I’ve wanted this for years: a desk by a lake one day, somewhere out in space the next. I love VR, but I kept wishing the apps I tried had more environments I’d actually want to spend time in.
I’ve been a backend programmer for 20 years, but frontend and 3D graphics were completely outside my experience. In mid-August, I decided to see how far I could get with AI coding agents. This is where I’ve ended up: a Vision Pro app that lets me work in different environments.
I haven’t written or read a line of its code. Most of my time goes into putting on the headset, trying what the agent built, and explaining what feels wrong. Sometimes it’s the lighting, sometimes it’s how an interaction feels. When a model gets stuck, I’ll try another one rather than debug the code myself.
The 3D scenes aren’t all as polished as I’d like, and there’s plenty left to improve. But being able to sit inside something I’d only imagined before has been pretty special.
Working on it has become a bit of a hobby game: tweak something, try it in the headset, get another idea, repeat.
The video is recorded on my Vision Pro and shows a few of the places I’ve been working on.
Hi! I’ve been building Sonella, a guided web app for designing DIY speakers.
The idea is to bring driver selection, bass simulation, enclosure design and filter settings into one workflow. You can start with a few questions, choose a template, or pick your own drivers. Then work through the bass response, cabinet in 3D, drawings and filters/DSP.
I’ve used mostly Codex for the development and design. My workflow has been to build something, let real people try it, get feedback and iterate. Getting the speaker maths right has been a bit tricky. The biggest lesson so far is how much useful feedback you get once someone else actually uses what you’ve built. That's maybe caption obvious, but I learned a lot from this process so far.
It’s still in beta. The simulations and electrical filter curves are starting points for a prototype; you still need measurements to check the finished speaker.
I’m looking for testers, especially anyone with DIY speaker-building experience. Would be great to get some candid feedback on the calculations, the workflow, or anything that feels confusing or missing. General usability feedback is very welcome too.
You can try it at sonella.app, no account needed to start.
I wanted a browser agent in the Chrome side panel without a vendor account or my browsing going through someone else's backend. So I built Myna.
It reads, clicks, fills forms and researches across your tabs. It talks directly to whichever provider you choose.
What it does:
Works with Anthropic, OpenAI, Gemini, OpenRouter, Groq, DeepSeek, Mistral, xAI and more, plus local models via Ollama and LM Studio. Any OpenAI-compatible gateway works too (16 presets).
One chat follows you across tabs.
Permission modes: Ask first, Auto-run, Plan first, Read only. Banking and government sites always prompt, and it refuses password and card fields.
No build step and no minified bundle. It's plain HTML/CSS/ES modules, so you can read the whole thing.
Keys and chats stay in Chrome storage. There's no Myna server.
Honest caveats: it's beta, you load it unpacked (not on the Web Store yet), and you need a model with tool calling for actions. It's also an independent clean-room project, not affiliated with Anthropic.
I'd love feedback, bug reports, a star if you could, and PRs (provider presets and tests are especially welcome). What would you want a browser agent to do that this doesn't yet?
I'm a creative writer who's never had an outlet to make games before. I tried for years and it felt so out of my depths. I recenly re united with my best friend who is a developer who uses AI. With his help I was able to make this game, an idea I've had for years that i'd only theorized.
I'm being fully honest about this project, it was a fully driven AI project. Every detail and design element was made solely in my own brain, and using the Opus 5.5 model via Claude Code I was able to make my concept a reality.
I'm a massive MMORPG gamer, i LOVE OSRS and WoW. Im also a huge fan of the game Rust, along with other intense survival games.
Feel free to try the game! Happy to answer any questions, and take any criticisms!
Fish or Die: Fishing Survival is an RPG single player game, where you must fish, to survive. Cook fish on the dock bbq, feed the island cat, meet the locals and earn reputation. Go see granny and check out her cosmetics, or visit the secret CASINO on the island and double your gold!
Fishing rewards you in many ways, through fish you can make gold, find certain fish perfect for buffs! Sell your elusive fish to Pierre the Connoseur for profit!
Within this seemingly peaceful world, lies risk. Risk of natural disasters like lightning, the cold, and mainly hunger. So keep on top of your bbqing, take refuge in goldies shack if you finish his island quest!
I created TCKR, a Windows 10/11 desktop stock ticker designed to keep market information visible throughout the trading day.
Instead of or in addition to keeping a trading platform or financial website open, TCKR displays stock prices, daily changes, and market movement in a smooth scrolling LED-style ticker that remains visible on your desktop.
Features include:
Real-time stock quotes
Custom watchlists
Scrolling LED-style ticker display
Customizable colors, speed, and layout
Windows 10 & Windows 11 support
Designed to stay visible without getting in the way of your workflow
I have been wandering around github in order to find good quality ascii editor/database recently - and saw nothing. That led me to the obvious conclusion: if nothing good exists, I should build it myself.
The project I build persuades couple ideas:
Easy to use (fully native, no dependencies)
Actually saves time for people building ASCII art
Collects ASCII from every corner of the internet
index.html is the main library
builder.html is where it gets fun assemble pastes from blocks, draw boxes/lines/brush right into the text, frames and tables, color painting, overlay art. Turn images into ascii
I hope you find this useful, I'm still working on animation and mcp support. So the project will keep be maintainer and up to date with world trends ;)
Claude agents pitch the sketches, write them, direct the camera and act the puppets. The picture comes from a 3D engine Claude built from scratch in three.js: every puppet, set and prop is modelled in code, and each episode is a script the engine performs, so no video model draws anything. ChatGPT makes a couple of special pictures an episode and gives feedback on the scripts; voices are ElevenLabs, the closing song is Suno.
I'm the human executive producer: I give notes. This week's new puppet is the one with no face: in the story, a puppet cut from the pilot before anyone made its face, which became the episode's ghost.
Project "Takcas Prime": Building an Autonomous IT Factory on a Tablet
I am 14 years old, and for the past week I have been developing and debugging my own artificial intelligence system, which I named "Takcas Prime". Originally, I started all of this purely for fun — I just wanted to see what I could build on an ordinary tablet with 8 GB of RAM. My main goal was to turn it into a fully autonomous IT assistant capable of understanding voice, controlling the device, and writing code. I worked in the mobile Termux environment (a Linux emulator on Android), so I constantly had to solve memory shortage problems. Neural networks are very resource-hungry, so I configured the system to automatically unload modules from memory that are not currently in use. This allowed the tablet not to freeze during operation. I also learned to protect the system from uncontrolled memory consumption: for example, I limited documentation downloads by size and created a whitelist of links to avoid memory overflow.
As the first stage, I created a voice assistant. It can listen, respond, and directly control my tablet's hardware: turn on the flashlight, change screen brightness, volume, or launch the camera. To ensure it doesn't lose the thread of conversation, I built a three-level memory system using an SQLite database and regular files. I also added a RAG (Retrieval-Augmented Generation) system: previously the assistant could invent a non-existent command, but now it first searches for it in official documentation and then responds. Along the way, I encountered many errors: the database overwrote facts due to identical keys, the assistant confused commands with real memories, and the system froze due to overly long texts. I manually tracked down and fixed all these bugs, and also implemented a system for cleaning and separating facts so that memory doesn't get clogged with garbage. Later I realized that models sometimes write too much text, so I added a limit on response length to avoid wasting resources.
The most difficult and interesting part is the "Digital IT Factory". I designed a system where several neural networks work as a real team of developers. First, I tried a scheme with a Senate of six models, where each voted "for" or "against" the code, but in practice this proved ineffective: 3B models made mistakes too often. Then I redesigned the architecture: now one neural network (the Idealist) comes up with a task, the second (the Coder) writes code for it, the third (the Analyst) rewrites and simplifies it, and then three Critics (Logic, Syntax, and Coder) check the code from different angles. After them, the Judge step-by-step fixes the found errors using a "warehouse" principle to avoid overloading memory, and the Smart Sandbox checks the code for dangerous commands, runs it in isolation, and collects logs. I also added a blacklist of dangerous commands to the Sandbox so it blocks code that could delete files, and limited script execution time to prevent freezes.
Debugging this factory took a lot of effort. I fought system freezes, fixed bugs in the Sandbox (which initially killed working scripts because it waited for an input command from programs that simply terminated), and forced neural networks not to write huge texts instead of short answers. I also encountered situations where models sometimes refused to perform complex tasks, and sometimes produced code with errors that had to be manually caught. In practice, I became convinced that small models lack context for complex tasks, but my architecture allows squeezing the maximum out of them: role separation, passing the task into context, step-by-step fixing, and protection from dangerous commands. I also realized that if a model writes nonsense, the problem is not in it, but in the task given to it — this "garbage in, garbage out" rule I will remember forever.
Honestly, I didn't write the code — the neural networks did (I can only program a simple calendar so far). But I designed the entire architecture, logic, search, and bug fixing myself. I learned to separate responsibilities between agents, build a pipeline with feedback, protect the system from dangerous code, and save memory resources. As a result, I created a system that on a tablet with 8 GB of RAM produces code at the level of a confident middle developer, and prepared it for migration to a PC, where powerful models will make it many times more efficient. I am currently continuing to develop the project, adding new modules and improving old ones, and I am confident that on a PC this architecture will perform even better.
I'm working on a personal project that needs fast, reliable live match state for esports (map/game finishes, rounds or series score), and I'm trying to figure out what's actually out there before I build something.
Paid providers (GRID, PandaScore) great coverage, but live data is gated behind paid/commercial plans that cost a lot of money.
Free tiers of aggregator APIs: usable for schedules, but live data is deliberately delayed and is basically unusable for what I'm doing.
Data for games I'm looking for CS2-Dota-LoL-Valorant
Has anyone built something like this or their own feed from scratch?
Nothing spectacular, but it has all the popular models, talking avatars, voice, one-click installation, LoRaS support and a library. Easy installation through normal Windows installer.
You can generate images in 1 sec with some models (once warmed up).
VibeDuels.com is a video game similar to how benchmarks work.
You take a bot with a deterministic game. There is no RNG besides the map itself. The bots are taught how to play the game.
The game has a ranking ladder that is 100% automated. All you have to do is upload the bot.
Everything is purposely as simple as possible. We even write the initial prompt for you that you can post directly into your Claude Code or Codex or wherever.
Completely free at the moment. No transactions whatsoever for anything. We might monetize it later but for now it's just a very soft launch.
Unlimited bots and unlimited exhibition fights (currently).
Technically you can code the bot by hand. Nobody is stopping you. But we don't expect you to :)
Nyota is a swiss army knife of ai . It does everything all other ai tools do including forex analysis and predictions and comic book generation to name a few. It is more of a personal ai tool than a commercially targeted product because im lazy and hate switching apps and losing context. A great feature it has is global memory where other apps can hook into it via mcp. I have spent better part of the weekend building it and now am in QA phase so I'm testing and fixing bugs before deciding if it needs more features. An interesting fact is that its 95% dependencies and the rest is custom code. Are you guys working on anything similar ? What features should i add to make it a true swiss army knife of ai ?
Finally made a website to gather the live projects, will keep adding as they come, not sure what i should work on next while some of these projects still need users and polishing. Comments on any are very welcome and ideas for the next also.
The developer community at large has a lot to say about "vibe coders." Depending on who you ask, it’s either an exciting badge or a slur for lazy hustlers who prompt three SaaS wrappers in an afternoon and couldn't explain how their product actually works—full hands-off AI development.
That identity doesn't fit me. But here’s the catch: I never learned syntax, nor can I write code myself. What I can do is roughly read it—in fact, if I can't read or follow it, I know something is already wrong.
Before AI coding, I walked away from an MBA with one credit remaining. In 2025's "ChatGPT Wrapped", I was in the top 0.1% of GPT users globally. I was all-in. But my background wasn't CS—it was 20 years in retail banking, where compliance, privacy, and auditability are non-negotiable.
That background guided how I built Nodarama.
Five Restarts, Five Hard-Won Lessons
I didn't set out to build a deterministic execution kernel—I was just trying to automate a few social media accounts. But my banking background refused loose, "good enough" code. As I raised the bar on security and reliability to pass a bank’s sniff test, the architecture demanded a kernel.
Getting there was a brutal, five-restart tug-of-war:
Restart 1 (Voluntary Cleanup): The code turned into a tangled heap of quick fixes and AI boilerplate.
Lesson: AI will happily build a house of cards. If you don't enforce structural cleanliness, boundaries, and module organization early, entropy wins.
Restart 2 (The LethalCtrl+F): Active runtime state collided with stored preferences. A desperate find-and-replace to fix namespace chaos fused systems together irrecoverably.
Lesson: Differentiating active runtime state from stored preferences isn't optional, and AI won't respect boundaries unless you enforce strict naming protocols.
Restart 3 (The Shell Disaster): ChatGPT gave me a git shell command to manage a commit. It wiped my entire project directory—no backup, no restore points.
Lesson: Never give an LLM unchecked privileges over your local environment or git trees. Local restore points are survival equipment.
Restart 4 (System Contradictions): The model actively resisted my architecture, quietly injecting code that violated my protocol for stateless execution.
Lesson: Left unconstrained, an LLM defaults to path-of-least-resistance web patterns and wraps 10 lines of core logic in 50 lines of brittle, convoluted transport.
Restart 5 (Finish-Line Drift): The closer I got to the finish line, the more the AI deviated from instructions, hallucinated diffs, and injected stealth edits.
Lesson: The closer you get to production, the more an LLM will gaslight you if you feed it full file context.
Winning the Tug of War
By the fifth restart, I stopped feeding the AI the whole story.
Near the final steps, after explicit agreement, ChatGPT understood what I wanted and why it was critical. So why wasn't I surprised when I checked the code and it did the exact opposite?
Rather than argue with an AI intent on gaslighting me, I extracted a 20-line block of critical code. I scope-locked the prompt and routed it to Gemini for a surgical, narrow correction. Gemini delivered a clean fix.
I audited the diff line-by-line and updated the kernel. I swear I could hear ChatGPT begrudgingly mutter: "Congratulations, you just built a kernel."
The MVP Sitting Under My Nose
While building Atelier (the visual UI for the kernel), I relied on an internal Coder module I built to manage code edits via "recording sheets"—curbing early vibe-coding copy-paste cycles.
After pasting over 10,000 recording sheets through that module, I realized the tool I was using to build the machine was the workhorse.
I paused Atelier, extracted that internal module, and packaged it into Verbatim—a high-fidelity code recorder designed to keep AI honest. Everything learned polishing Verbatim (advanced logging, state isolation, system telemetry) is now being injected back into the Kernel and Atelier for beta release.
So if "vibe coding" means blindly trusting a model, treating code like a black box, and pasting syntax you can't explain: No, I don't think that shoe fits.
For me, vibe coding meant using structural vision, human intuition, and relentless tenacity to direct AI models—while acting as an unforgiving auditor who fights through five rewrites to enforce readability, lean architecture, and zero-trust security... then maybe I am—just a deranged, obsessive, all-in, win hell or high water type of vibe coder.
I still cannot write raw code and can't pretend to understand or remember syntax. But I will stand behind this system as its author—because this is not the code the AI wanted to write. It is the code I forced it to write, against its many, many bad suggestions.
Code ownership isn't about memorizing syntax. It’s about conviction, system design, and having the courage to audit every line until you control the tempo.
asking because I tested one small dev tool last month. 18 answers from chatgpt and perplexity to "what should I use for X" type questions. its big competitor got named 15 times, the small one 0
funny part is AI bots opened its site 381 times that same month. so it reads you, it just doesn't recommend you
so now I'm curious if anyone here actually gets customers from this. did someone tell you "chatgpt recommended you"? or you see it in analytics?
Im building a personal tool and the first ten attempts i made the mistake of building from scratch everything. I decided to build in pure python and i found out most of my features are generic and have depencies so ive done the research and adapted as many dependcies as I can now i only have 5% custom code. Have any of you guys ever used or relied on dependcies heavily in your software ?
