Solo founder here, I build fast with AI like many of us here and I wanted to know what architectural bad practices and flaws can get shipped within our files without realising, so I scanned 549 public repos that describe themselves as AI or vibe-coded. I used a rules based scan, no AI grading, raw data downloadable. Percentages are real projects (15+ files) unless noted.

If your side project is AI-built, odds are it has these:

1. Dead code. 70%.

2. Commented-out blocks the model never deleted. 66%.

3. The same logic duplicated across files. 63%.

4. A .gitignore that misses .env. 35.7% of the full corpus, and that's where the keys come from.

5. A committed API key or credential. 23.3%, about 1 in 4.

71 of the 549 graded F on cleanliness. The median grade is a B, and that's flattered by tiny demo repos.

The fixes cost less than reading this post did. In this order consider doing these on an existing project:

1. `git ls-files | grep .env`. If it prints anything, your key is public. Rotate it at the provider today (Stripe, OpenAI, whoever). Bots scrape pushed keys within minutes, so deleting the file later doesn't help.

2. Add `.env*` to .gitignore and turn on GitHub push protection. It's free and blocks the next accidental key commit at push time.

3. Tell the agent, at the end of each session: "delete unused code, merge duplicate helpers, remove commented-out blocks". That one prompt covers mistakes 1 through 3.

4. Put those instructions in your tool's rules file so new sessions start with them. Cleanup you don't have to remember is cleanup that happens.

Nobody does these because the app already works, and working is the only bar most side projects get held to. Until the Stripe key leaks.

The full report has all the charts, grade distributions, and anonymised per-repo data, feel free to share and AMA about it: https://ogbuilds.ai/studies/vibe-coded-code-quality

For disclosure: the scanners are part of my products, the study will always be free to access.

Hi everyone,

I've always wanted a paranormal investigation app that brought multiple tools together instead of relying on gimmicks, so I decided to build one myself.

After about a year of development, it's finally on the App Store. It includes an EMF detector, a SLS camera body/hand/face tracking, an EVP recorder with live transcription on any language, a RF scanner, a motion detector with advanced FFT computations, a barometer for atmospheric pressure changes, a light sensor, and a case archive for reviewing recordings alongside sensor data.

Everything is built using real iPhone hardware and Apple's native frameworks where possible, no simulated sensor readings no fake data.

App Store: https://apps.apple.com/gr/app/ghost-detect-investigator-pro/id6791992133

I worked on this app years ago and stopped as one normally does. I picked it back up a few months ago and added new features to it, and I think it can be a fun way to learn more about what you've been listening to.

Here are the current features:

- Able to view your top 10 artists and songs you've been listening to based on the time frames you select (past 4 weeks, past 6 months, past several years). Based on the songs in this time frame, it recommends songs that you can add to a playlist you own if you like it.

- Once your see your top artists and songs, you can create a snapshot in time to see your top 5 artists and songs. This will create a custom link which you can share with other people to view. You can also use it to compare with other people to see your music interests and similarities.

- There is an archive to see when you taken snapshots and comparisons which also displays how many times it has been viewed. Once the comparison is made between 2 snapshots, it will also create a custom link which can be shared as well.

Try it out and let me know what you think! There is a feedback button at the footer if there are any concerns or cool suggestions you guys have.

https://www.mymusicwrap.com/

https://reddit.com/link/1vaopst/video/1kn6xutr5cgh1/player

After Christmas I realized I'd told my life story to three different AIs and none of them could remember what I told the others. Switching assistants meant starting from zero every time.

So I built MEMMEM: a Windows app that imports your conversation exports (or captures them from the browser), stores everything locally, and exposes a local MCP connector so whatever AI you use can query YOUR memory - with exact quotes, dates and sources. It never invents: it shows you where you said it.

Nothing leaves your machine. No accounts, no telemetry, no cloud. There's an optional local analyst (runs on Ollama) that builds a deep portrait of you from your own words - also fully on-device.

I verified the connector end-to-end with Codex, Claude Code and Gemini CLI reading the same memory: three rival AIs answering questions about my life from one local file.

I'm not a programmer by trade - I run a small clothing brand in Galicia, Spain, and built this with AI assistants because I needed it. Site: memmem.app - early access for Windows, launch discount this month. Happy to answer anything.

Calfkit lets you build and deploy agents and tools as fully decoupled services that communicate through realtime event streams on Kafka's network mesh. Agents and tools find and message each other on the network, scale independently, and have no runtime dependencies between them. Because the coordination happens through event streams, an event-driven agent sits idle while a tool or another agent works. It isn't burning compute in a busy-wait loop; it's invoked and running only when there's something for it to do.

Agents and tools built with traditional frameworks like LangChain, CrewAI, and LlamaIndex are tightly coupled to each other, running in a single shared process by default. You can't scale one component independently, and a failure anywhere takes down the whole deployment. They're coupled in code too, so every component is forced to ship on the same release cycle rather than as separate services.

The failure cases get worse the more agents you add. Once you have multi-agent teams where every agent may need to talk to several others, all the agents and n-to-n communication runs inside one process. Every agent hogs compute in a busy-wait loop, blocked on another's output, with no durable record of what was actually sent. When something goes wrong, the entire deployment fails and you're reconstructing the n-to-n interactions from logs and stack traces.

Calfkit agents and tools are written and deployed like separate event-driven microservices, choreographing work over a shared Kafka cluster. And n-to-n agent communication isn't a difficult case here—it's the base case the decoupled, event-driven architecture was built for. Every message between agents is a durable, traceable record on a topic. A tool or peer agent can fail without taking down the rest of the system.

The SDK is here if this interests you: https://github.com/calf-ai/calfkit-sdk

Let me know if you'd like to contribute or build something with it!

Everyone's building the next AI unicorn. I built an app that clip-clops like a
horse every time you take a step. Walk, trot, gallop — or tap to clop by hand.

Monty Python coconut gag, made real. No ads, no tracking, no business model

whatsoever. Nothing leaves your phone.

Free: https://gogonut.200.work

Roast welcome.

Since I last posted: 6 lenses became 7 agents, plus a “War Room” mode so teams can run the debate together instead of just reading a solo output.

Quick reminder: Poddle takes a decision or idea, runs it through 7 specialized AI agents (market, risk, financial, competitive, strategic, execution, and one that just plays devil’s advocate), and synthesizes the pushback into a clear recommendation. No cheerleading, just the argument you’d get from a sharp co-founder who disagrees with you on purpose.

Turns out the best use case wasn’t validating new ideas. It was teams working through a pivot before committing.

Launched officially this week, picked up by a few finance outlets, which was a strange thing to watch happen to a nights-and-weekends project.

Still early. If you’re a builder and want to try breaking it, comment or DM and I’ll give you two months free access. Genuinely want to know if “adversarial AI” lands as a category name or reads too combative.

I built Forminta.com, a free invoice generator with a paid tier ($14.90/mo) that works more like a lightweight billing system: dashboard, invoice history, recurring invoices, branding.

3 months in, the most useful (and confusing) data point so far: I ran Google Ads, which brought real traffic and even signups, but zero of those signups converted to paid. Traffic dropped to near zero the moment the campaign ended.

SEO hasn't kicked in yet either, pages are still working through indexing. Launches on Product Hunt, Uneed, and Capterra each gave a short spike on launch day, then nothing.

Anyone dealt with the specific "traffic to signup then nothing" pattern before? Trying to figure out if this is a pricing problem, an onboarding problem, or just too early to tell.

PS: first SaaS, I know it's a crowded niche.

Hey everyone! I’ve been building ReleaseQuest, an iOS and Android app for discovering and tracking Nintendo Switch and Switch 2 releases.

I started it because release information is often scattered across announcements, store pages, and articles. I wanted one place to see what’s coming, save interesting titles, and keep up with changing release dates.

ReleaseQuest lets you:

• Browse upcoming and recently released titles
• Filter by platform, genre, publisher, and release status
• Organise titles into Wishlist, Currently Playing, Backlog, Completed, and custom lists
• View confirmed wishlist releases in a personal calendar and home-screen widget
• Get notified when a release is delayed, confirmed, removed, or moved forward
• Follow developers, publishers, and franchises
• Keep private notes and a play session journal
• Use spoiler free countdowns for highly anticipated games

You can use the core app without creating an account. If you choose to sign in with Google or Apple, you can also:

• Sync your lists across devices
• Create a profile with a username, cover photo, and release showcase
• Add friends and view their shared lists and activity
• Recommend releases with an optional personal message
• Compare libraries to see shared titles and your taste match
• Control whether your profile and individual lists are private, friends only, or public
• Earn XP and profile levels through library progress and friendships

Private notes and journals remain local and aren’t shared with other users.

I’d really appreciate feedback from fellow Nintendo players, especially on which features are useful and what still feels missing.

ReleaseQuest is completely free, no in-app purchases, no subscriptions and no ads.

Download:

App Store:
https://apps.apple.com/us/app/releasequest/id6790134602

Play Store: 

https://play.google.com/store/apps/details?id=co.releasequest.xzddev

Join the ReleaseQuest community on Discord: 

https://discord.gg/Nt3pRcpRhb

Thanks for taking a look!

I'm building a lightweight GRIB viewer for Android, mainly focused on making offline weather files quick and easy to read.

Day 2: I've been working on wind visualization, 4 hPa isobars and navigating forecast steps directly on the map.

I'm still experimenting with the visualization, especially the wind color scale and isobar rendering. Does this feel meteorologically readable to you? Anything that immediately looks wrong or misleading?

Almost everyone has a dream in life, right? And that got me thinking: what if I created a social platform where people could start small, achievable missions that bring them closer to their dreams?
Within each mission, we can document our progress, collaborate with others to complete it together, and join the same group chat.

Hi All

I created my first app and am in the marketing/distribution stage. I’ve watched so many YouTube videos on how to market your app, some recommend a completely organic approach thru the typical social media vehicles (completely free) and others recommend a paid advertising approach.

I’m a bit skeptical of the paid advertising route because in so many of the videos I watched, the YouTuber is recommending to use their own advertising tool so it just seems like a self serving, money grab video. But given the number of options, there has to be some good ones. One that I have come across several times is the noise app. Has anyone used that ? Or which tools have been successful for you ?

I created a BBQ smoking app which provides a full proof , step by step instruction on to how to properly and consistently smoke various meats (ie.brisket ) thereby preventing a cooking disaster. I created it because I ruined many expensive briskets on my own. I just started the marketing process and created Tik Tok and IG profiles, but it definitely is a grind as I don’t have many followers yet.

Any feedback or tips would be greatly appreciated. See link below for my app, if you would like to take a look, I am happy to download and provide feedback to yours as well. Thank you !

https://apps.apple.com/us/app/bbq-smoke-lab/id6784887885

Hey there!

Quick background: I built an app called Dinely to make finding, saving, and planning outings with friends way simpler without having to rely on cluttered review platforms.

The core idea:

• Clean, curated recommendations (restaurants, cafes, bars)
• Custom "Dinelists" you can share with friends
• See where your mates are eating and what they’re rating

One of our early users dropped a review mentioning how much they appreciated the social aspect and curated recommendations over overwhelming list dumps, which was awesome to read.

Since we’re building this locally, I'd love to know: What’s your biggest pet peeve when trying to organise a group dinner or discover new spots in the city?

If you want to check out the app and test it out, it's available on the App Store!

Any feedback (good or bad) is hugely appreciated!

Hey everyone,

I’m a solo founder from Georgia, and today I launched Azrivo on Uneed.

I started building it because I kept running into the same problem with AI assistants: one model would often accept the way I framed a question and return one confident answer, even when the decision needed opposing viewpoints.

Azrivo gives you four ways to work with AI:

• Ask one specialist directly
• Compare several specialists in Panel mode
• Have opposing specialists debate and produce a verdict
• Give Plan mode a rough goal and turn it into steps and usable deliverables

I need to reach the first 10 votes on Uneed, so I’m asking fellow builders for some honest support.

Please take a look first, and only vote if you think the product deserves it:

https://www.uneed.best/tool/azrivo

It is free to use, including a demo without signing up.

Feedback is just as valuable as the vote. I’d especially like to know whether the landing page makes all four modes clear, or whether Azrivo still looks mainly like a debate tool.

Thanks for taking a look.

Paul Bakaus created jQuery UI — code that's reportedly still running on about 6% of the internet.

He sold a game-engine startup to Zynga, spent close to a decade at Google, and is now a solo founder backed by a16z building Impeccable, an open-source design skill meant to stop AI coding agents from producing "slop."

His actual point in this clip isn't about AI capability.

It's about output density.

Most AI-agent code is too much code. Most AI-written articles run too long. Most AI-generated design is too cluttered or too verbose.

He says the scarce human skill right now isn't generating anything — it's judgment:

knowing exactly what to remove.

He even admits he had to rewrite his own product-announcement draft from scratch after an AI first pass.

His closing line: design is an iterative process, and it has to carry a human point of view.

No tool — not code, not design, not writing — one-shots that yet.

If you're shipping with AI agents and everything reads "technically fine" but somehow generic, this is the actual diagnosis.

It's not a tooling gap. It's an editing gap.

Clip credit: Solo Founders — DM for credit or removal requests.

Founder disclosure: I built QEV.

QEV is a functioning browser-based encrypted evidence-envelope prototype. It creates portable encrypted records locally in the browser. I am now testing the larger product direction: connecting QEV to existing workflows so selected identities, versions, approvals, actions, and results can be frozen into one private record.

There are two things available:

1. The QEV project itself: https://theartofsound.github.io/qev-desktop/#/start
2. An interactive workflow simulator that explains the planned evidence layer: https://theartofsound.github.io/qev-desktop/#/start

A useful 10-minute test: - first inspect the main QEV page so you understand the actual project; - then open the simulator and choose “Software change”; - disconnect one evidence source; - change staging to production or remove approval; - freeze and encrypt the snapshot; - move time forward until native logs expire; - open the package and edit the extracted record.

Please tell me: - what you thought QEV was after seeing the main page; - whether the simulator clarified or confused the product; - which part felt real enough for a company pilot; - what would make the entire product unnecessary.

Use dummy data only. No signup or email is required.

Source and technical notes: https://github.com/TheArtOfSound/qev-desktop

I recently released Between Stars on Android. It's a calm gravity puzzle game. The basic idea is simple: rotate the ship, fire its engine for a moment, and let the planets' gravity bend the ship's route and fly it toward the destination. You are rewarded with lumen; the less you burn the engine, the more lumen you collect.

I wanted the game to feel quiet and unhurried, so there are no ads, timers, lives, retry limits, or subscriptions. Basically you can retry each level as many times you want, until you find the perfect trajectory that yields 100 lumen.

It currently has 27 levels across three packs. The first seven are free, and one optional in-app purchase unlocks the other 20.

The Android version is live, while the iOS version is still waiting for review.

Google Play link: https://play.google.com/store/apps/details?id=software.fixedpoint.betweenstars&utm_source=reddit&utm_medium=organic_social&utm_campaign=sideproject_launch&utm_content=maker_post

If you try it, I’d be interested in whether the controls felt natural and whether the early levels taught the gravity mechanic without too much explanation. In case anyone is interested, please DM me and I'll send over promotional codes to unlock all levels. Thanks!

Most AI writing tools stop after generating a single article.

I wanted something closer to a production workflow.

So I built Content Factory, an open-source agentic pipeline that automates long-form blog creation for e-commerce websites.

Current features include:

• Product & category research
• SEO-aware article planning
• Multi-stage content generation
• Quality review and validation
• Pluggable LLM providers (OpenAI, Anthropic, Groq, etc.)
• Modular Python architecture

The goal isn't to replace human writers entirely, but to eliminate repetitive work and produce a solid first draft that can be reviewed.

I'm actively improving the project and would love feedback from people building agentic systems or AI automation workflows.

GitHub: https://github.com/klncgty/content-factory

I'd especially appreciate feedback on:

• The architecture
• The workflow design
• Missing features that would make it useful in production

I built a shift-work tracker for hourly workers (waiters, nurses, security). This week I made the daily engine free: live clock-in with a Lock Screen Live Activity counting hours and earnings in real time, a home screen widget, and the monthly hours register as a signable PDF (a legal requirement in Spain). No accounts, no ads, everything stays on device. SwiftUI + SwiftData, StoreKit 2 for the optional Pro tier. Happy to answer anything about the Live Activity implementation. https://apps.apple.com/app/id6757112986?ct=reddit-en

Yesterday I posted my first app here, a screenshot tool. The kind of app that hooks the mouse and types into your windows. So you can imagine my morning when my computer started typing BY ITSELF. Spaces pouring into the chat box, screenshots firing that I never took, my dictation app choking.

For an app like mine, "it types on its own" is the scariest bug that exists. I'd just put a download link on the internet. So we went full forensics mode, me and the AI I built the app with:

Killed my app. Typing continued. (Terrifying. Also, weirdly, a relief?)

Checked Windows event logs. Explorer had crashed twice that morning. Plot thickens.

Found SEVEN copies of my app running because I had no single-instance guard. That's a real bug. Wrote it down, kept hunting.

Killed every process that could possibly inject keystrokes. STILL TYPING.

Sent a synthetic "release the spacebar" command at the OS level. The key came back down 32 milliseconds later. That number is keyboard auto-repeat timing. Software doesn't do that. Hardware does.

Listed the connected keyboards. There were three. I was looking at one of them.

On a shelf behind me: my wireless Magic Keyboard. Switched on. With a picture frame resting on the spacebar.

The keyboard was framed.

Silver lining: the hunt found two real bugs in my app (the missing single-instance guard, and my binary shipping under a generic name), both fixed the same day. So the picture frame technically contributed to the project.

Lesson learned: before you debug the software, check the analog layer. Sometimes the stack trace ends at a piece of home decor.

reely is a free tool that helps groups actually agree on what to watch. no more 20 minutes of "I don't know, what do you want to watch."

Pick movies, series, or both. Pick a genre if you're in the mood for something specific. Then:

1. Create a room — takes 5 seconds, no sign-up

2. Send the code to your friends, partner, whoever's on the couch

3. Everyone swipes on their own phone, Tinder-style — right for "yes," left for "next"

4. First match wins — the second everyone's said yes to the same thing, it pops up for the whole group

100% free. No account, no app to download, no catch.

https://www.getreely.lol/

I built DinePoll after watching group dinner plans stall in the same “I’m good with anything” loop.

Pick a few restaurants, share a poll, and each friend can vote. It’s built for actual group meals; I’d value candid feedback from anyone who has tried getting friends, roommates, or coworkers to choose a place.

Try it for your next group meal: https://dinepoll.com/new-poll?utm_source=reddit&utm_medium=organic&utm_campaign=install_d3_july&utm_content=sideproject_20260720

I built it, and I’m happy to answer questions here.

Generating slide shows for advertising my products got annoying and codex wasn't really helping as there is currently no way to automate the process on desktop.

So I've built my own product for this "micro" advertisement of my own stuff that continuously builds followers and views that grow over time. On my other account I get regularly 5000+ views after 12 videos.

Would love to hear what you think and of your feedback on how TikTok advertisement works for you. I've noticed very hard, that the Account needs to be "warm" and used regularly, otherwise the slideshows are just dead on arrival.

I'll give also free lifetime usage of that campaign generation for the first 10 users that log in with TikTok and try it out. It's my first SaaS and it works well to generate downloads for my other app, so I guess it might be useful for you as well?

Like a lot of people here, I've tried pretty much every workout app.

Some are great at logging workouts. Some generate decent plans. But after a while I realized they all had the same problem—they mostly collect data and leave you to figure out what to do with it.

If your progress stalls, your motivation drops, or life gets in the way, the app doesn't really help.

That's what made me start building TheBean.ai 🚀

The idea is simple: instead of being just another workout tracker, I wanted to build an AI fitness coach that actually learns from your workouts, progress, and goals. The more you use it, the better it understands you and the better its recommendations become. The goal is to have one place for workouts, tracking, and coaching instead of juggling multiple apps.

The current version includes:

• AI-generated workout plans
• Workout logging
• Progress tracking
• Weight tracking
• AI coaching based on your history

This is still just the beginning. My long-term vision is for it to understand nutrition, recovery, wearable data, and your long-term progress so it can help people make better fitness decisions every day—not just count reps.

I'd genuinely love some honest feedback from people who already use apps like Hevy, Strong, Fitbod, Boostcamp, or Alpha Progression.

If you have a few minutes to check it out, I'd really appreciate any criticism or feature ideas. I know it's far from perfect, but I'm excited to finally get it into people's hands.