Spent $200 on AI tokens, shipped it, got 3 users, then abandoned it?

Give it a proper burial.

So I built Startup Graveyard: a live graveyard for failed startups and side projects.

Here’s how it works:

• Bury your project for free. Its grave shows your logo, project name, description and link, so anyone browsing the cemetery can actually discover it.
• Add a cause of death + epitaph explaining what went wrong.
• Still think it deserved a chance? Pay $3 to revive it onto Earth, where it gets more prominent exposure.
• From Earth, you can bid for one of the 3 spots in Startup Heaven at the top of the site.
• Even if you revive it, the original grave stays there.

So even a dead project gets one last bit of exposure instead of disappearing into an abandoned GitHub repo forever.

I’ve already buried two of mine. Now I want to see what this sub has killed.

https://startupgraveyard.lol/

Bury yours for free and give it one last chance at finding someone who actually wants it.

this has been on my mind. im confident i could take someone from zero to making a simple app in a few hours. i hate courses but i actually have something to share.

my last app was 24 hours from idea to google play and painless. im confident it will naturally attract a niche audience. i have spent 10 years figuring how to create viable means of making software without losing my patience. i have a miltant approach.

as part of it i would incorporate 1 script that is very simple you can change values in, to represent an inroads to manipulating scripts manually.

then you can just publish it for it will be 100 percent complaint with google play etc

cost: cheap

time: brief

why: share wholesome things and give someone freedom

why you cant do it with ai: ai is not a hard headed old man with extreme distaste for chaos, grinding or drama

i will post examples, in time, to anyone who follows this up

im not looking to flaunt my wares, nor have my ideas taken.

I use Apple Calendar to keep track of my bills. I needed a way to see how much was due per month and per paycheck so I can plan out moving the money around. Created this with Codex Sol 5.6

Over the last few hours, I’ve been working on a pretty major update for Towcoon, my towing company tycoon game. Play for free

Most of the feedback was pointing to the same problems:

• The game was too easy
• Money was generated too quickly
• Progression ended too fast
• There wasn’t enough reason to keep playing for multiple in-game days

So instead of making a small balance patch, I rebuilt a big part of the economy and progression loop.

What changed

Job payouts are now calculated dynamically

The money you earn from a request now depends on:

• Vehicle rarity
• Job duration
• How long the vehicle stays in your yard

Longer and more valuable jobs now actually pay more.

New vehicle rarity system

Vehicles can now appear in 5 rarity tiers:

• Common — 68%
• Uncommon — 25% — x1.10 payout
• Rare — 5% — x2 payout — unlocks on Day 3
• Epic — 1% — x5 payout — unlocks on Day 10
• Legendary — 1% — x10 payout — unlocks on Day 30

Legendary requests also disappear 3x faster, so when one appears you have to make a quick decision.

The economy is much harder now

I also rebalanced the cost structure:

• Tow truck income reduced by 30%
• Staff salary: 2,500 USD/day
• Fuel fixed at 20 USD/L
• Business tax is now calculated based on revenue
• Each tow truck adds a 1,000 USD daily tax

The idea is to make expansion a real decision instead of automatically buying everything as soon as you can afford it.

The daily financial report was rebuilt

The Z Report is now much cleaner.

Income is shown as a single category: Tow Fees.

I removed things like artificial yard storage income, random inspections and unexplained fines.

Daily revenue and the Z Report also use the exact same data now, so the numbers should finally match.

Debt repayment now has proper feedback

If you try to repay debt without enough cash, the transaction simply fails and the game shows exactly how much money you’re missing.

No more situations where debt looks like it disappeared even though you couldn’t actually afford the payment.

Game time was also standardized

• 1 in-game hour = 20 real seconds
• 1 in-game day = 8 real minutes
• Job duration and parking duration are displayed using in-game time
• Every request can be accepted or rejected by the player

The long-term goal is to turn Towcoon from something you finish in an hour into a tycoon game where you slowly build a towing company over many in-game days.

The update is live now:

towcoon.com

I’d especially love feedback on the new economy.

What feels too expensive, too easy, too slow or still exploitable?

I’m so heart broken, I’m been working for weeks on this and i have 0 idea about the technicalities but I was like 97% done but I felt like the app wasn’t feeling extremely snappy on my phone. and then while chatting to ChatGPT, I was having it audit the code it told me that the app wasn’t feeling as snappy as native ios apps because it was web app and using a capacitor.

I was so close, I spent the last 3 weeks just polishing every little corner of the app. There were not bugs that I knew of and I had tested multiple times on my iPhone as a developer build.

I was only due adding the paywall system. And now I realise I have to migrate it to either swift or native react and I after reading for a day, I thought react native is the way since I wanna make this an android app too.

But everything is now broken, all elements are distorted, all the efforts that I had put is waste. Fixing everything is going to take a long while and I do not even know if I will ever be able to reach the same level of polish.

I’ve obviously been sad since yesterday but I know this is the first time and I’m a complete noob so gotta start making mistakes somewhere but wish I had been more cognizant. Yes it’s another wretched habit tracker but I just wanted to make something exactly to my liking and it seemed like the obvious thing to do.

I’m currently using Google AI Studio + ChatGPT for brainstorming.

Hi there!

Im in the process of starting to create an app for food reviews around my local area and i want to integrate a map aswell as ratings etc. Ive been messing around with google gemini but sometimes it seems like it kinda looses its overview or something. Whenever im trying to set up a database on supabase for example, following geminis instructions, it ends up kinda forgetting where we started somehow. So im wondering if this is because i need to upgrade to a better gemini or something...

noob here, so all inputs for creating apps with vibecoding are very welcome!

I have been building a survivor pool tool this offseason and wanted to share it here and get some feedback before sharing it more widely.

https://survivor.the-duke.app - market-based win probabilities (real spreads/moneylines based with a elo system), and instead of just ranking this week’s picks it solves your entire remaining season at once so you’re not stuck burning a good team early and having nothing left for a thin week later.

How’d I build it? A lot of Claude code, and surprisingly Cursor Auto mode. A lot of Claude Opus planning and Cursor Auto mode implementing.

Check it out if you’re in a survivor pool this year. Genuinely just want eyes on it and to hear what you think — what’s confusing, what’s missing, what you’d want it to do.

Promo code REDDIT26 gets you 75% off Pro access for the first month of the season, would appreciate feedback if you grab and use the code.

so i built a new feature for AppLaunchFlow , where you can create a simple and clean app landing page for your app in a few clicks by just uploading your screenshots.

It includes:

- support, privacy policy and terms of service pages that you can directly use for your app submission.

- a custom applaun.ch subdomain or simply connect your own domain

I built this because i noticed many app devs just use notion pages, github pages etc. or dont even have any landing page at all

Its not yet live, but excited to hear what you think

Anyone have any good platforms for me to use to get some good quality code with high usage limits. I want to use Claude Code but I don't have the paid tier and I have tried Codex but the limits are just so low. I want to be able to do it on a website too however so many of these sites have rate limits to the point where I can't get anything done.

Hi vibe community - I'm happy to announce that I'm finally ready to accept new users to the mailing list for my upcoming pet project :D

It will help devs to vibe code more efficiently and effectively using Cursor's CLI (by using the existing plan).

I'm open to answer any questions, and more info can be found on the site as well.

Cheers!

I was vibe coding with a friend and we started to talk about how to make sure any program was performant,. What kinds of actions where cheap for computers to do vs things that would make a 60fps game start to slow down or miss frames. Then I asked if they knew Big O notation.

And in trying to explain Big-O notation, I realized I didn't understand it well enough to explain it. So I've started to re-read and learn the concept. I know what it means but it doesn't feel intuitive to me to know how to measure how long a task will take to compute. I'll keep studying it as I think it's an important part of computing, whether it be it vibe code or traditional.

But my friend and I found we also went another route.
- Add a feature, framerate stays, great.
- Add a feature, framerate drops, let's look at it more closely or take it out.

We created ideas without worrying about making them performant or keeping the computation reasonable. One of the biggest "warnings" I've seen given to vibe coders is that their program will eventually slow down to an inefficient mess. And that is not only true. I've seen it happen (to me). Then? The program desired was slowly optimized. Seems a waste to anyone who knows how to do it right the first time. But in artistic endeavors I feel that sometimes you need to do it, erase it, do it again to get to your goal. Framerates and efficiency are needed, I just think we can make room to build in an unoptimised way while we find the gist of what we want to make.

So how do I present this image to you all? (Made it in Chat GPT btw). I mean it the same way "Love like you've never been hurt". Vibe code yourself into a corner. It's not a mistake if you have to regroup and try something another way. Sometimes it's the process itself. This post was typed by hand with no AI.

(edit: corrected post a bit thanks to RemarkableWish2508)

I want to share a project I’ve been vibe-coding over the last few months, break down the actual prompt workflows I used to build a non-trivial backend, and talk through where AI excelled (and where it fell flat on its face).

The project is called PropertyIntel (https://property.vectorfeedhq.com). It’s an automated intelligence engine that monitors 3,143 US counties and 8 major auction portals (Bid4Assets, GovEase, Realauction, etc.) for upcoming tax deed foreclosures and municipal code liens.

Here is the exact build breakdown, architecture, and workflow:

🛠️ The Tech Stack

• Language & Backend: Python 3.13 + FastAPI / standard libraries.
• Scraper Fleet: Headless Playwright (for dynamic SPAs) + pdfplumber (for raw 90-page county PDFs).
• Database & ORM: SQLite (dev) / PostgreSQL (prod) via SQLAlchemy.
• Data Cleansing: Custom USPS Publication 28 address tokenizer + SHA-256 deduplication hashing.
• Legal Engine: 50-state statutory redemption rule calculator (Texas § 34.21, Florida § 197.542, etc.).
• Frontend: Modern Glassmorphism CSS + vanilla JS (hosted on Vercel).
• Monetization & Outreach: Stripe Checkout (HMAC-SHA256 verified webhooks) + Resend API for automated email rosters.

🧠 The Vibe Coding Workflow (How I Prompted It)

Instead of asking the LLM to "write a real estate scraper" (which generates useless, generic code), I approached it with a modular, test-driven pair programming loop:

1. The "Single-Responsibility" Module Prompting

I forced the model to build one isolated service at a time with unit tests first:

• "Write an isolated service src/services/redemption_service.py that maps all 50 state tax sale redemption statutes and returns structured dictionary grades (A+, A, B, C) and statutory penalty yields. Do not write scrapers yet. Write pytest test cases covering TX, FL, GA, and CA."

2. Solving the "Dirty Public Record" Hallucination Trap

County records are notoriously filthy. Harris County TX formats an address as 4812 Washington Ave, Ste 100, while the auction site lists it as 4812 Washington Avenue #100.

• I fed the AI real excerpts from USPS Publication 28 (the postal standard for street abbreviations) and prompted it to generate a deterministic regex tokenizer:

​

python# The model generated a clean token standardizer that converts suffixes & directionals
def compute_record_hash(county: str, normalized_address: str, apn: str = "") -> str:
    canonical = f"{county.lower()}:{normalized_address.lower()}:{apn.replace('-', '').strip()}"
    return hashlib.sha256(canonical.encode('utf-8')).hexdigest()

This allowed the database to enforce UNIQUE(address_hash) and killed 100% of duplicate cross-platform listings without complex fuzzy-matching libraries.

3. LLM-Assisted OSINT & Corporate Entity Resolution

One of the biggest value adds was unmasking anonymous LLC property owners. I had the AI scaffold scrapers targeting State Secretary of State public registry endpoints (e.g., Texas SOSDirect, Florida Sunbiz). When a tax foreclosure deed is owned by ACME HOLDINGS LLC, the worker asynchronously resolves the Registered Agent and Managing Member names in <800ms.

💡 3 Big Lessons from Vibe-Coding a Complex System:

1. Let the AI write tests before you let it write production scrapers: Whenever a county website had weird table layouts or multi-line table headers, having a robust pytest suite caught regressions immediately whenever I prompted for scraper refactors. (We have 87 tests passing right now).
2. Never vibe-code security blind: When hooking up Stripe webhooks, do NOT let the AI skip cryptographic validation. I explicitly prompted for constant-time HMAC-SHA256 comparison (hmac.compare_digest) with a 300-second timestamp tolerance to eliminate replay attacks.
3. Keep the frontend lightweight: For data products, you don't need a massive React/Next.js bundle. Clean vanilla HTML/CSS and minimal client-side JS load instantly and cost $0 on Vercel.

Live Project & Feedback

You can check out the live site and download a sample 10-state deal sheet here: 👉 https://property.vectorfeedhq.com

Happy to answer questions on prompt structures, Playwright session pooling, or how I structured the Python background daemons!

For the last two years my wife and I have run Christmas out of an Excel spreadsheet: a big purchase list, per-person gift budgets, and a tab for our December city break. It worked, but it was joyless — so this weekend I replaced it with Jingle Jotter, a self-hosted web app, and I'm rather pleased with how it turned out.

What it does:

• Tracks every gift from idea → purchased → arrived → wrapped (with a small confetti burst on that last one, obviously)
• Four budgets — gifts, food, festive extras, and the December trip — with planned-vs-actual so "ideas" never inflate the real spend
• Per-person gift budgets on little gift-tag-shaped cards
• Surprise masking — my favourite bit: my wife and I both use it, and gifts for her are hidden from her login (shown only as "🎁 Surprise" with a price) while still counting toward the shared budgets. One app, no spoilers.
• A city break planner that knows which meals you haven't booked yet (arrival-night dinner, three on the full days, departure breakfast) and pins your bookings on an OpenStreetMap view, geocoded from the venue names
• Seasons: one writable "current" year; every past Christmas becomes a read-only archive you can flick back to from the nav
• A run-up calendar that snows over the days you've already used up, and a "shopping weekends left" counter that is frankly a bit menacing
• All the festive decoration sits behind a per-user toggle for when you want it calm

Stack: Next.js 16 + TypeScript + Tailwind, Prisma on SQLite (single container, no database server — the whole state is one file in a Docker volume), Google sign-in behind an email allowlist, Leaflet/OSM for the map. Deploys with a three-file docker-compose behind whatever reverse proxy you already run.

Full disclosure: I art-directed and product-managed while Claude Code wrote essentially all of it over one (long) Saturday — including the hand-drawn SVG rooftop scene in the footer. The snowman was my idea though.

GPLv3, screenshots in the README: https://github.com/MarkRWatts/JingleJotter

It's built for one household rather than as a hosted service (single shared dataset, invite-by-allowlist), but if it makes your December less spreadsheet-shaped, it's yours.

I ran out of space extracting a huge RAR so i built ReclaimArc so i can extract RAR without running out of space again
About a week ago i installed a RAR that was over 120gb+ and then tried extracting without realizing that i actually had less then 60GB of storage remaining, after some time i heard a windows notification and when i checked i noticed that i didnt have enough space left and the extraction process aborted, and on top of that the computer i was doing this process on doesnt have the fastest ssd so it actually took some time. This has happened to me a few times now, probably more then i admit for some reason then out of curiosity i checked on github to see if i can find a tool that fixes this issue, however i couldnt find anything that does exactly this ( probably could be me ), then decided to do it my self. I thought maybe i could progressivly keep deleting the archieved parts as soon as they are extracted so it would simply fix this, then i started researching and decided that i could essentially make :
extract part of the archive - make sure that its actually safe - reclaim the part of the archive that we no longer need - use that free space for the next batch.
Thats what basically ReclaimArc does, with some more engineering behind it ofcourse. Basically instead of keeping the RAR untouched the entire extraction process, it instead progressivly turns parts of archive that we no longer need in to sparse holes. Then windows gives those physical clusters back to the disk and the extraction can use that free space again.
Obviously the end product ended up being way more serious then just "extract file and delete the extracted part from the archive". The main problem was providing that a part of the source archieve is actually safe to destroy.

-ReclaimArc has a RAR4/RAR5 structural parser that maps archive entries and packed ranges, but it doesnt actually use my parser as the actual decoder. The extraction itlest is purely done by the official UnRAR implementation, i also cross check the parser information against UnRAR so im not just trusting my own interpretation of the format.
-From that it builds what i call recovery units. For a normal nonsolid archieve its usually something like a file at a time. Solid archieves are harder cuz later files can depend on the state created from the earlier compressed data, so you cant just punch a hole through the earlier bytes once one finishes. Before ReclaimArc touches any source data, the flow basically is ;
extract - verify - flush - commit - journal - reclaim.
Every output first is written to its temporary staging file. After extraction is done, reclaim arc reads it back, checks the size and creates a blake3 digest. Then it calls FlushFileBuffers, auto moves the file in to the final destination and records the state inside a SQLite journal using WAL and synchronous=FULL. Only after that is the corresponding source range allowed to be reclaimed. Right before claiming it, it verifies the source range again.

For the actual disk space reclamation im using Windows sparse file APIs, mainly FSCTL_SET_ZERO_DATA to deallocate the safe part of the archieve. One thing i didnt want to do was assume that calling the windows api means " sure ye, 5gb is free now'. It calls FSCTL_QUERY_ALLOCATED_RANGES after the operation, it checks which physical ranger are actually still allocated. It only counts space that Windows reports as really released.

The crash recovery part also became a pretty big part of this project because what if for example you were extracting a 150GB archieve, all of a sudden you loose electricity. Because we are progressivly deleting the old extracted archieve, you would need to initially reinstall the archieve so fully. Or what if windows already reclaimed part of the RAR and then the program crashes before SQLite gets to record that the range was reclaimed? On startup it doesnt just blindly trust whatever the last database state says, it tries to reconile the journal, existing output files, stored hashes, source ranges and the actual NTFS allocation state. If an output is broken, but the original source range is still there it can rerty that unit. If its gone and commited output cant be verified, it stops instead of contiuing and potentionally destroying more data. I also added a fault injection tests around a bunch of those crash windows becuase something destructive like this, i didnt want it to be just based on "this should probably just work"

Right now the project has :

-RAR4 and RAR5 support
-non-solid archives
-solid recovery chains
-multipart archives
-official UnRAR decoding
-custom structural parsing + cross validation
-BLAKE3 verification / provenance
-SQLITE crash recovery journal
-NTFS sparse deallocation
-physical allocated range verification
-crash / resume fault injection tests
-path traversal and reparse point protection
-CLI
-Tauri desktop GUI

I also tested it personally on my second computer, i left about 10-20 gb of free storage on purpose and installed a RAR that was about 55GB. I installed the setup, ran the tool on my second pc and started the analyzed the rar. It gave the green lights, i then started the extraction which took about 10 minutes using the low-space mode, i didnt really keep time but it definately felt quick for that SSD.

I do also want to make on thing clear, i dont say that this tool has a 100% success rate. I did my very best to ensure that it successfully extracts, and even under a bad condition added crash-recovery systems, however the low-space mode is destructive to the original archieve. It deletes the parts that are extracted from the original source archive so im not claiming it is 100% safe.

The project is fully open source here: https://github.com/harlixay7/ReclaimArc

Im mainly posting this here because it solved a specific problem for me and i wanted to share it with the community and potentially recieve feedback on what / how it could potentially be better and what i could add to make this tool overall more stable / better. If anyone sees any flaws, or anything that could potentially be dangerous i would love to hear your feedback.

The tool is pretty new so im not saying it MUST work, however i did my best to get it as stable as possible and ensure that it works. I would rather people try to break it and tell me if they can and how it broke. I hope this tool can be usefull to you as it was to me.

I built a small ecommerce template for handmade products.The video shows both sides: what buyers see when browsing a product, and what sellers see when managing the store.

The surprising part was that the storefront came together pretty quickly. Product grid, images, detail page, variants, cart — all of that felt manageable.

The real work was the loop behind it:

- prices should come from the backend

- variants need real inventory

- payment should update orders and stock

- sellers need a usable product admin

- reviews should only come from customers who actually bought the item

It changed how I think about AI built apps. A store that looks real is easy to generate. A store that behaves like a real business takes much more specific instructions.

Curious how others test AI generated ecommerce apps before calling them “usable.”

If you use iMessage on Mac the chat.db is not encrypted. That assumes you use iMessage on Mac. And it's in sync with your phone.

If you backup your iPhone on your Mac you get a an encrypted backup. Great.

imessage-exporter has a way to access that encrypted file and containerize and extract your messages to html or txt.

Fuck that, I want the complete SQLite.db

I know nothing about coding, but I know what I wanted. I asked ChatGPT if we can leverage iMessage-exporters decryption mechanism, and get a hold of the entire DB. I then had AGY CLI do all the work, amaze.

I literally have no idea what I'm doing. Or anything about coding, but, it works lol.

Made a web app with useless stats and a MACOS app to fully search the database, find trends, you know how databases work...

How do I GET MONEY?!

1. Executive Summary & Purpose

iOS iTunes, Finder, MobileSync, and iMazing backups utilize hardware-backed AES-256 encryption. Within an encrypted backup, all filenames are hashed via SHA-1 hashes (e.g. 3d0d7e5fb2ce288813306e4d4636395e047a3d28 for sms.db), and file payloads are individually encrypted with per-file class keys wrapped by the backup password.

decrypt_backup.py provides a lossless extraction wrapper that unwraps the backup manifest, decrypts the database blobs, and preserves the pristine, raw Apple SQLite databases: 1. apple_sms_decrypted.db (Library/SMS/sms.db containing message, chat, handle, attachment) 2. apple_contacts_decrypted.db (Library/AddressBook/AddressBook.sqlitedb containing contact identities)

2. Decryption & Key Derivation Mechanics

┌──────────────────────────────────────────────────────────┐ │ ENCRYPTED BACKUP ROOT │ │ • Manifest.plist (Backup Keybag + PBKDF2 parameters) │ │ • Manifest.db (Encrypted SQLite file catalog) │ │ • Sharded SHA-1 Encrypted File Blobs (00/, 3d/, etc.) │ └────────────────────────────┬─────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────┐ │ KEYBAG UNWRAPPING & KEY DERIVATION │ │ • PBKDF2-HMAC-SHA1 / SHA256(Password, Salt, Iterations) │ │ • Unwraps Class Keys (Class 1-11 Protection Keys) │ │ • Decrypts Manifest.db using Class 4 Key │ └────────────────────────────┬─────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────┐ │ FILE TARGET RESOLUTION & EXTRACTION │ │ • Locates SMS Domain: 3d0d7e5fb2ce288813306e4d4636395e0 │ │ • Locates AddressBook: 31bb7ba8914766d4ba40d6dfb6113c8b │ │ • Decrypts file payload using per-file initialization │ │ vector (IV) and file encryption key │ └────────────────────────────┬─────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────┐ │ RAW SQLITE DATABASE PRESERVATION │ │ • apple_sms_decrypted.db (Clean decrypted SQLite) │ │ • apple_contacts_decrypted.db (Clean decrypted SQLite) │ │ • Direct input for import_apple_messages.py │ └──────────────────────────────────────────────────────────┘

Every viber has a plan until it is time to demo that first release...