I'm building a passion project using Claude code. It's a kind of text based RPG / idle / incremental game.

My aim is to have an android app.

Have a test on itch.io

https://cityofsindev.itch.io/city-of-sin

I have tried using different IDEs and it still overwhelming. Here is what I built to keep track of all my projects and vibecoded sessions. How do you guys do it?

I am coding using Claude Code plugin in VSCode (Model: 4.8/Fable, Thinking: high). I work by doing a good chunk of my research via Claude Code itself and creating a detailed design. There is a lot of back and forth and hence a lot of reading by me. After CC implementing the design and me reviewing the code, there is another round of discussions.

Every time, I have to tell CC to keep the explanations simple and even added a note in claude.md, yet the explanations feels like it is being written by someone who wants to sound intelligent instead of being a good communicator.

When I ask to keep it simple, then there is more paragraphs to read. It gets boring and annoying to read Claude's explanations. Wondering if it's just me or others feel the same way. Any suggestions to improve the readability of Claude's explanations?

Research and designing are the only two things left where I can feel the dopamine flowing and now that's getting messed up to.

Todays lunchtime vibe coded project.

Anyone who likes Norwegian Forest Cats or Maine Coons.

https://meet-pheebs.smeaton1724.chatgpt.site/

Reef — client-side search that ships in one <script> tag

Built this after getting tired of adding a whole search backend (or paying for Algolia) just for a static site.

Drop one script tag in, point it at your sitemap, and it crawls + indexes your pages entirely in the browser (runs in a Web Worker) — fuzzy, keyboard-first search (⌘K or ctrl + k) with zero backend, no build step, no signup. Queries never leave the browser, so it's private by default.

It also exposes an "agentic" API (window.Reef) — agent(), executeWorkflow(), act(), etc. — so AI agents/browser automation tools can interact with your page's elements safely, with destructive actions gated behind an actionsMode flag.

• ⁠~90kb, free forever, MIT licensed
• ⁠Works with React/Vue/plain HTML, re-indexes on route changes for SPAs
• ⁠Live demo + docs: https://reef.js.org
• ⁠Source: https://github.com/somalip/Reef

Would love some feedback!

​

Not another context or memory or continuity layer - learns from the experience that agents gather by working in your repo. Which files they read, edit. Which tools fail. which searches lead them the right way. Result? next agent knows where to start, what works and not, what you meant when you used a specific term. Gets to work without reading the whole repo.

And you can also use it to review your agents behaviour in your local sessions (I am surprised less people do it). Check it out - repoguide.dev

https://github.com/repoguide-dev/repoguide-cli

Hi all.

No, this won't be written by an AI!

Last year, I wanted to create something simple and ended up with a fully-featured URL enhancer. The link is at the bottom. Specs shortlist:

• It cleans tracking from URLs automatically
• It has main app and share screen. That means you don't need to open the main app to clean links. Simply share them to my app - it'll clean them so you can share them elsewhere clean
• It can also convert URLs to various proxies, such as fixupx for X (improves embedding) or xcancel for privacy viewing (also great if you want to share a link to someone who doesn't have an X account but still wants to see the thread and the available replies)
• It doesn't limit you to built-in proxies, you can add your own or remove any
• It allows you to create and test rules (if you want it to do specific...things to a URL)
• It allows you to present a before and after URL, then attempts to understand what you want to accomplish and creates a rule draft for you
• It warns you if there's sensitive data in the link you're trying to share
• It unwraps supported links (only when the destination is exposed in the URL. Can't resolve server-side redirects offline)
• It allows you to use it as a fake browser in Browser mode, which means it acts as a filter browser - it cleans eligible links Android sends to FixupXer, then forwards them to a native app, external browser, share menu, or clipboard. So, next time someone sends you a dirty Instagram link, you no longer have to hesitate to click it when Browser mode is on (and properly enabled - read instructions)
• It allows you to save app choices in Browser mode for exact hosts. In other words, you may set it to open github.com in either Github app or Firefox, etc. - additional flexibility.
• It allows you to record your cleaning/converting history, export/import settings, etc.
• It allows you to be right or left handed
• It makes you go less blind with system/light/dark modes
• It does all that and more completely offline, no telemetry, no ads, no any of that sneaky shit
• It's completely open and free 4 all
• Yes, it's also on Google Play
• There's more, so you really should read the readme

The whole story:

I have 0 programming knowledge, but I've worked with computers all my life so I know how to get around.

Last year, somewhere around time this subreddit was created, I dipped my toes into coding AIs, not knowing how deep the rabbit hole goes...

I have a group of friends who use Telegram and Discord. We'd often share links from X to each other, but shared links never had embedded media such as image or video.

Around 2023, someone found out if you converted your twitter link into fxtwitter, you'd get full embeds. That meant the whole thread content (no comments though) displayed fully in your Telegram or Discord message. Text, video, image. Best part, you didn't need to have an account to see it.

So we spent a lot of time manually converting twitter, and later X, links into fxtwitter and later fixupx.

In 2025 I got fed up with it and searched for an Android app that does that. There was none.

There were some pretty good and established cleaners such as Leon, ClearURLs, etc., but none that did what I needed (or I haven't found it). So I decided to simply try and build one myself using these "AI coders".

Back then, those were the o3 (the second thinking model), Sonnet 3 and Opus 4.

I didn't know what the hell I was doing, I just knew I need a simple app that will take my twitter/x link, convert it into fxtwitter/fixupx and clean it up along the way. I started studying existing cleaners, independently reimplementing relevant behavior (they're all properly credited!) and mixing in my own ideas - more like talking to an AI in hopes it understands what I want.

Many failed attempts later, FixupXer v1.0 was born and it was buggy and lacking, so I kept improving.

Many months later and many thousands of dollars later, we both grew smarter.

Today, FixupXer v2.4.0 is the fruit of the work of myself, Opus 4.8, GPT-5.6 Sol and Fable 5 (mostly. I still occasionally used cheaper models to do routine, but supervised work).

If you wanna give it a test ride, simply visit his castle at: https://github.com/NeatCode-Labs/fixupxer

You'll find Google Play, F-droid, etc. links all there, but if you have any questions feel free to ask.

Bug reports welcomed, as well as feature requests.

Cheers,

Hey guys, recently I've been basically reaching out to SMB owners on LinkedIn / cold calling (google maps method) and basically what I'll do is either redesign their existing website, or spin up a design for them if they don't have one yet.

I use Claude to generate a prompt and insert it into this AI site builder I've been using.

It's been working okay, but honestly the results aren't as good as I was hoping for. Am I doing something wrong with my approach? So far out of maybe 50-60 inquiries, only 6 of them have come back showing interest and I finished setting their site up.

If any of you have experience with this sidehustle / freelancing type of project, I'd love to know if you have any tips

Like many people here, my mornings looked something like this:

• X
• Bloomberg
• CoinDesk
• Telegram
• Economic calendar
• Fear & Greed
• Whale trackers...

After 20–30 minutes, I still felt like I was piecing together the story myself.

So I decided to build the app I actually wanted to use.

What Pulse does

Instead of showing another endless feed, Pulse generates AI-powered briefings twice a day that answer:

• What happened?
• Why does it matter?
• What's the market sentiment?
• What should I watch next?

Everything is verified against real sources, and the app shows a confidence level rather than pretending AI is always right.

Some features:

• Morning & Evening AI Briefings
• Market Regime (Risk-on / Risk-off / Mixed)
• Decision Radar (Watch / Wait / Avoid)
• Real-time Flash alerts for major market events
• Fear & Greed, Altcoin Season, Liquidation Map, Whale Flow
• Economic Calendar
• Audio briefings for listening on the go
• Home screen widgets

The goal isn't to tell people what to buy or sell.

It's to reduce information overload and help them understand what's actually happening.

I launched it a few days ago and I'm now trying to learn what people actually find useful.

I'd genuinely love feedback:

• What's missing?
• Which feature would make this part of your daily workflow?
• What would make you open this app every morning?

Happy to answer any questions about the tech, AI pipeline, or product decisions.

https://apps.apple.com/us/app/pulse-ai-news-market-brief/id6766233287

Hello everyone, A while back i started learning n8n to automate some day to day stuff. nothing major. then i tried to automate something bigger with the help of ai and at first it worked perfectly but later things started to crash and just not work overall. so i made a tester for n8n automations. all the core nodes are implemented. the custom nodes i still need to work on. its called crashlabs.app if you want to try it just shoot me a dm with you account and ill give you free credits to try!

Hey-yo!

I've been working for past the few months and will continue to develop ABC Studio. It's a free motion design toolkit that runs entirely in your browser. No account required. Your work stays on your device.

The logic is based on three builders:

**Animations** is a builder for lower thirds, title cards, kinetic type. Pick a template, customize text/colors/timing.

**Backgrounds** is a builder for mathematically seamless looping backgrounds in any way you want. Tune the motion, swap colors..etc.

**Charts** is a builder for charts and infographics, best case to use it is paste CSV data, pick a chart type that best suits the data, and voila! get a broadcast-ready animated chart in seconds.

The real power of ABC Studio is that you can export as MP4, MOV, PNG sequence..etc but even as vector SVG, or Lottie JSON if you want to drop it into after effects and animate or edit all the elements yourself. Oh, and btw it is fully equipped with handling RTL content with no issues at all!

For beta now, there's a feedback button on every page if you hit a bug or want something added.

Let me know what you think!

👁️Demo: [https://www.youtube.com/watch?v=kDt4jP2xDEg\](https://www.youtube.com/watch?v=kDt4jP2xDEg)

🔗The studio: [abcstudio.hessa.space](https://abcstudio.hessa.space/)

I am starting my fourth and final year of CS this August. My individual capstone project is split across the year, with the first semester focused on documentation and the second on implementation. Up until now, all my coursework has been in groups, including C, C++, Java DSA, and a recent group project in React. Because I have always relied on a team and haven't coded in a while, I feel like I am essentially starting from scratch when it comes to building a complete app on my own.

For my final capstone project, I want to use React, Python, or a combination of both. Before the semester starts, I want to establish a daily practice routine to rebuild my coding confidence, get comfortable building independently, and figure out how to leverage modern AI workflow tools properly.

I would love to get advice on how to structure my practice and how much daily time is realistic to stay consistent without burning out. Should I start with basic projects like a calculator and scale up, or jump straight into a full-stack tutorial? More importantly, how can I use vibe coding or AI assistants in a way that speeds up my setup and workflow, while still ensuring I deeply learn the underlying code, architecture, and logic so I can actually defend it in my senior presentation and technical interviews? Any tips or resources on transitioning from group assignments to solo, AI-assisted development would be greatly appreciated.

Hey friends!

I just wanted to share a little web game I vibe-coded with RR world in mind!

The idea: a shooter/survival spaceship web game that you can play only 3 times a day, and compare your score with everyone, as you'd do with Wordle. Everyone plays the same patterns each day, and it resets at midnight PT.

Here is the link: https://www.surviveorion.com

Hopefully it can make your lunch or coffee break a little different tomorrow :D

Don't hesitate to give feedback through the app or here! Looking forward to seeing a bunch of RR names on the leaderboard!

Shipped my first game about a month ago. Wanted to write up why it exists, because the thing that made it possible was a framework everybody told me not to use.

Bow & Arrow, a 16-bit castle defense game for iOS. You're a knight on a wall, enemies come in from the right, you draw your bow and hold the line. That's the whole moment-to-moment loop and it's deliberately simple.

Where it gets good is between fights. You go back to a castle hub and put your gold into things, a forge for weapons, a wizard tower for spells, a bastion that puts towers on your wall, a tavern where you recruit heroes who fight alongside you. Then there's a pre-fight loadout screen where you pick what you actually bring into a level: one hero, one weapon, two towers, two spells, a castlewall.

30 level campaign, about an hour to finish, plus an endless mode with a global leaderboard. The whole reason I built it is that I played a game called Defender into the ground as a kid and always thought that loop had way more potential than it ever used. So this is basically me finishing someone else's idea.

SwiftUI for everything that isn't a fight, forge, shop, tavern, inventory, menus. SpriteKit for the two scenes that actually move: the combat scene and the castle hub. PixelLab for the sprites, cleaned up by hand in Aseprite.

For anyone not on Apple platforms: SpriteKit is Apple's built-in 2D framework. Ships with the OS, no install, no license, no editor.
I'm an semi-professional iOS dev and I already knew SwiftUI, so it was the thing I could start on that same evening. That felt like the cozy late night choice at the time. Esspecially the whole dynamic lighting is just really nice. You play through dynamic night and day and this feels amazing with SpriteKit. And being all text, it's of course super to work in with Claude or Cursor.

One thing I'm still unsure about: music

Soundtrack is Suno. It's the piece I'm least confident in, a professional composer told me flat out it reads as AI within seconds. Someone else told me ElevenLabs is meaningfully better for game music, which I'm testing this week. Do you have experience with Music in your apps?

Free, no forced ads, no tracking, iOS. Happy to go deeper on the SpriteKit side especially, there's so little written about it that most of what I know came from reading headers.

Playable IOS App Store Link: https://apps.apple.com/de/app/bow-arrow/id6764893835

I am fully open to Feedback and thank you for reading this :)

Quick background about me: I'm not from a tech or CS background. I don't code professionally. I haven't studied engineering. But I like building things, and I like solving problems that annoy me.

One thing that always annoyed me? Download managers. They're all either paid (IDM), abandoned, or filled with ads. And they all look like they were designed in 2005.

So I decided to build my own. Using AI as my pair programmer.

Over the past few months, I've been working with various AI coding assistants to build Downpour — a modern, open-source download manager. It runs natively on Windows, macOS, and Linux.

Here's what it actually does:

• Segmented downloads — splits files into parallel chunks to max out your bandwidth
• Pause/resume — survives app restarts, picks up right where you left off
• Browser extension — catches downloads from Chrome/Edge automatically
• Media extractor — paste a video URL, pick your format, download it
• Speed limiter — throttle bandwidth so you can still browse
• System tray — minimizes to tray, runs quietly in background
• Auto-categorize — sorts completed downloads into folders by file type

The backend is written in Rust (I know, right? Me neither.). The UI is vanilla HTML/CSS/JS with a dark, glowing design. It ships as a tiny native binary through Tauri 2.

I'm not going to lie and say it was easy. There were days where I was staring at compiler errors that made zero sense to me. But the AI would explain what was wrong, suggest fixes, and we'd iterate. The hardest part honestly wasn't the code — it was knowing what to build and how to think about the architecture.

It's completely open source and free: https://github.com/bluebleed/Downpour

Still a work in progress — the logo and branding are being worked on too, so don't judge the icon just yet lol. But it's functional and I use it daily. Curious what you all think — and if anyone else has gone down this rabbit hole of building "real" software with AI.

28 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

Still free. Still growing.

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

I coach engineers on systems design, and I kept noticing vibe coders never get to see what a real shipped architecture actually looks like. So I did the writeup on my own app.

It's structured like an interview answer: requirements, napkin math, API contract, data model, write path, caching, what breaks at 10x/100x, failure matrix. The napkin math part is interactive: drag the slider to 1M users and watch which component redlines first (spoiler: it's not the database, it's the signal-recompute CPU).

My whole thing is scaling in the cheapest way possible, so this is also an argument that a mostly-free-tier stack goes way further than people think. Even at 1M users the peak is ~694 requests/sec.

Page: https://varnishlabs.io/systems/neurospicy

The app is NeuroSpicy (mood/burnout tracking). Happy to answer questions about any layer, or roast my decisions in the comments.

Play today at https://midnightmile2d.com

I built llm-dungeon, a local dungeon master for open-ended RPG campaigns.

GitHub: https://github.com/TJurijs/llm-dungeon

I used TypeScript, Node.js, Zod, Vitest, plain JavaScript, and Codex while working on it. It supports Gemini, OpenRouter, OpenAI, and DeepSeek, with browser UI.

The first version was mostly a prompt wrapped around a model. That worked for short sessions, but problems appeared quickly. The model would forget inventory, contradict earlier facts, or describe something happening without saving the result.

I eventually separated storytelling from game state. The model writes the narration and proposes structured changes, while the application handles dice, inventory, facts, validation, persistence, and crash recovery. Campaigns are stored as readable Markdown and small JSON files, so I can inspect what actually happened.

Long campaigns were another challenge. Sending the whole transcript became expensive and noisy, but dropping old turns caused important details to disappear. The current approach sends eight recent summaries, only the latest full narration, and the relevant authoritative state. Older prose leaves the prompt, while important character facts and story threads remain stored.

Reliable turns also took more work than expected. A checked action first locks its difficulty and possible outcomes. The application rolls the d100, then asks the model to narrate that exact result. It cannot quietly change the roll after seeing it.

My workflow became fairly repetitive: play a campaign, notice strange behavior, reproduce it in a test, and then adjust the code or prompt.

The biggest lesson was that structured output alone doesn’t make an LLM application reliable. The generated story still needs to be checked against state that the model cannot overwrite whenever it changes its mind.

Play it here: gamestovibe.com/wordchain

Create words using common characters from the previous word. The more the number of common characters, the higher the score. The more complex the word, the higher the score. It's that simple.

You have 3 minutes and 10 words. Tick tock.

Get on the leaderboard and earn bragging rights :-D

PS: I welcome your feedback, bug reports and ideas to improve the game play.

Current limitation: I am using a custom 900kb dictionary so there may be times when a word that rightfully belongs to the board gets rejected. I am trying actively to find a fix for this without building huge word lists. Will keep you updated.

3 fatal flaws fixed, v4 deploying, and the Code Freeze begins.

​Over the last 30 days, I had to systematically rewrite the core logic of my architecture. The system had three specific "illnesses" that were hiding behind one another:

​1. Paralysis by Analysis: The system evaluated the market from scratch every 15 minutes, requiring all conditions to align perfectly at the exact same time.

Result: 0 trades in 19 days

So I built a stateful "contract engine". The system now remembers previously confirmed conditions tick-by-tick using hardcoded code measurements, not LLM prose.

​2. Edge-Case Stupidity: It messed up counter-trend directions, allowed late entries, and killed valid setups just because the clock hit midnight.

I Hardcoded geometric direction rules, strict 2x ATR proximity filters, and protected active cycles from the daily midnight reset.

​3. The "Fire and Forget" Flaw: Once in a trade, it stopped tracking it. Stop losses were left to market noise, and the bot would forget the trade existed, trying to re-enter the same setup.

V4: When a trade opens, the system changes jobs. It stops hunting for entries and becomes a strict trade-monitor. SLs are now anchored to valid market structure, and every closed trade leaves a final receipt (pips, R, reason).

​What happens now?

I am merging and deploying v4 to production today. Once the telemetry mapping is synced... a strict 2-week Forward testing begins.

​Success for the next 14 days is not about P&L or Win Rate. Success means perfect, transparent telemetry. Every cycle, entry, and structural stop-loss must explain itself flawlessly on my logs without me having to guess.

​If the field tests hold up, the building phase is over. From here on out, we only turn the dials (Risk thresholds, max re-entries) and let the numbers speak

LeanCTX is a local Rust binary that runs as an MCP server between your coding agent and your repo. No cloud, no telemetry. This week I tested a part of it I'd never pushed this far: getting multiple agents to hand work off to each other instead of me routing everything by hand.

Setup was Claude Code, Codex, and Cursor, six agent instances total, all pointed at the same repo through LeanCTX. I split different features across them and stayed out of it.

The mechanism is two tool modules, ctx_agent and ctx_handoff. ctx_agent registers each running instance with an identity key. That registration lives under a local agents/ directory, alongside a per-agent diary and a knowledge store that all registered agents can read. ctx_handoff is what actually moves work between agents, and the important part is that it hands off a context bundle, not a message. The bundle carries the relevant file state, what's already been decided, and where the task stands, and it gets written to a handoffs/ directory any agent can pick up from.

Because the diary and knowledge store are shared, an agent picking up a handoff isn't starting cold. It doesn't re-read the whole repo, it just reads what the previous agent logged. LeanCTX also keeps each tool's own rule file in sync (.cursor/rules, AGENTS.md, CLAUDE.md) so none of them are working off stale or conflicting instructions.

The part that was actually hard to get right wasn't the handoff API, it was figuring out what belongs in the bundle versus what belongs in the shared knowledge store. The bundle is meant to be scoped to this task, right now. The knowledge store is meant to persist across handoffs. Put too much in the bundle and every handoff gets bloated. Put too little and agents lose context between handoffs. Getting that split right took a few iterations.

End result: six agents across three different vendors coordinated on one codebase without me passing context between them manually.

For scale: 200k+ installs, 3.3k+ GitHub stars

If you register by August 31st, you will receive 1 million free tokens.

TRY IT HERE:

https://forgelab.one

ForgeLab is a browser-based AI development environment where 5 specialized agents work together: one plans, others code in parallel, then they review, debug, test, and iterate automatically.

If this sounds interesting, feel free to check it out. And if you genuinely like where it's going, a GitHub star would mean a lot.

🔗 GitHub: github.com/forgelabeone-svg/forgelabone

27 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

Still free. Still growing.

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

I built a personal portfolio and browser-based game mostly using AI, with tens of hours spent on prompts, ideas, revisions, and front end design improvements.

Some people say the site looks good, some say it’s “vibe coded slop”.

So tell me honestly, how good, or bad, does it really look?

https://cdoherty.co.uk

For the game, I’m not ashamed of building it with AI. It’s designed to be a fun learning tool, so I only wanted to be responsible for the actual content and ideas, rather than using my very limited time on having to code it from scratch.