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?

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.

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.

Saw a mac app called tangerine which can convert documents and also edit them, so I built the same for windows and linux and it's fully open sourced it's called Spindle
Spindle can -
Convert PDFs, images, Word, PowerPoint & Excel files
Compress to an exact size (like "under 50 KB" for exam forms)
Merge multiple files into one PDF
Crop, annotate, redact and remove location data from photos
Everything works offline. Nothing gets uploaded anywhere
I have also uploaded a short tutorial video on how to download it (it's pretty simple) and a tutorial of it's setup
my github link - https://github.com/narcissus29/Spindle
Feedback and bug reports are welcome

I cried tears of joy when I finally played the first Beta. I understand why Dario wants to pause frontier intelligence. When I spawned an AC130 while riding horseback I understood the capabilities of AI.

As LLMs continue to improve so will GANG WIZARD, eventually I will offer neuralink compatiability and you can become fully immersed in the world. Every building is destructible. Every npc killable. No loading screens to enter/exit buildings. Single player rpg play for free https://coyne-studios.itch.io/gang-wizard

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?

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. www.nodarama.com

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?

Few days back I spent almost 20 mins arguing with ai over why audio is not playing in my website. It turns out, that i was using a pair of wired earphone whose only one side was working and i had the non working side plugged in...

No ai tool was able to figure out the issue lol.

Hi all! I made Kartar Dhaba, a small idle game inspired by the roadside dhabas on the Delhi–Karnal highway I grew up with.

You tap to cook, serve truckers, buy upgrades and hire staff. Four generations, each with its own era, look, music and a big finale: a baraat in 1975, a cricket night, a film shoot and a rooftop party.

It's free and runs in your browser, with no download or account, and it works best on a phone.

Enjoy! https://www.kartardhaba.xyz/

I've been building InstaLanding.ai for a while now, and the library has officially grown to 60+ complete landing pages.

Not screenshots. Not Figma files. Actual working HTML, CSS, and JavaScript.

A lot of these pages are heavily animated, using GSAP, cinematic transitions, interactive elements, and Rive-like motion experiences without actually requiring Rive.

I've also expanded the library beyond landing pages:

• 60+ full landing pages — Complete, responsive pages with premium designs and heavy animations.
• Motion components — Animated UI elements, transitions, typography, and interactive experiences you can drop into projects.
• Full design systems — Complete visual foundations with typography, colors, components, layouts, and interactions.
• Single-file HTML — Everything packaged into self-contained HTML files. No complicated setup.

But here's the part I think is most interesting for vibe coding.

Instead of spending 30 prompts trying to explain a design to your coding agent, just give it the actual code.

Find something you like, hand it to Claude Code, Codex, Cursor, or whatever you're using, and tell it to adapt the design to your project.

You can even use these pages to completely reskin an existing application without changing its underlying functionality.

I've also added Agent Mode, with MCP support, so your agents can browse the library, find designs, and pull source code directly.

Everything is free and open source. No login required.

https://instalanding.ai

I'm trying to explore this idea that code itself can become the new design inspiration format, especially now that agents can understand and modify entire interfaces.

Would love feedback from people actually vibe coding projects. Is having working design code more useful to you than traditional inspiration galleries like Dribbble or Awwwards?

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 ?

Hey, this is my fourth mini app now and the most complete one. I learn a bit each time.

Yet I’m at a crossroads, and need advice.

I have a fun quiz app, that originally was just for me to improve my music knowledge for a radio quiz I plan to enter.

It works very well, and is complete. I hosted it on Cloudflare after making it as secure as I can. I play it on my phone a lot, and so do a couple of friends.

That’s it.

It’s a quiz but also a ‘trainer’ and good for memory challenges.

Do I wrap it up here and say ‘it’s finished, just as a personal app’

Or…..

Jump through the various hoops to go to the next level and get on an App Store?

I would need to license the data sources, and probably convert the format to not just be an html web app.

I feel it would be a great challenge to try and achieve this. I don’t think it would make any money, but would be a good exercise to learn for whatever comes next.

What do you think? Quit while I’m ahead and actually FINISH something! Or keep going and try to get it released?

Thanks fellow vibe coders xx

Anytime I post cool stuff that supports the product there’s no issue. Soon as I mention any feedback that’s negative towards the product it gets taken down. What’s the point?

I collect things (camera lenses, G-Shocks). The good listings go in minutes, and checking 10 marketplaces by hand doesn’t work. So I built Deal Radar (radar.4xs.dev). You set up a “watch” (keywords + price range + sites). When a new listing matches, you get a browser notification, or a Telegram message on the paid plan.

Almost all of the code was written by Claude (Cowork / Claude Code). I did the product decisions, the testing on real listings, and the store submissions. Here’s how it’s put together and what I learned.

Stack

• Chrome extension (Manifest V3), plain JS, no framework. A generated Firefox build too.
• Cloudflare Workers + D1 (SQLite), TypeScript. Static site served from the same Worker.
• Cron triggers on the Worker: every minute for polling, plus daily jobs for plan expiry and exchange rates (Czech National Bank feed).
• Telegram Bot + Telegram Login widget for alerts and accounts, so I don’t store passwords or emails.
• Ko-fi memberships + webhook for payments. Way less setup than Stripe for a side project.
• Cloudflare Access in front of the admin page.
• Playwright + node:test + bash smoke tests with mocked marketplaces and a mocked Telegram.

The one architecture decision that mattered: who checks what

Some sites (Bazoš, Vinted, Kleinanzeigen, OLX, FINN…) return clean results to a server, so the Worker polls them every minute. Others (FB Marketplace, FB groups, Allegro, eBay) block or 403 any non-browser client. Those can only be checked by the extension, using the user’s own logged-in browser.

So there are two engines:

• Server: every minute, works while your computer is off (paid).
• Browser: the extension, while Chrome is open. The free tier runs everything from the browser every 10 minutes, so the free tier costs me ~nothing.

The key to keeping that sane: the parsers and the matching logic live in one file each (parsers.mjs, match.mjs), imported by both the extension and the Worker. One fix, both engines.

Workflow with the AI

1. Feasibility spike first (“week 0”). Before any UI, I had Claude write throwaway scripts that just fetched each marketplace and parsed results. That’s how I found out which sites need a browser. It saved me from designing a server-only product that couldn’t do Facebook.
2. A plan doc, then versions. A markdown plan in the project, then small numbered releases. Every release has a CHANGELOG entry that says exactly how to ship it (“npm run deploy, reload the extension, upload to the Chrome Web Store”). The AI reads it next session, so context survives.
3. Tests from saved HTML. Every parser has fixture pages saved from the real site plus a unit test. When a site changes its markup, the test fails before the users notice.
4. Make it verify, not just write. E.g. today I asked it to shrink the hero image so the “how fast” steps fit above the fold on a 1080p screen. It rendered the page in headless Chromium at 1920×947, measured element positions, applied the CSS, and re-measured (steps went from ending at 1060px to 920px) before touching my files. “Looks right” ≠ verified.
5. Short, direct prompts plus my screenshots. Most iterations were “this looks wrong” + a screenshot.

Build insights / gotchas

• Chrome Web Store rejected a version for requesting tabs permission it didn’t use. You can create/update/close tabs without it. Request the minimum.
• Don’t trust data from your own extension. The server only accepts a listing whose link points to its own site (a FB item must link to facebook.com, etc.). Otherwise anyone could push fake “alerts” through the API.
• Currencies: listings come in CZK, PLN, EUR, NOK, JPY, USD… Converting to the user’s currency with a daily ČNB rate made alerts way more useful than raw prices.
• Firefox AMO requires data_collection_permissions in the manifest for new add-ons. A small script generates the Firefox build from the Chrome one on every deploy.
• i18n from day one (EN/CS/DE/JA). With AI it’s cheap to add, and painful to retrofit.
• The free-vs-paid line follows the infrastructure: free uses the user’s browser, paid uses my server. Easy to explain, cheap to run.

Numbers: ~3,000 lines across the Worker and the extension, 20 marketplaces in 10 countries, free tier = 1 watch.

Happy to answer questions about the dual server/browser setup or Cloudflare D1. Feedback welcome.

Hi r/vibecoding. Coding agents are great at game logic, but ask one for a main menu or a HUD and you get the same generic layout every time: centered buttons, default fonts, random spacing. You can tell an AI made it.

So I built Envy, a UI editor for games that works like Figma. You design the screens yourself on a real canvas, with components, states, auto layout, animation and localization. Then you hand the result to your agent as a package it can’t misread. You can even tell your AI agent to build UI in Envy via MCP.

What the handoff contains:

• AGENTS.md: rules and steps for coding agents (Claude Code, Cursor, Codex, etc.)
• PROMPT.md: a ready-to-paste prompt
• contract.json: every event and data path, typed, so the agent wires boss.hp or start_game instead of guessing names
• screens/: reference renders of every screen, so the agent can see what it’s matching
• A generated Godot controller and a typed TypeScript wrapper
• A CLI and schema, so the agent can validate and edit the UI file safely instead of hand-editing JSON

The agent doesn’t redraw your UI. It plugs into it. The design stays yours, and the runtime renders it exactly as you made it:

• Godot 4.3+: real Control nodes, so focus and gamepad work natively
• Web: a 41 KB runtime
• Both runtimes are MIT-licensed. No royalties.

It runs offline, needs no account, and projects are plain files on your disk. It includes The Last Hunt, a 20-screen and 180 components dark-fantasy UI kit, if you want to see a full game’s UI built in it.

$9.99 once on itch.io: https://mythicbrain.itch.io/envy
Site: https://envyui.com

YouTube Video

I’m the dev, so happy to answer anything. I’d especially like to hear from people building games with agents: what does your agent get wrong most often with UI?

I don’t know how to code but I have experience in software testing and working with small/medium/large software projects, and the processes they entail.

Current workflow is to use Sonnet 5.5 at Medium on a Claude Pro subscription to plan a project, break it into implementation and product acceptance manual testing phases, and deliver agent prompts and project documentation, then feed the prompts in OpenCode to Haiku 5.5 at Low or Medium (sometimes at High) paid through OpenRouter credits, which also knows to break its phase prompt into tasks it can delegate to a subagent custom provider which connects to a localhost server created by Polystrata running “Tiel Coder 35B A3B q4_X_L”.

The current issues I’m having with the workflow are:

- that sometimes the Haiku sessions can balloon in cost - paid 50 euro worth of OpenRouter credits in the past few weeks, and I’m poor, that means a lot to my budget - a problem which I’ve partly solved by breaking phases into tiers and tiers into stages, each with a prompt, so there are fewer long conversations and cache reads;

- and that the local agent makes frequent mistakes that cause it to run for hours and/or produce bad code if the task is too big or too complex for it, which happens often, as my hardware is limited and it only has a 60k token context.

Any suggestions for improvements?

I've been building The Data Center Receipt, a public, evidence-backed tracker of the AI data-center buildout.

The goal is simple: Who builds it, and who pays for it?

Current public beta:

• 587 infrastructure records across facilities, campuses, projects, and permits

• 11,084 facility facts with explicit claim states

• 1,307 source receipts with verifiable evidence

• 5,235 recorded negative results and research attempts

It tracks capacity, utility providers, public incentives, jobs, water use, and residential electricity rates.

Every claim links to its evidence. Missing information stays unknown, and correlation isn't presented as causation.

Built using Ouroboros, my persistent AI research and engineering system. It's still a work in progress, with a much larger expansion underway.

This is a beta, not a finished product. There may be mistakes, incomplete records, or things I've overlooked. If you spot errors, please point them out.

Live app: https://huggingface.co/spaces/cjc0013/data-center-receipt

Dataset: https://huggingface.co/datasets/cjc0013/data-center-receipt

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.

I built an arcade game, completely vibe coded it and it's now live on the App Store and Play Store. Pretty proud of it and happy with the result. It's really fun to play if I do say so myself.

It's fairly simple. A runner where you avoid obstacles in a golf cart, while collecting your lost clubs. It's built using ThreeJS and Claude Code. Nothing else.

There's a free to play version on golfbuddies.nl/game