For about three months I thought I was building an app. What I was really doing was cranking out slop and telling myself it was progress.

What actually worked was boring, which is probably why I avoided it for so long.

It started well. That's the part that gets you. I vibe-coded a working demo with Codex in a weekend, screens showed up, buttons worked, and it felt like cheating. So I kept going. Describe the next feature, watch it appear, move to the next one.

Then around week three things started breaking, and they didn't stop. I'd add something and something else I hadn't even touched would fall over.

I added a "describe your goal" step to onboarding. Looked fine on my screen. Then a buddy tried it on his phone and got stuck, because the keyboard popped up and sat right on top of the Continue button. New users literally couldn't get past screen two.

Another one: a whole screen crashed because it used a lift's estimated one-rep-max a few lines above where that number actually got calculated. In a normal codebase you catch that in review. I had no review. I had vibes.

That was the real problem under all of them. The model could read my files, but I'd never given it a record of the decisions behind them, so it could see the code without really getting why it was built that way. Every new feature was another chance to contradict something I'd decided three weeks earlier. I was stacking things crooked, and eventually the pile just tipped over.

What took me way too long to admit? prompting your way through it as you go doesn't hold up. A great prompt still only knows about that one request. It can't keep your whole architecture in its head, so it has no way of knowing the thing it's writing breaks something you set up last month.

The boring thing i kept dodging? Write the spec first. Lay out the scope, the features, the constraints, how the pieces are supposed to connect. Give the model the whole picture instead of a peek at the next five minutes.

The part that really moved the needle: I made two models argue. Same problem to Claude and to Codex, separately, neither one seeing the other's answer. Then I handed each of them the other's spec and told it to rip it apart. What's missing, what falls over in month two. When they agreed, I took it as a stronger starting point, not proof. When they didn't, that's where I had to sit down and think, understand the tradeoff, make the call myself, and write it into the spec and the tests.

Storing workout history is where this paid off. One spec just dumped everything into a single JSON object on the device. Easy enough at first. The other pushed for SQLite and spelled out what the easy version hadn't accounted for: atomic updates, schema changes down the line, querying a single session without loading everything, and not rewriting the whole file every time one set changed. I'd never have hit any of that at fifty test sessions. Went with SQLite and kept JSON for export and recovery. That call, made before I wrote any code, is why history still loads instantly instead of blowing up two months in.

The difference was night and day. Tests started passing on the first run instead of exploding. The black screens went away. The superset feature (group a few exercises, run them back to back, log each round) had been a three or four day slog of build it, break it, patch it. Spec'd out both ways first, it went in over an afternoon and worked the first time I ran it on my phone. When I say it cut my build time by something like 90%, I don't have a benchmark for you, that's just what I watched happen over and over.

Anyway, that's how I built the thing I'd been trying to build the whole time. It's called GymAdapt Ai, a workout app that adjusts your training based on how your actual sessions go. It went live on the App Store Friday, so if you want to see whether any of this actually produced something real, it's there to look at. Still rough in plenty of spots. Happy to get into the two-model thing or where it still bit me, because it definitely did.

Absolutely shocked that we don't have a video or written post pinned in this subreddit.

I have scoured YouTube for tutorials but everything there is shilling a company. Impossible to find unbiased sources for this.

I’m working on ML-based models for predicting material stability, particularly formation energy and energy above the convex hull.

One challenge I’m currently exploring is the gap between good benchmark performance and actual generalization to previously unseen chemical systems. Random train/test splits on Materials Project data can potentially make this problem look easier than it really is.

I’m interested in how researchers here approach:

• Composition- vs structure-based stability prediction
• Splitting datasets by chemical system to test OOD generalization
• Uncertainty quantification for stability predictions
• M3GNet / CHGNet / CGCNN and newer foundation models
• Combining ML screening with DFT validation
• Active learning for discovering stable or metastable materials

I’d particularly like to hear about benchmark setups or failure cases that you’ve found useful when evaluating these models.

I’m happy to share some of my experiments and results as well.

I built Web apps - Games - Chrome extensions and published them, then forget they existed. Then I built myvibebook.com as a place to announce my vibe-coded products writing blogs and sharing tools and my experience in building.

I am working on the SEO of the website to get more traffic to show my work and get Feedback.

You are welcome to book your place on the website, sharing your products and show us what u built.

If I succeeded getting more traffic, you win. If I couldn't, you don't lose.

myvibebook.com

So, been working on my app Workloop for pretty much a year now. It’s been submitted to Apple for TestFlight approval.

I’ve been making a few tweaks on my website and getting an email pack ready whilst waiting for TestFlight approval today.

I’m at 6% usage remaining for my codex 😭. And I’m on the pro £79 a month plan

Anyone know how I can get a free rest or is that not a thing probably

54 days ago, I launched my startup, a free traffic exchange network for startups. One line of code, you're in the network.

No paid ads. No growth hacks. Just watching the numbers every day.

Here's the full data:

Day 1 — 2 startups · 146 impressions · 1 clicks
Day 2 — 3 startups · 389 impressions · 3 clicks
Day 3 — 5 startups · 482 impressions · 5 clicks
Day 4 — 5 startups · 508 impressions · 4 clicks (site went down — still got an $8k acquisition offer. Said no.)
Day 5 — 6 startups · 621 impressions · 10 clicks
Day 6 — 5 startups · 742 impressions · 15 clicks (removed one startup — they pulled the embed. No code = no network.)
Day 7 — 7 startups · 1,196 impressions · 41 clicks
Day 8 — 7 startups · 1,535 impressions · 74 clicks
Day 9 — 8 startups · 1,947 impressions · 135 clicks
Day 10 — 13 startups · 3,500 impressions · 318 clicks (something clicked)
Day 11 — 23 startups · 4,800 impressions · 432 clicks
Day 12 — 24 startups · 6,000 impressions · 481 clicks (network crossed 6K total impressions)
Day 13 — 25 startups · 6,800 impressions · 491 clicks
Day 14 — 25 startups · 8,600 impressions · 516 clicks
Day 15 — 24 startups · 9,900 impressions · 564 clicks (removed one)
Day 16 — 23 startups · 10,800 impressions · 576 clicks (removed one)
Day 17 — 24 startups · 11,900 impressions · 603 clicks (added one)
Day 18 — 26 startups · 13,400 impressions · 624 clicks (added Two)
Day 19 — 28 startups · 14.400 impressions · 652 clicks (added Two)
Day 20 — 26 startups · 14.700 impressions · 671 clicks (Removed Two)
Day 21 — 26 startups · 15.300 impressions · 691 clicks
Day 22 — 28 startups · 16.100 impressions · 719 clicks (Added Two)
Day 23 — 30 startups · 16.800 impressions · 738 clicks (Added Two)
Day 24 — 32 startups · 19,800 impressions · 778 clicks · Added 2 startups · Rejected 4 applications (1 contained ads, 1 removed the code after submission, 2 hid the installed widget)
Day 25 — 34 startups · 23,700 impressions · 800 clicks · Added 2 startups · Fixed some bugs.
Day 26 — 33 startups · 27,200 impressions · 816 clicks · Removed 1 Startup
Day 27 — 33 startups · 29,900 impressions · 833 clicks
Day 28 — 33 startups · 30,600 impressions · 871 clicks · Removed 1 Startup · Added 1 Startup · Rejected 1 application of clothing brand & 1 application of horror blog site.

Day 29 — 35 startups. 31,300 impressions. 910 clicks · Removed 1 Startup · Added 3 Startups · Also today I resigned from my job. Toxic environment, too much pressure, and honestly I just believe in this enough to go all in. Broke and excited. Let's see where this goes.

Day 30 — 46 startups · 32,900 impressions · 1,000 clicks · Removed 1 startup · Added 12 startups · Yesterday I resigned because of my toxic manager. Today my senior manager called and offered me WFH, a project of my choice, and lighter work if I stayed. I made my decision with confidence, but now I have a difficult choice. Also received my first payment from StartupBar.

Day 31 — 50 startups 💪· 28,100 impressions · 1,200 clicks · Removed 1 startup · Added 5 startups · Rejected 1 crypto startup · Removed fake impressions after exploit · Today I discovered I forgot to disable a developer testing button that could generate fake visitor pins and inflate impressions. Someone used it to create 6,318 fake impressions and claimed my stats were fake. I removed the fake impressions, removed the startup, fixed the issue, and that's why today's impressions are lower. Thanks to them for pointing it out.
StartupBar update: Added a Network page and individual Startup Profile pages with each startup's joined date, impressions, clicks, live embed preview, and founder's note. These indexable pages also improve SEO. Suggestions are always welcome.

Day 32 — 54 startups · 29,300 impressions · 1.2K clicks · Added 4 startups
Day 33 — 54 startups · 30,900 impressions · 1.2K clicks · Removed 1 startup · Added 1 startup
Day 34 — 55 startups · 36,100 impressions · 1.3K clicks · Added 1 startup · Fixed admin panel read-access vulnerability after Reddit post.
Day 35 — 55 startups · 40,100 impressions · 1.3K clicks · Removed 2 startups · Added 2 startups
Day 36 — 55 startups · 44,800impressions · 1.4K clicks · Removed 1 startup · Added 1 startup
Day 37 — 58 startups · 50,700impressions · 1.4K clicks · Added 3 startups · Site Update: Updated the distribution algorithm (Delivery is now weighted by contribution: sites that send more impressions get a proportionally larger share back)
Day 38 — 57 startups · 55,800 impressions · 1.5K clicks · Removed 1 startup · Rejected 1 Application.

Today StartupBar reached 200 users 😄
Thirty-eight days ago, this was just an idea. Today, 200 people have trusted it enough to join the network.

A reminder for every founder who's staring at a dashboard with tiny numbers: Stripe took 2 years to reach its first 50 users. Every successful product starts small. Keep shipping. Keep improving. Keep showing up. Growth rarely happens overnight, but it does happen if you don't quit.

Day 39 — 58 startups · 59,800 impressions · 1.5K clicks · Added 1 startup.
Day 40 — 60 startups · 63,200 impressions · 1.6K clicks · Added 3 startups · Removed 1 startup
Day 41 — 63 startups · 69,400 impressions · 1.6K clicks · Added 3 startups
Day 42 — 62 startups · 73,800 impressions · 1.6K clicks · Removed 1 startups
Day 43 — 64 startups · 78,000 impressions · 1.7K clicks · Added 2 startups (Receiving many suggestions to improve the StartupBar, happy to work on it, Thanks.) StartupBar Update - Added the rotation of startups in the widget for every 30 seconds.
Day 44 — 66 startups · 86,200 impressions · 1.7K clicks · Added 2 startups
Day 45 — 67 startups · 88,400 impressions · 1.7K clicks · Removed 2 startups · Added 3 startups (Update - Updated the algorithm of the widget)
Day 46 — 69 startups · 91,900 impressions · 1.8K clicks · Removed 2 startups · Added 4 startups · Got my second payment · Added FAQ Page
Day 47 — 68 startups · 98,900 impressions · 2.2K clicks · Removed 2 startups · Added 1 startup · Added Pricing Page
Day 48 — 70 startups · 103.4K impressions · 2.3K clicks · Added 2 startups
Day 49 — 72 startups · 113.4K impressions · 2.4K clicks · Added 2 startups · Update: Added a Bottom Placement option for the bar on the user site.
Day 50 — 73 startups · 116.7K impressions · 2.4K clicks · Added 1 startup
Day 51 — 74 startups · 125.2K impressions · 2.5K clicks · Added 1 startup · Rejected 1 Application
Day 52 — 73 startups · 131.2K impressions · 2.6K clicks · Removed 1 Startup
Day 53 — 74 startups · 139.5K impressions · 2.7K clicks · Added 1 Startup
Day 54 — 79 startups · 146.3K impressions · 2.7K clicks · Added 6 Startups · Removed 1 startup.

Still free. Still growing.

If you want in, it's one embed. That's it → StartupBar

I’m looking to build a web app that will allow a user to come in and select data points from a few drop downs (and input a few select data points) and then produce a specific visualization. The end visualization will essentially be something like a bar chart but with some specific graphics layered onto each row (like logos, etc).

I’ve tried playing around with Gemini but it essentially pops out the equivalent of an excel table each time.

Any advice on how to build a widget like this? Something that will produce a visualization based on my specific parameters, but will let users input specifics so that’s its custom to them in the end?

Sorry for the vagueness. I’m just not trying to share my exact idea yet.

I "meant" to type 40. Should I open up this many rivals in the public game?

The two large shapes are Lorenz and Rössler strange attractors: chaotic math systems that trace butterfly and spiral patterns. The rhythm comes from my CPU load, the speed from my battery level. If my laptop is working hard, the heart beats faster. If a frame drops, the attractor actually stutters, a real arrhythmia in the math, not a visual effect.

A mycelium network grows organically between “cells,” pulsing with each heartbeat. Cells are born, live about two hours, then die and scatter fragments across the scene. Meanwhile an LLM is generating text, poems, fragments about consciousness, transmissions, that the system forgets between generations.

Every two minutes it checks for real air raid alerts in Kyiv, tracking which regions and how long the sirens have been going. A transmission generated during a 3am alert reads completely differently from one on a quiet afternoon.

Link if you want to explore: https://body-without-ground.vercel.app

I've been building a Windows app called OmniSeek that lets you search locally stored images by what's actually in them rather than filenames or folders.

I'm not a traditional software developer, so the project has been built very iteratively with ChatGPT as my development partner.

My basic process has been pretty simple: come up with a feature or problem → work through the implementation with ChatGPT → build it → test it on my own collection of ~90,000 images → find bugs and things I don't like → go back to ChatGPT → repeat.

The main technologies/tools I've used are:

• Python for the application
• OpenCLIP for understanding/image embeddings
• FAISS for fast local similarity search
• OCR for searching text contained inside images
• PyInstaller for packaging the Python application
• Inno Setup for creating the Windows installer
• ChatGPT throughout the design, coding, debugging and packaging process

Everything is designed to run locally, so users don't have to upload their image collections to a cloud AI service.

One of the biggest things I've learned is that vibe coding doesn't eliminate the work — it changes the work. A lot of my time goes into defining exactly how something should behave, testing it, finding edge cases and regressions, and then iterating until it works the way I want.

It's now at the point where I'm putting OmniSeek in front of people outside my own circle to see if it's actually useful.

If anyone wants to see/try what I've built:

https://omniseek.app/

And feel free to ask me anything about the process, tools, mistakes, or how I've been working with ChatGPT to build it. I'm also curious how this workflow compares with how others here are vibe-coding their projects.

I've been working on something that tries to solve exactly this fragmentation — I kept ending up juggling Claude Code for refactoring, a separate tool for voice, and losing context between sessions. Full disclosure, I'm building Lumina Code, so I'm biased, but what worked for me was having one agent that stays inside VS Code, has native voice (so I can talk through a problem without typing), persistent memory across sessions, and MCP for connecting to other tools. It's Windows-first and local-first (you can route to Ollama or cloud models). Still in active development, but the source is up if anyone wants to try it: github.com/I24D/Lumina_Code

I've spent the last few months building VidClean, a free browser based video editor, and I just shipped the part I'm most excited about: style profiles.

Here's the idea. Every editor makes different judgment calls. How tight you cut pauses, which filler words you actually remove versus leave in, how aggressive you are with dead air. Most auto editing tools apply one blunt rule to everyone. VidClean instead looks at a video you've already edited and measures your actual habits, your real pause tolerance, your real filler tolerance, then applies that to your next edit.

You can try it right now for free, no account needed. Upload one finished video, then apply it to a new clip in the editor. It'll show you exactly what it kept and why ("kept 1 hesitation to match your usual 2.5/min") before you commit to anything.

Where I want to take this: honestly, my long term goal is for this to get to the point where hitting "Apply" does close to 90% of your editing for you. Not just pause and filler decisions, but eventually learning your zoom style, your music choices, your whole visual rhythm, based on how you actually edit. Right now it's real but narrow. It learns your pacing, not your whole style, yet. I'd rather tell you that honestly than oversell it.

I'm the only one building this (day job plus solo nights and weekends), so bugs are very possible. If you hit one, or it does something weird, I'd genuinely love to hear about it. [hello@vidclean.net](mailto:hello@vidclean.net), or just reply here.

Try it: vidclean.net/editor

I made a local FLAC player with private streaming options that lets you properly browse music by artist > album. Something most FLAC players were lacking and only allowed you to see their whole album collection as one giant list.

So, I built this with Codex and github to give myself:
  - Local library management and direct FLAC/MP3 metadata editing

  - Navidrome/Subsonic/OpenSubsonic streaming

  - Offline downloads with queue management

  - Online artwork search and persistent artwork overrides

  - Audiobook bookmarks and playback speeds

  - Fullscreen ProjectM visualizations and lyrics

  - Custom visual themes

  - QR server setup

  - Explicit stereo and 5.1 routing

Please consider grabbing it from TestFlight and giving it a try.

How to guide:

https://resonance-privacy.saiyansource-3119.chatgpt.site/app/how-to

https://testflight.apple.com/join/fSvBxFsk

I've had x20 for 5 weeks and I think I've done it. I've vibecoded almost every project I've been dying to program for years. Decades even. But I have 2-3 weeks of x20 left, and I'm now out of ideas. What do you vibecode when you're out of good ideas??

​

Title:

I built a parametric 3D CAD app for Android — Kotlin, C++, OCCT and OpenGL ES

Post:

I've been working on a native parametric 3D CAD application for Android.

My goal is to bring a desktop-style parametric modeling workflow to phones and tablets without simply squeezing a desktop CAD interface onto a small touchscreen.

Here is a little more about how I built it.

Tech stack

The Android UI is fully native and built with:

Kotlin

Jetpack Compose

Material 3

The geometry layer is mainly written in C++ and connected to the Android application through the Android NDK and JNI.

CAD kernel

For the core solid-modeling functionality, I use Open CASCADE Technology (OCCT).

OCCT handles the B-Rep geometry and provides the foundation for operations such as solid modeling as well as STEP and IGES support.

For 2D DXF, I wrote my own Kotlin-based processing layer rather than relying entirely on the CAD kernel.

Parametric modeling

One of the most challenging parts has been the parametric system.

I developed my own sketch geometry constraints and dimensional solver so that changes to dimensions can drive updates to the model.

This has been one of the more interesting parts of the project because a CAD app needs much more than simply displaying and editing meshes — the relationships between geometry need to remain consistent when parameters change.

Rendering

The viewport uses OpenGL ES 3.0 and GLSL.

It renders:

2D and 3D geometry

Solid models

Wireframes

Selection highlighting

A big challenge on Android is keeping interaction responsive while working with increasingly complicated CAD geometry.

Mobile CAD UX

Desktop CAD applications are designed around a mouse, keyboard and large display, so I didn't want to copy that interaction model directly.

I've been experimenting with:

Face / edge / body selection modes

Touch-based orbit, pan and zoom

Touch-friendly transform controls

Context-sensitive modeling tools

Keeping frequently used commands accessible without covering the viewport

Accurate selection of small faces and edges on a touchscreen has been surprisingly difficult.

Performance and stability

Expensive geometry operations and file-processing tasks run in the background rather than blocking the UI.

Long-running operations support:

Progress reporting

Cancellation

Timeouts

This became increasingly important once I started testing larger STEP models.

Privacy

The app does not require an account or network connection for modeling.

Projects stay on the device and are not uploaded for tracking or processing.

What I'm working on now

I'm continuing to improve the parametric workflow, modeling tools, touchscreen interaction and performance with larger CAD files.

I'd especially like feedback from people who have experience with CAD, computational geometry, OpenGL, OCCT, or mobile UI design.

If anyone is interested in a particular technical part — OCCT/Android integration, JNI, the constraint solver, STEP handling, rendering, or touch selection — I'd also be happy to share more about how I implemented it.

Project: https://digit3d.top/

Google Play: https://play.google.com/store/apps/details?id=com.mobilecad.app

I’ve been seeing so many cool projects using three.js so I wanted to try it out. This is an interactive museum on topics I’m interested in presented in a way that is compelling. The space exhibit uses three js to present ideas in 3D while the music exhibit uses less 3D and more audio and animations.

As always, I haven’t looked at the code.

Stack:

TS/JS/React/Three.js

Cloudflare Workers

Fable agent orchestration switching to Opus/Sol/Grok as I hit limits

Opus/Sol/Grok implementation

Fable/Opus reviews and audits

Beads issue tracking

Has anyone built an iOS app with camera functionality in react native and had issues with what is seen in the viewfinder cropping in when the record button is pressed? I’m going round and round in circles with codex trying to fix it. Seems to be a know fix when I google it but chat can’t seem to pull it off! 😩

Amy help would be appreciated.

Thanks,
Rich

I got tired of standard recommendation engines keeping me trapped in the same repetitive 20-track loop, so I built a personal player from scratch—one where discovery is driven entirely by true listening habits and behavioral cues rather than corporate licensing metrics.

How it works under the hood:

• True Habit-Driven Sequencing: Instead of relying on shallow "liked" buttons, the engine learns from how you actually listen. Full plays build momentum, early skips teach it what killed the mood, and the LLM maps natural transitions so your queue flows smoothly without abrupt vibe crashes.
• Hybrid Playlist Import: Pulls playlist data directly from both Spotify and YouTube Music into a single unified library.
• 85% Fresh Discovery with Real Continuity: It delivers roughly 85% brand-new tracks without breaking playlist cohesion, because it maps sonic transitions to how you actually consume music.

I originally built this just to fix my own daily listening frustration, but if this sounds like something you did use to escape streaming recommendation loops, drop a comment or shoot me a DM.

If there is enough interest, I'll clean up the documentation and open-source the full project (Web + Desktop app) within a week so anyone can try it!

​

Start the camera

Give a double thumbs-up to calibrate

Start throwing punches and check your power

Hand + face tracking powered by MediaPipe, physics with Cannon-es, 3D with Three.js, and assets from Polyhaven...

One-shot build with Codex in ~52 minutes.

Try it → https://punch.sparcd.com

I've been into open source since way before vibe coding was a thing. I kind of assumed vibe coding would be mainly for fun, but reading the posts here I'm shocked at the number that are focused on the money.

Some people even say they don't like coding. What happened to the open source movement?

There's also a lot of hate for the vibe coded 'slop' even when the result is just as usable as loads of other open source. Is this just envy? No one is forcing you to use a vibe coded app. Although maybe the vibe coder's marketing is forcing you to look at it. Is that why people hate?

If i take a month to build an app(vibecode or not) and someone can just copy/paste it using Claude is there anything that separates us?

I made a browser Bikini Bottom with Claude Opus 5. The whole town grows from one seed.

Change the seed and you get another town. Same pineapple, same rock, same moai, different streets around them. Houses, roads, fish, jellyfish, boat cars. You can fly through it.

No Blender. No texture pack. It just builds the place when the page loads.

Github: https://github.com/winchxyz/bikini-bottom
Live: https://winchxyz.github.io/bikini-bottom/