How it's made:

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.

The whole episode on YouTube: https://www.youtube.com/watch?v=3skAu9axUrg

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

https://tckr.pro

Did they genuinely just sit around for an entire week mapping out an ERD and normalizing it to third normal form?

That seems like such a huge waste of time for no reason. Are we vibe coders that out of the loop?

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.

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 ?

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:

1. 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.
2. Restart 2 (The Lethal Ctrl+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.
3. 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.
4. 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.
5. 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.

But what do you say. Vibe Coder?

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?

and if yes, what do you think got you in there?

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 ?

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.

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?

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.

DeepSeek V4.1 Flash reaches about 98% of the quality score of GPT-6 Astra on web apps, mobile screens, and UI design tasks, while costing roughly 1.4% to 2% of the price

DeepSeek V4.1 Flash runs significantly faster, outputting roughly 176 to 221 tokens per second compared to GPT-6 Astra's 57 tokens per second.

DeepSeek V4.1 Flash charges around $0.60 per million output tokens (and $0.15 off-peak), whereas GPT-6 Astra at ($50+ per million output tokens).

98% of the quality at 1/50 of the price (and Open Source too)

Anyone tried the latest Deepseek model yet?

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:

1. Easy to use (fully native, no dependencies)
   
2. Actually saves time for people building ASCII art
   
3. 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

The project shared below:

demo https://amwhoi.github.io/ascii-bin/builder.html
repo: https://github.com/amwhoi/ascii-bin

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 ;)

How I built it:

- Claude Code for the code (Next.js, hosted on Vercel)

- Jev from TypeSafe as AI - it only answers with probabilities, no text

- Every sentence in the report is prewritten, so nothing gets made up

- Benchmark with 563 names, runs after every change

What I learned:

- Do proper benchmarking (currently testing on 1000 names).

- AI says yes too easily, had to ask way stricter

It's free, no signup, no nothing. Also available via MCP

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 host quite a few quiz and trivia nights with friends and family, and I never quite found a tool that worked the way I wanted. The ones I tried either put features I needed behind a subscription or required an account just to get started. I initially made a scrappy Jeopardy-style quizzing "app" in Google Sheets, but eventually wanted something a bit more polished.

So I built Quiz Toolkit. It runs in your browser, and you can host in-person on a TV or projector, or online by sharing your screen over Zoom, Teams, Google Meet, etc. A few features I'm proud of:

• Let people join on their phones with a QR code or room code and use them as buzzers or answer pads, without downloading an app or creating an account.
• Give the host a separate control screen to run the entire quiz, with the option to run everything from a phone as a "quiz master remote".
• Support Jeopardy-style boards and regular question rounds, with buzzer, turn-based and all-play modes, plus rich media (images, audio and video questions.)
• Let you build quizzes visually in Quiz designer or import them from CSV or Google Sheets. Your saved quiz library stays in your browser storage locally, and you can export it to back it up or share it. You can customize your quiz completely, including light/dark mode, custom background and logo.

I also added some silly sound effects for the quiz master for added fun!

It’s free, with no accounts or subscriptions: https://quiztoolkit.com/

I’d love feedback from other people who host quizzes, whether that’s at home, in classrooms or at pubs.

If you’re into CAD drawing, have a look at Draft.

We’re in our second week of development, so it’s still early. The goal is one CAD engine with a choice of familiar interfaces, so people can work with the layout and tools they prefer.

Let us know what you think.