So I was like ok lets just see if the friction is the issue here

 I made a lightweight browser version focused on the behavior people were already choosing.

No install. No app store. Just pick a topic and jump in.

https://thinklavender.com/ragebait

The bigger lesson for me was to watch what users actually do, especially when it is not what you wanted them to do.

Curious if other founders here have had users prefer the “wrong” feature and whether you followed it or kept pushing your original vision.

Hey guys!

​I’ve just released MetriBody, an app dedicated to tracking physical evolution through body measurements and charts rather than just scale weight. It's built with Flutter and supports EN, ES, FR, and PT.

​It's in its early stages and I'm looking for honest feedback on the UI, usability, or features you'd like to see next.

​If you have a few minutes to check it out, I’d deeply appreciate your support:

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

​Drop your links below too, happy to support back! Thanks a lot.

Today's the day — Better is officially on Google Play.

For those who haven't seen my previous posts: Better is a fitness app that combines workout tracking and nutrition logging into one app. The gap I noticed was that apps like Hevy and Strong are great for workouts but don't touch nutrition, and MyFitnessPal is great for food but doesn't do workout tracking properly.

The build journey:

• Built with Kotlin Multiplatform (85% shared code for Android + iOS)
• 19 features shipped for the MVP
• Full offline support with sync
• 300+ exercises in the library
• Integrated Open Food Facts for 2.4M+ searchable foods
• PR detection system that celebrates your personal records

What I learned building this:

1. Feature creep is real. I had to cut my initial feature list in half to actually ship.
2. Offline-first is hard but worth it. Nobody wants a gym app that needs WiFi.
3. Food databases are messy. Deduplication, bad data, missing macros — spent more time on data quality than I expected.
4. The "two-app problem" is real. Every lifter I talked to confirmed they use at least two apps.

https://play.google.com/store/apps/details?id=io.behzodhalil.better

iOS is coming this week. Would love any feedback from people who currently use multiple apps for fitness tracking.

I see the same story in this sub every week. Someone spends six months building, ships, posts a launch thread, gets forty visitors, and concludes the product is bad. The product is usually fine. What is missing is distribution, and most of us treat it as an afterthought because building feels productive and marketing feels like shouting into the void.

Here is the mental shift that changed things for me: marketing is not a launch event, it is a daily habit that starts before the product is done. If you only remember one thing from this post, make it this. One piece of content per day, on one channel, for ninety days, before you judge anything. Not five channels. One. The founders you see everywhere are not everywhere, they are consistent in one place and it creates the illusion of omnipresence.

Choosing the channel matters less than people think, but the logic is simple. If your users are developers, write where developers read, meaning here or on Hacker News or in dev newsletters. If your users are normal humans, businesses, creators or shoppers, short form platforms are the cheapest attention available right now, and you do not need to show your face. Slideshows, screenshots with text on them and screen recordings all work fine faceless.

On the content itself, the mistake I made for months was talking about my product. Nobody cares about your product, people care about their problem. Every piece of content should name a specific pain your user recognizes, in their words, before your product is ever mentioned. "Your churn emails are ignored because they all say the same thing" will always outperform "check out my retention tool". A useful exercise is to write down twenty complaints your target user would say out loud, then turn each one into a piece of content. That is your first month done.

The last piece is expectation management. Weeks one to three will feel like posting into a void, and this is where almost everyone quits. Algorithms need time to figure out who your content is for. My own curve looked exactly like that: a first month where total views barely reached triple digits, a second month of slow movement, and then a sudden spike that came out of nowhere. The curve is not linear, it is flat and then it jumps, and you have to survive the flat part.

For transparency, I am the founder of Cinerads, which automates the short form part of this system by turning a product URL into daily TikTok slideshows. I built it because I could not sustain the daily habit manually. If you want to try it, comment and I will DM you a discount code, but honestly the system above works whether you automate it or grind it out by hand. What I would love from this thread is to hear which channel finally worked for you, because the ninety day rule applies everywhere but the right channel differs by product.

Comparing https://resume.zoevera.com against https://chatgpt.com

And what a purpose-built ATS checker caught that GPT-4 didn’t.

Let me be upfront: I use ChatGPT for everything. Code reviews, draft emails, explaining stack traces at 2am. It’s genuinely useful. So when I needed to tailor my resume for a senior backend role, my first instinct was to open a chat window.

That was three weeks ago. Here’s what I learned.

What ChatGPT actually does well

Ask ChatGPT to “improve my resume” and it will:

• Clean up passive voice (“responsible for” → “led”)
• Suggest stronger action verbs
• Add structure and formatting consistency
• Rewrite vague bullets into something that sounds more impressive

For general writing quality, it’s genuinely good. If your resume reads like it was written by someone who hasn’t slept in 48 hours, ChatGPT will fix that.

What ChatGPT fundamentally cannot do

Here’s the problem: ChatGPT doesn’t know what job you’re applying for.

You can paste the job description into the prompt, sure. But there’s no mechanism for it to:

1. Score your resume against that specific JD — it has no concept of a match percentage
2. Identify which keywords are present vs. missing — it will suggest improvements but won’t systematically audit keyword coverage
3. Know how Applicant Tracking Systems parse text — it will rewrite content without knowing whether an ATS will ever see it

ATS filters work on keyword frequency and placement. A resume that reads beautifully to a human can score 40% on an ATS if the right terms aren’t in the right sections. ChatGPT optimizes for human readers. ATS systems are not human readers.

I ran a test. Same resume, same job description (Backend Engineer, Node.js/AWS stack). I gave ChatGPT the full JD and asked it to optimize my resume for ATS.

The output was well-written. It added “microservices” and “REST APIs” in a few places. But it missed:

• “AWS Lambda” — mentioned 4 times in the JD, absent from my resume after the rewrite
• “CI/CD pipeline” — appeared in the required skills section, never added
• The Projects section — ChatGPT rewrote my experience bullets but left the Projects section untouched, which is where most of my relevant backend work lived

When I ran the same resume through resume.zoevera.com, it flagged all three gaps explicitly, with section-level attribution. The ATS match score went from 54% to 81% after applying the suggested changes.

The core difference: diagnostic vs. generative

ChatGPT is a generative tool. It produces new text. It’s very good at that.

An ATS checker is a diagnostic tool first. It measures the gap between your resume and a specific job description, then tells you exactly what’s missing. The rewrite comes second — and it’s grounded in what was actually identified as absent, not what the model thinks sounds better.

This distinction matters because:

ChatGPT hallucinates improvements. It will add metrics you never achieved (“improved system performance by 35%”), use terminology that
sounds right but wasn’t in the JD, and rewrite bullets that didn’t need rewriting while leaving critical gaps untouched. Every line needsfact-checking.

A purpose-built tool works from the actual gap. The keywords it adds are the ones the JD asked for. The sections it flags are the ones the ATS will score. The output is closer to submission-ready.

A practical workflow

These tools aren’t mutually exclusive. The best result I got came from using both in sequence:

1. ATS checker first: identify the keyword gaps and get a scored rewrite that closes them
2. ChatGPT second: use it to polish tone, tighten sentences, and clean up anything that sounds mechanical

The ATS checker handles precision. ChatGPT handles prose quality. Neither does both well alone.

The cost argument

ChatGPT Plus is $20/month. If you’re actively job searching, that’s a fixed overhead whether you use it or not.

Most people search for jobs in windows — a few weeks of active applications, then nothing for months. A per-session model makes more
sense: pay when you need it, nothing when you don’t. ZoeVera’s pricing works that way — free analysis, one-time payment for the full
rewrite, no subscription.

For a developer audience specifically: if you’re applying to 10–15 roles over two weeks, you’re not optimizing resumes 365 days a year. The math on a monthly subscription doesn’t work.

What I’d actually recommend

• If you just need better writing: ChatGPT is fine and you already have it
• If you’re applying to roles where ATS filtering is real (any company using Workday, Greenhouse, Lever, iCIMS): use a dedicated ATS checker first, then polish with ChatGPT
• If you’re a developer and haven’t thought about this: your resume probably uses technical jargon that means something to you and nothing to an ATS keyword parser. “Built scalable backend” is not the same as “developed microservices architecture using Node.js and AWS ambda” — even if the underlying work is identical

The ATS doesn’t know what you meant. It only knows what you wrote.

Tested against a real Backend Engineer job description. Tools used: ChatGPT GPT-4o, https://resume.zoevera.com. June 2026.

I’ve been working on LoopTroop, an open-source local GUI for running larger AI coding tickets without treating the whole thing as one giant chat.

The thing I kept running into was context rot. A coding agent can look fine for the first few steps, then the session fills up with logs, failed edits, half-reasoning, repeated files, and suddenly it starts forgetting constraints or “fixing” the wrong thing. For small edits that’s tolerable. For multi-file tickets it gets ugly.

The approach I ended up building is closer to an MLOps-style workflow than a chat tool:

- the ticket moves through explicit states instead of one open-ended conversation

- an LLM Council does the heavy planning: interview questions, PRD, and bead plan

- each model drafts independently, then drafts are voted/scored anonymously, refined, and checked for coverage

- work is split into small “beads” with target files, acceptance criteria, validation steps, and test commands

- execution happens one bead at a time through OpenCode

- when a bead fails or times out, a Ralph-style retry keeps the failure note but throws away the polluted session

- the GUI keeps the Kanban state, artifacts, logs, bead status, diffs, and final review in one place

The main idea is: preserve durable artifacts, not chat history. The PRD, bead specs, logs, failure notes, test commands, and diffs live outside the model. Each phase gets the minimum context it needs. If something fails, the next attempt starts fresh with a compact note, instead of dragging the whole broken transcript forward.

This is intentionally slow. It’s not trying to beat Cursor/Claude Code/OpenCode for a 2-line change. It’s for the annoying tickets where you want the agent to scan, ask questions, plan, decompose, execute, retry, and hand you something reviewable instead of a mystery pile of edits.

The app is local and open-source. It attaches to your local repos and uses your configured OpenCode models/providers. The result is still human-in-the-loop: you approve planning artifacts and review the final PR/diff. It does not silently merge code.

Repo:

https://github.com/looptroop-ai/LoopTroop

Full 16-minute walkthrough/demo:

https://www.youtube.com/watch?v=LYiYkooc_iY

Still early alpha, but the full ticket lifecycle is working. Any feedback is more than welcome. If you try it and it works or breaks, give me a sign; happy to talk through it.

built this because I kept seeing freelancers and solo founders sign contracts they didn't fully understand and only find out what was in them when something went wrong.

it's called 4eyes.ai. paste or upload any contract and you get risk flags with exact clause quotes, compound trap detection, missing protections, and either negotiation suggestions or operational workarounds depending on whether you can actually negotiate the contract or just need to work around it.

example from Upwork's own ToS: Section 7 routes all disputes through their arbitration program. Appendix A charges $337.50 per party to actually arbitrate. individually both look fine. together it means any milestone under $337.50 can be held hostage because fighting it costs more than just surrendering.

free, no login, under two minutes.

https://4eyes-ai-pqjbj7zjjwhwhh2rii7tap.streamlit.app

Logseq stores everything as plain .md files, but if you ever open one of those files directly on iOS (from Files, iCloud, Dropbox, a backup, etc.) you just get raw text. I built a small viewer to read them rendered on a phone.

Md Preview:

• Renders GitHub-Flavored Markdown — headings, tables, task lists, footnotes

• Code blocks with syntax highlighting, plus LaTeX math and Mermaid diagrams

• Opens .md / .markdown / .mdx / .rmd / .qmd from Files or the Share Sheet

• 100% on-device — no account, no uploads, no ads, no subscriptions

Free on the App Store: https://apps.apple.com/app/id6760341080

Details: https://markdown.cybergame.ai/

Not a Logseq replacement at all — just a quick way to read loose .md files when you're away from the desktop app. Curious how you all read your graph on the go.

Hi everyone!

I'm a solo developer and after a year of hard work I finally launched my own messaging app. The entire project—from the Android app and backend to the web version , was built by one person, with AI helping speed up development.

I'd love to hear what Android users think about the concept, UI, and overall experience. I'm especially interested in honest criticism and ideas for improvement.

Below functionality.

1. No phone number or email required
2. Strong focus on privacy and security - functions like:
   1. PANIC PIC - special PIN to erase everything from phone
   2. No Server storage - every message encrypted before sending to peer, after delivery purged from server
   3. You can use different names in different groups
   4. Screen prevention - based on settings prevention of screenshot taking by peer (video call included)
   5. Voice changer during call (in real-time) and voice messages
3. Voice & video calls
4. Meeting links (similar to Google Meet or Zoom
5. Voice rooms
6. Built-in mini apps
7. Fully functional Web PC Version

App is free , only point 2 (security features) above is paid $19.99 for year , but 15 days of trial of secuity functionns provided

Google Play Link: https://play.google.com/store/apps/details?id=com.meetvap.messenger
Apple app store Link: https://apps.apple.com/tr/app/meetvap/id6767963508

Website: https://meetvap.com

MeetVap

Thanks for your time!

Hey, looking for beta testers for FocusCall: an iOS reminder app I built for people with ADHD (or anyone else who's gotten really good at swiping away notifications without reading them).

The idea came from a r/ADHD post with 2.4k upvotes: "If only there was an app that would call me like a personal assistant would." That line stuck with me. A push notification gets dismissed in half a second. A ringing phone doesn't.

So FocusCall calls your own phone at whatever time you set. Press 1 if you're on it, 2 to snooze an hour, 3 for tomorrow. Don't pick up, and it calls back (Nag Mode). There's also a location version, call me when I get to the store, that kind of thing.

What I actually need: people willing to use it for real reminders over a couple weeks and tell me what's annoying, confusing, or just missing. I especially want to hear about the calling part itself, since that's the entire point of the app.

One heads-up: US numbers only for now (the Twilio number I'm using is US-based), and this is still TestFlight, not a real App Store release yet.

If you're outside the US but would actually use this if it worked there, drop a comment with your country. That's genuinely how I'll decide where to add support next.

Link: https://testflight.apple.com/join/P2wZbTbT

Long-time lurker, first real post. Hoping the mods give me a pass for self-promo because I think this community is exactly who I need to hear from.

After years of doing astro the conventional way — Windows laptops on the balcony that kept crashing, dedicated cameras with driver problems, endless cable management — I decided to find out whether modern iPhones could actually do live-stacking natively. Turns out they can, more or less. So I spent the past year building it.

AstroStackerPro: real-time live stacking entirely on-device, up to ~600 frames per session, IMU-based derotation using the gyroscope for untracked long integrations, on-board editor with denoise (AI, on-device), sharpen, light-pollution removal, and exports to FITS for those of you who want to take the stack into PixInsight or Siril.

Privacy: 100% on-device, no cloud, no analytics, no account.

Requirements: iPhone 11 or later, recent iOS.

Public TestFlight beta is open. One-person project, v1.0.1, definitely rough in places, and I am genuinely at the stage where real users finding the things I missed is more valuable than gold to me. So please be hard on it.

Honest caveat: for certain targets (deep sky, high magnification) you'll still want a tripod, a star tracker, or a dedicated iPhone telephoto — but I'm actively pushing to minimize the extra gear needed.

Best channel for detailed feedback is email at astrostackerpro@icloud.com, I reply individually.

🛰 https://testflight.apple.com/join/aYaV63UV

🌌 https://astrostackerpro.com

Clear skies.

i'm 16 and i just shipped my first real app. it's called War Table. you give it one hard decision (a job, a move, whether to quit something, an idea you're unsure about) and five different AI models, chatgpt, claude, gemini, grok, and qwen, each argue it from a different role across three rounds, then you get one clear verdict with the disagreements kept visible. i built it because asking one AI a real decision just hands you one confident answer while hiding the takes it skipped. it went live this morning and honestly it feels surreal. free to start, no card. https://apps.apple.com/us/app/war-table-ai-council/id6780293764 genuinely want people to throw a real decision at it and tell me where the verdict falls flat. cant believe i made it even this far!

A while back my wife told me she wasn't exhausted from doing chores, she was exhausted from being the only one who remembers them. Appointments, birthday gifts, when the kids need new shoes, all of it lived in her head and I honestly didn't see it.

I looked for an app where a couple could actually map this stuff out and compare. Couldn't find one very good. So I decided to make it myself, which was a questionable decision since I can't code at all.

It took forever. Way more evenings than I'll admit to her. Got rejected by App Review over my paywall wording, twice, and the second rejection took me a full weekend to figure out.

Anyway it's live now. It's called Balance. You and your partner each swipe through the same deck of everyday tasks and it shows you where your answers don't match on who actually handles what. When we tested it on ourselves the mismatches were... humbling.

Free to try, iOS only for now (Android is in progress). If anyone here tries it I'd love feedback on the onboarding, that's where my drop-off is ugly. Also happy to answer questions about shipping an app when you're not a developer, it's doable but painful.

https://apps.apple.com/us/app/balance-your-mental-load/id6766533067

For most of my life I tried to be someone else. I'd find someone I admired, decide they were better than me, and copy them. That mindset pushed me into a business I never enjoyed and only started because I looked up to one specific guy. It failed. I felt completely lost.

Around that time I was obsessively tracking my sleep with a Whoop, trying to optimize it. I kept getting good recovery scores. And I was still exhausted, yawning through entire afternoons, dead by 2pm. That's when it clicked: the score doesn't do anything. It just confirms you slept well or badly. Cool. Now what? Knowing isn't fixing.

So I built the thing I actually wanted. It takes the data your wearable already collects sleep, recovery, heart rate, and turns it into a daily protocol instead of another number. It tells you what supplements to take based on your metrics, predicts your most productive hours and gives you the exact time window when you should do deep focus tasks and light focus tasks, it tells you how much caffeine you have in your system left based on your first coffee taken and notifies you when you should take the next caffeinated drink for maximum productivity, it even tells you when to nap so your energy lasts the whole day instead of crashing and much more...

It's on the App Store as RizeAI https://apps.apple.com/us/app/rizeai-maximize-your-energy/id6762402079. i built by myself, it's early stage right now, and I want honest feedback, what's confusing, what's missing, what you'd never use. Tear it apart.

Would love a website and idea review, be honest

Solo founder. I built a web app that locks your crypto until a date you pick (up to 10 years), no early withdrawal. Once it's locked, it's locked — the point is to kill panic-selling.

Came from my own problem: couldn't stop trading at the worst times, so I made it impossible.

Link:TimeLock

The problem I kept hitting: I'd drive a couple hours to a spot and get flat midday light, or show up "an hour before sunset" and miss the actual window. Weather apps tell you if it'll rain — nothing told me when the light would actually be good, here, on this date.

So I built PrimeHour. You add your destinations + dates and it turns the trip into a shot-by-shot plan:
• Every location gets a 0–100 light score from sun angle, cloud cover, and forecast — with a plain-language reason for the number, not a black box
• A live sun & moon compass, an auto daily schedule with "set up by" times, and AI camera settings tuned to the gear you own
• Offline-first, so it all works in a canyon with no signal

Free to plan unlimited trips; Pro ($4.99/mo) adds the AI planning, full forecast, and auto-schedule.

Two things I'd genuinely love feedback on:

1. Does a single 0–100 "light score" feel trustworthy, or would you rather see the raw factors?
2. What would you like to see implemented in the future?

👉 https://apps.apple.com/us/app/primehour/id6772597417

Hi everyone,
I’m looking for Android testers for Facet, a privacy-focused photo gallery app I’ve been building.
The idea is simple: instead of manually hiding photos in a vault, Facet acts more like a normal gallery that adapts what it shows depending on context. It keeps everything on-device, with no cloud photo upload and no analytics.
I’m currently preparing for a quiet Android release and need more testers to help validate the app across different devices, photo libraries, and real-world usage.
I’m especially looking for feedback on:
Whether the gallery feels natural to use
Whether the adaptive filtering makes sense
Any obvious false positives/false negatives
Performance on different Android devices
Anything confusing, broken, or missing
Sign up here: facetapp.io
It’s early, but the core app is working. Any testers or feedback would be hugely appreciated.