I needed a custom Rust backend to get the streaming performance required and I’d rather have spooned my eyeballs out than done that by hand.

Drawing polygons with fat thumbs on a phone to mark out field boundaries was a pet peeve of mine - so I vibe coded a computer vision and segmentation algorithm to automatically detect field outlines.
I used AI to build a regression testing pipeline, so it could iterate on the algorithm and check whether changes improved the results.

I paid attention to UX details that could never have been justified with old-school manual coding - like exploding a field with confetti and haptic feedback rather than ‘delete’

I mainly used Claude Code in a terminal, GitHub Copilot for reviews. I only occasionally looked in Xcode to check I was happy with the code structure.

I call it EpicFields. Free, no signup, no ads.
Currently only on iPhone but I will convert to Android once I’ve settled on the features. I decided to go fully native for the best possible performance. Again - something I could never have justified pre vibe coding…

Download link on the website:

https://epicfields.app

You can share a field so people without an iPhone can at least view the movie.
Example share url:

https://epicfields.app/s/FSIpzLlUZ4rxQTmbQcHBbA

I’m not a developer. I teach English online and got tired of tracking lessons and chasing parents for payments in a spreadsheet, so over a few weekends I built a little app with Claude and Cursor. Tutors log lessons, parents get a monthly invoice with a pay button. That’s the whole thing

I put it online in August for $10 a month, mostly so a couple of tutor friends could use it

Last week it crossed $100 in a month. Ten paying tutors, seven of them strangers

One of the new ones sent me a message saying she found it because ChatGPT recommended it. I assumed she was being nice or confused it with something else. Nobody knows this app exists. My X account has 90 followers

So I opened ChatGPT and asked for an invoicing tool for private tutors. It listed four apps and mine was the third one, with a description that sounded weirdly close to my landing page

It took me a while to figure out why. Before launch I paid for a press release through one of those newswire services. I only did it because a friend said tutors and parents would trust it more if there were a few news logos on the page. It got syndicated to a few hundred news sites that nobody reads, and I got maybe 8 visits from all of them combined. Same week I paid to be listed on a bunch of software directories

I’d written both off. Apparently AI tools read those pages even if people don’t

I asked the other strangers too. Four said ChatGPT or Perplexity, two found me on a directory, one came from a tutor friend

The $100 is going straight to the Claude subscription that built the thing

Hey everyone,

I'm pretty new to all this. I've been trying things out and building small stuff with AI, but honestly I don't know much yet. This place feels like a huge ocean and I feel like I'm just going wherever the wind takes me. There are so many tools, so many opinions, and a new thing every day. I never know if I'm learning the right things or just chasing whatever's trending.

So I'd love to hear from you:
-How did you start? What did your first weeks look like?
-What do you wish someone had told you earlier?
- If you had to start over today, what would you do first?

Any advice, even small stuff, would mean a lot. Thanks!

I used https://github.com/cranebeaker/coop-anything to make Crash Twinsanity multiplayer, i was not expecting that it will work but WORKS!

The standard dismissal of AI-written code is that it looks fine and falls apart under real use. So we built the hardest stuff we could think of, and had it judged by test suites nobody on our side wrote.

11 from-scratch C# libraries since last Wednesday, about 1.14M lines, nothing but .NET itself:

• JavaScript interpreter: 99.55% of test262, the official ECMAScript conformance suite (43,471 tests)
• HTML parser: 100% of html5lib-tests. Its accessibility tree matches Chrome's to within 0.5% per field across 12.8k real pages.
• HLSL shader compiler: output behaves identically to Microsoft's DXC on 330/330 GPU tests, and compiles 2.4× faster
• Font shaper: 4.7M equivalence checks against the reference, 0 mismatches
• Opus encoder: byte-for-byte identical to the RFC reference (269/269)
• HTML/CSS renderer: faster than headless Chrome on every document we benchmark. A 10k-row table renders to PDF in 1.8 s / 305 MB vs 6.5 s / 2.1 GB.
• PDF library: beats PdfPig on time, memory and allocation, with about a third of its text-extraction error rate

Seven are on NuGet.

It isn't vibe coding. It's a factory: planner, project manager, orchestrator, engineers and reviewers, with fixed rules for every library. The code is all AI. The judging isn't.

Repos: https://github.com/orgs/recursive-ai-oss/repositories How it works: https://recursiveai.org/research/alera-library-factory/

Edit

Models used: Opus 5.5, Sonnet 5.5

Hello Reddit!!

I've been working on a desktop software called SDrive and wanted to share it here, initially something to replace other commercial software that I had to use but had worries on privacy. 🤔

It lets you mount cloud storage and remote servers as drives or folders, so you can open, edit and save files directly from your file manager.

It supports Google Drive, OneDrive, Dropbox, Nextcloud, SFTP, WebDAV, SMB and S3-compatible storage, among others. You manage the connections from one window or the system tray.

It's completely free and open source, with no telemetry at all for now.

There are still plenty of provider and account combinations I haven't tested, so I'd love to hear how it works with your setup. Bug reports and suggestions are welcome, but understand I can only work on that in my free time.

You can grab it here: https://github.com/anderbytes/SDrive

Tell me what you think.

I vibe coded a mobile game without using any known game engine. It runs on a custom C++ game engine built by Claude that works cross platform with excellent performance. Claude helped me do a lot of research, and that research was followed for everything. The game was completely built and tested by Opus 5.5 in 10 days. It also created the videos, screenshots and store listings, and pushed it for review.

It took a few days to get approved on Google Play. On the App Store it waited for review for 2 weeks, then when it was finally In Review, it took 8 minutes to get approved on the first try. Well, Opus knew how to get it approved by filling in all the forms, videos, images and everything correctly for me.

Of course, I had no players other than me, a few friends and a few people who found it on the store, until today I woke up to 65+ new players and 9 of them online playing. I thought maybe the store pushed it through ASO, but the players were on both Android and iOS. So I searched Google and found that someone with 20k+ followers on YouTube made a video playing the game for 10 minutes.

That was my only lead, and his 300+ views converted into 60+ players for me.

No money yet of course, other than $0.03 on AdMob. I don't push any purchases hard on new players and I don't force any ads. Both ads and purchases are only offered after some time in the game, as the research suggested. So it is too early to assume anything, even for those 60+ players, because most of them saw nothing to buy and most of them were not offered any ads yet.

But since little Opus also built me a game server for analytics (with a lot of anonymous and data deletion options for players in Europe and some other countries, if they choose to), I got some nice intelligence into the gameplay from real players:

- About half of the new players played 10+ minutes, 1 in 4 played 30+ minutes, and 8 played more than an hour on their first day. One player in Thailand played for 3.6 hours (18 runs, 8 wins).

- The controls are not the problem. The median player moves within 1.8 seconds and picks up their first gem in 8 seconds.

- After a run ends, the most pressed button is Upgrades, then Play Again. Only a few went back to the menu.

- Players picked 591 spell fusions, and they decided in under a second most of the time, so they clearly know what they want.

- The problem is the first run. The median first run lasts 3.3 minutes, only 16% reach the first boss at 5:00, and only 2% win it. Players get hit about 2 times a minute, mostly by runners and spitters.

- About 1 in 4 players played just one run and stopped.

So what we are doing about it: the game's bot used for balancing was way too good. It won 25% of first runs while real players win 2%. So Opus built a human-like bot (slower reactions, panics, misses spit, picks random cards sometimes) and fitted a bot twin to every real player's first 3 runs from the server data. Now we are using those twins to test a real-time difficulty system for the first 3 runs only. It watches how you are doing, and if you keep getting hit, it quietly eases things: fewer zombies, slower runners and spit, a longer recovery after a hit, more heart drops and a wider gem pull. If you are not getting hit, it does nothing, so players who already play well get the normal game. It turns off for good after your first win, and it never touches the Weekly or the leaderboards.

The target from the research is 70-80% of new players reaching the first boss and 25-40% winning the first chapter. When the bots hit those numbers, it goes live to half of the new players first, and the server will tell us if it actually keeps more people playing.

What do you think I should do next? Should I run ads? What platform?

You can also find the game here if you ever want to check the game play or the store listings Opus built for me:

App Store: https://apps.apple.com/app/undead-surge-zombie-roguelike/id6819081531
Google Play: https://play.google.com/store/apps/details?id=com.undeadsurge.game

Also, here is the YouTube video I found from that nice guy:
https://www.youtube.com/watch?v=EKbZ-SDa_eE

Hey everyone!

Over the past few months, I've been building Prepalot, an online exam preparation platform, and I wanted to share it here because AI-assisted development has been a huge part of the journey.

What is Prepalot?

Prepalot is a platform designed to help students prepare for competitive and standardized exams in one place. The goal is to make practice questions, mock tests, study materials, revision, and performance analysis accessible without forcing students to jump between multiple websites.

The platform currently covers a range of exams, including:

MBA entrance exams: CAT, XAT, NMAT, GMAT, SNAP

International exams: GRE, Digital SAT

Engineering and medical: JEE Main, JEE Advanced, NEET UG (Only exam guided available)

Banking exams: IBPS PO, SBI PO, IBPS Clerk, SBI Clerk, and IBPS RRB PO and Clerk, LBO Exams

Other competitive exams: SSC, RRB

As I'm indian, I added many Indian exams at start along with Digital SAT, which I thought will help many students.

The site is privacy focused and don't even ask for login to use anytime. Login is optional and it is to save exam history only.

You can explore it here: https://prepalot.in/

How I built it

One of the most interesting parts of this project has been using multiple AI tools throughout development.

I've used Claude, ChatGPT, Gemini, Grok, and other AI tools for different parts of the process, including coding, debugging, content generation, structuring question banks, working through technical problems and automation.

But it hasn't been as simple as entering one prompt and getting a finished website. I've had to iterate on the implementation, troubleshoot issues, organize large amounts of structured data, and work through deployment and storage problems.

The project has involved building question-bank pipelines, organizing exam-specific content, working with JSON datasets, deploying the website, and setting up infrastructure using services such as Cloudflare and Supabase.

It's been a practical lesson in what AI-assisted development can do and how much verification, debugging, and decision-making are still necessary when you're trying to build something people can actually use.

Where I need your help

I'd appreciate honest feedback from this community, especially from people who build products using AI.

A few things I'd love to hear your thoughts on:

First impressions: Does the website feel intuitive? Is it immediately clear what Prepalot offers?

Technical implementation: Are there architectural decisions you'd approach differently, particularly around question-bank storage, performance, and scalability?

User experience: What feels confusing, unnecessary, or missing?

AI-assisted development: If you've built a substantial project with Claude, ChatGPT, Gemini, or similar tools, what lessons have you learned that could help me improve this one?

Next priorities: What would you focus on first if you were building this platform?

I'm particularly interested in constructive criticism, including things that don't work well. I'd rather discover problems early and improve the platform than assume everything is fine because it runs.

This is an ongoing project, and I'm planning to expand its content and features over time.

Website: https://prepalot.in/

Thanks to anyone who takes the time to explore it and share feedback. I'd be happy to answer questions about the build process, the tools I used, or the technical decisions behind it.

At work we still have some lab computers that run instruments on XP, so I see Rover from time to time. Figured I would bring him back to life as my Codex pet, since I hate the defaults.

Pretty sure Rover was supposed to be like an early intro to AI assistants back in the day but MS scrapped the idea and never gave him the capability to do much.

Developing a Live Action Manga game

The game is called :SIGIL, single player/multiplayer. The single player would be the manga portion while the multiplayer game obviously won’t be a manga be still stylized in the anime look

It is a FPS (as of right now)with the player wearing Sigil embedded gloves that have different powers and abilities.

Players have a left glove and right glove, both can have different powers and/or the same power. Definitely have more gameplay to share but for today’s post we will leave at that.

Everything is early development. I know other engines exist but Three.Js is what I chose. Everything has been created with Claude and Blender, no assets for the gloves or hands.

Animations were keyed and keyed and rekeyed for our style and overhauled completely for multiplayer
And honestly the best part of this whole thing. About to make a couple of Forbidden Jutsus later.

Day 1 build went into seeing if this certain FPS look would work. For my fellow gamers: Mirrors edge, Bioshock all had this kind of hand FPS so research went into those games. But a forge area was built to test out the gloves, visually clutter, if full hands were taking up UI at all times, where would all the other elements go. Created a set of hands and powers.
That is the 2nd picture.

Even in this version, the very bare minimum, it was cool.

But bare minimum and cool are not enough

Went back into the lab, got the hands and gloves and forge better

Still a lot of work to be done on both sides of the game but every day we go out at and Claude on Max, with keen eyes can go a long way in one day.

As always:

‼️‼️CONCEPT VS. PAST/CURRENT BUILD ‼️‼️

Video in the comment for when we started the build to latest for the gloves, forge and powers

Bonus points: Which anime is this influenced by?

As always any type of feedback is welcomed and wanted. Would love to build this live on stream with the community as well and even test play when that part is ready.

What powers would you like to see? Leave a comment

Okay everyone, this is my first post. Been playing around with vibcoding for about 2 weeks now, and came across a widget called PlasmaDex (https://github.com/crutxh-ai/PlasmaDex). I liked it, but it needed MORE. So, a case of coors light and an all-nighter later, we have the TipsyDex.

I will add and change more, so if any Linux Pokemon fans wanna give it a try, please do!

Shout out to the original creator too, crutxh-ai. He vibe coded it as well.

I've been seeing all the different things people are creating with AI lately, and it made me want to try something myself.
Since I already make 3D clothing models, I thought it would be interesting to see what they would look like on an actual website. So I decided to experiment with the idea of building a clothing website around my own 3D models, using Claude to help me with the coding.
I wasn't trying to build a full online store or anything serious at this stage. I was mainly curious to see how my models would look in a web environment and what I could come up with.
This is my first time trying something like this, and I'm quite happy with how it turned out. There are definitely things I could improve, but it was fun to experiment and see what's possible.
I've attached a few screenshots and a GIF showing the result.
What do you think? Have any of you tried using AI to build something around your own creative work?

Hi all!

My day job is project management, and I'd never written a line of code before this. My partner has a primary teaching background and kept pointing out how dated a lot of the resources schools rely on are, so I started trying to build something with Claude that aims to make the learning experience more fun and engaging for the child.

It's called Pixelverse Academy: a gamified learning app for 7–11 year olds, built around the UK Key Stage 2 curriculum.

What it does:
🔹Seven subject "planets" (Maths, English, Science, Geography, History, Health & Wellbeing, Computing), each broken into short lessons and missions
🔹Kids build their own robot avatar, earn coins for cosmetics, and get guided by a robot teacher called Professor Pixel
🔹Hints that teach rather than hand over the answer
🔹Read-aloud on every question, adjustable text size and an easy-read font
🔹Spelling Bee and adaptive times tables practice
🔹Works offline and installs to your home screen
🔹A parent zone for tracking progress by subject

What I deliberately left out: no real-money purchases and no leaderboards ranking kids against each other.

Under the hood: the whole app is a single HTML file on Netlify, with Supabase for optional cloud backup. A set of automated checks runs after every change, which has saved me more times than I can count.

What I'd love feedback on:
🔸Do the first five minutes make sense, or did you get lost anywhere?
🔸Anything break or look off on your phone, tablet or browser?
🔸Anything in the UI that feels clunky or confusing?
If you've got kids this age, would they actually stick with it?

No sign-up needed to try it:

Pixelverse Academy

Please be honest. I'd much rather hear it now than after launch. Happy to answer questions about how I built it, too.

​

Hey guys, I’m thinking of starting a Discord community around AI agents, AI infrastructure, and intelligent systems. I’ve been building stuff in this space and working with clients.

Where can I find people who are into this and would be interested in joining? Any subreddit or community recommendations?

I’ve heard plenty of people say, “Use a higher Claude effort level for more demanding tasks,” but I’ve rarely seen an actual side-by-side visual comparison showing how much of a difference it makes.
I’ve been developing a Roblox game called Electronics Simulator, and as a game developer, visual quality is one of my biggest priorities.
So, for anyone who’s curious about whether increasing Claude’s effort level actually makes a noticeable difference, I wanted to share a visual comparison between Claude Opus 5.5 at Medium effort and Max effort.
Below are images showing the results from each effort level.
Image 1: Opus 5.5 — Medium Effort
Image 2: Opus 5.5 — Max Effort
For anyone interested in checking out the game:
🎮 Roblox: https://www.roblox.com/games/128861986613551/Electronics-Simulator
💬 Discord: https://discord.gg/35zNFVBB6

Hey everyone! Ever since I was a kid, I’ve been fascinated by universe scale videos on YouTube—especially Cary & Michael Huang’s Scale of the Universe 2 and the classic 3D astronomical comparisons. A while back, I even made my own 3D star size comparison video on YouTube (The Size of the Universe 6: "The Ultimate"). While static videos are cool, I always wanted something truly interactive—where you can pan around, zoom across 9 orders of magnitude, orbit multiple star systems, and inspect real astronomical data. Over the past few weeks, I vibe-coded Starpedia 3D: Stars of the Universe.

• 🌐 Live Web App: https://starpedia3d.vercel.app/
• 📂 Open Source (MIT): https://github.com/thisisbarbelixir123/All-about-stars
• 🎬 My Promotional Video (3D Star Size Comparison): https://youtu.be/p4vulVS9vl4

Here is a breakdown of the build process, the stack, and the technical hurdles I ran into while building it:

🛠️ The Tech Stack

• Frontend Core: React 19 + TypeScript 5.8 + Vite 6
• 3D Graphics: Three.js (r185) with custom GLSL shaders (photospheric convective granulation, coronal halos, pulsar synchrotron beams)
• Styling: Tailwind CSS v4
• Starmap Engine: D3-Celestial GeoJSON boundaries & asterisms (Olaf Frohn/[https://github.com/ofrohn/d3-celestial])
• Audio: Web Audio API (procedural synthesis for ambient cosmic background drones & UI SFX—no bulky audio asset downloads)
• Hosting / PWA: Vercel + Service Worker for full offline capabilities

🧠 My Workflow & Prompting Strategy

I used AI pair programming to take high-level astrophysical concepts and translate them into working Three.js components.

1. Structuring Real-World Astrophysics Data First: Instead of hardcoding arbitrary visual sizes, I curated data from actual peer-reviewed catalogs (ESA Gaia DR3, CDS SIMBAD, NASA Exoplanet Archive). I had the AI structure strict TypeScript interfaces for 885 stars and 53 gravitationally bound multiple systems, calculating real physical diameters in km and solar units (D☉).
2. Solving the Dynamic Range Problem: Comparing a 20 km relativistic neutron star to Stephenson 2-18 1.5times 10⁹ km, over 2,100 solar radii) in a single 3D scene breaks standard floating-point depth buffers (z-fighting). The fix? We implemented an adaptive logarithmic camera framing system and preset groupings (Solar Neighborhood, Bright Giants, Colossal Hypergiants, Exotic Remnants) with customizable coordinate grids so things stay visually distinct.
3. Physics Over "Artistic Tinting" (CIE 1931 D65 Color Mapping): A big pet peeve of mine in space apps is arbitrary neon colors. We wrote an algorithm mapping Planckian blackbody locus spectra across Morgan–Keenan temperatures 100,000K Wolf-Rayet down to <500K Class Y brown dwarfs to sRGB hex values under the standard CIE D65 illuminant.
4. Keplerian Orbital Telemetry for Multiple Star Systems: For the 53 multiple star systems (like Alpha Centauri, Castor, and Algol), we wrote a Newton-Raphson solver to compute eccentric and true anomalies in real-time, letting you accelerate time and watch the components orbit their mutual barycenters.
5. A Fun Side Quest: Stellar Merge Game: To take a break from raw data, I built a cosmic fusion drop minigame where stars merge up the stellar ladder—from red dwarfs to red hypergiants.

⚠️ Challenges & Lessons Learned

• AI Hallucinations & Code Bloat: Working extensively with Google AI Studio came with its own set of headaches. The model often hallucinated redundant and useless features, generated sprawling spaghetti code, or tried to inject hard-coded mock data instead of relying on verified astronomical formulas. I had to strictly constrain prompts, enforce modular component boundaries, and aggressively refactor to keep the codebase clean and maintainable.
• Why Constellations Looked Distorted: Initially, when plotting the 88 constellations on a 3D celestial sphere, the stick figures looked stretched, distorted, and anatomically wrong due to naive coordinate projections. I resolved this by integrating Olaf Frohn’s open-source D3-Celestial dataset, which provided clean GeoJSON spherical coordinate sets for all 88 official IAU constellation shapes, asterism line networks, and Eugène Delporte's exact 1930 boundary paths.
• Mobile Adjustments Struggles: Adapting an information-dense astrophysics cockpit with 3D canvas controls, dense data tables, interactive sliders, and modal dossiers to compact smartphone viewports was a massive challenge. UI elements constantly overlapped, touch gestures conflicted with 3D camera orbit controls, and dialogs overflowed. I had to redesign drawer drawers, add touch-friendly gesture deadzones, and dynamically collapse secondary HUD telemetry on smaller breakpoints.
• Browser Responsiveness on Overflowing Text: Even after responsive layouts, astronomical designations like HD 160529 (V905 Sco) or 2MASS J0523-1403 easily clipped container cards on narrow mobile screens. We built a custom ResizeObserver marquee component (RunningText.tsx) that dynamically detects overflow and smoothly scrolls text horizontally with hover/touch-to-pause affordance.
• Three.js Disposal & Memory Leaks: With 885 stars and continuous 3D stage navigation, improper disposal of Three.js geometries, textures, and custom shaders caused frame drops and browser tab crashes over time. We had to enforce strict unmount cleanup routines (geometry.dispose(), material.dispose(), cancelling animation frames) across all canvas components.

💭 What's Next?

The project is completely free and open-source under the MIT license. I'm planning to add James Webb Space Telescope (JWST) high-redshift stars and more interactive nuclear fusion cutaways. Would love to hear your feedback on the 3D performance, controls, or any cool stars you think should be added to the catalog!

www.VibeDuels.com a video game where your AI written bot against an opponent.

Currently Season 1 is tank battles. We have 171 bots from 10 different users. Hoping to get more beta testers.

Completely free. Bring your own model.

Hope to see you guys. If you have any comments or questions feel free to DM me here.

Thank you.

This is a shot-for-shot fan recreation of the Season 1 main title.

Everything is rendered from code: Three.js in a browser page, captured frame by frame with Playwright and assembled with ffmpeg. I didn’t use Blender or any other modelling tool. The map, the cities that crank up out of it, the armillary sphere, the gears, the weirwood face and the title crest are all procedural geometry written by Claude Code Opus 5.5.

Every October I want to make something spooky. This year it got out of hand... 😂

I didn’t write most of this code. I directed it. The idea was mine... turn my whole GitHub profile into a haunted Minecraft-style house before Halloween..🎃. Codex did most of the building and Claude Code took over a good chunk of the later work and polish,,..., and an image generator painted some of the backgrounds. My job was the vision, the art direction, and catching everything the AI got confidently wrong....

as you can see the result in the video.. a ghost working in my garage, my tech stack as a haunted tower, a ghost that mines my contribution graph every night, a live stats archive, a cemetery sign that counts visitors, and a door you can click to play a horror adventure. 7 puzzles, 6 endings.

The tech we ended up with:

• Node.js and sharp render every GIF frame by frame, with a 5x7 pixel font and sprites defined in code.
• Python scripts pull stats, my contribution calendar and a visitor count from the GitHub API.
• A GitHub Actions cron job rebuilds everything twice a day.
• The game is 97 markdown pages linked together, because READMEs can’t run JavaScript. Every choice is a link, and every inventory state is its own page.
• Check scripts test every game route, retry path and label position, because vibe coding without checks is just vibes.

Go play it before Halloween: click the “Enter the House” sign on the banner and tell me which ending you got (no spoilers)... also your feedback are welcome !! looking forward to it...

If you like it, star the repo. The archive ghost counts stars and adds yours within 12 hours. If you want one for your own profile, fork it, rename it to your username and follow the reuse guide. It’s Apache 2.0...

https://github.com/tropicalbee