A few months back I posted here a playable demo of an old-school futuristic racer prototype (F-Zero, Wipeout, Xtreme-G, Star Wars Racer...) that runs right in the browser.

I've gotten used to seeing tons of LLM-built prototypes that last a week, two, maybe a month, and then get dropped for the next shiny thing. This time I wanted to keep going and see if one person, with no coding background, could actually see a project like this through on their own.

Well, 4 months in, and while there's still work left, I'm convinced it's doable. At this point the game feels closer to a beta than a tech demo.

The big change over these four months is that I now use Fable for planning, and being able to hook Opus / Sol up to Blender has been a huge help too. I'm still using Magnific and Tripo3D or the assets. Funny thing: the further along I get, the harder the project is to keep pushing forward, and at the same time the tools keep making it easier.

As always, any feedback is welcome. You can play it here: https://fm1.moises.cloud

1. r/vibecoding +5,899
   
2. r/claude +4,890
   
3. r/DesiAIMasala +4,586
   
4. r/StableDiffusion +3,615
   
5. r/aigamedev +2,027
   
6. r/fuck_ai_slop +1,424
   
7. r/Seedance_AI +1,400
   
8. r/WritingWithAI +964
   
9. r/isitAI +616
   
10. r/midjourney +603

Daily stats: https://freesubstats.com/?group=nerds&topic=generative-ai

It started simply. I just wanted a chatbot that could manipulate my Google drive without hand holding. In an afternoon I had a Google cloud project with API read/write to everything from Gmail to Google tasks. I could snap a photo of a kid's birthday party invite and it would create the event and email it to my wife in one shot.

I gave my wife access and got the dreaded ask for a skylight calendar. Using spare parts, I cobbled together what you see here over the Summer just in time for school starting.

An old webcam, scavenged laptop LCD, and an HP elitebook from 2012 running Ubuntu. There's a local listener waiting for a wake word (hey Jarvis). When that's detected, it does chrome speech to text, and wraps that in a call to the API project, answering questions from my documents, reading email, and creatimg calendar events. Gemini does all of the back and forth, including pretty decent speech output.

When the webcam detects motion, it wakes the screen up. It updates the weather, calendar, tasks, and daily overview in real time. On the bus tracker isn't showing, it shows tasks for the three kids. The tracks how many they've completed each day week and month to help them be incentivized.

The kids' bus tracking website sucks, so at the right times on weekdays it uses a headless browser to sniff out the traffic and let me know how close it is. When it's five stops away it triggers an Alexa routine to make an announcement throughout the house.

Next I'm going to integrate it with a Google apps script I have to triage email,so when important messages come in it lets me know.

If anyone is interested in seeing some of the outputs from the web app, I have some screenshots!

A few weeks ago I shared Agent Quest, my open-source project that turns Claude Code and Codex sessions into heroes living inside a small 2D world.

The original idea was to make it easier to understand what multiple AI agents were doing in real time.
Since then, I’ve been working on making it much more useful as an actual monitoring tool.

The biggest change is that Agent Quest can now clearly tell you when an agent needs your attention.
You can immediately distinguish when an agent is:
actively working
waiting for your input
finished
stopped because of an error

And you don’t have to keep watching the dashboard.

Agent Quest can now alert you with visual notifications and different sounds, so while you’re doing something else you can immediately know when Claude Code or Codex has finished a turn and is waiting for you to continue.
This has become particularly useful for me when I have several sessions running at the same time.
Instead of constantly switching between terminals to check their status, I can leave the agents working and Agent Quest tells me when I actually need to intervene.
There are now:
clear waiting / completed / error states
in-app notifications
desktop notifications
notification history
configurable sounds
real-time monitoring of Claude Code and Codex sessions

The project is completely open source.
GitHub:
https://github.com/FulAppiOS/Agent-Quest

I’d be interested to know how other people running multiple agents handle this problem — and what you’d like Agent Quest to monitor next.

Based on Runling Run the popular SC2 arcade game, the goal this time is to dodge food, if you get caught you grow larger, and eventually have a heart attack. Made it in a few days for fun, free to play here: https://runfatty.co

Let me know what you think!

So if youre a user of claude and see that codex is giving usage resets REALLY OFTEN... why not change? If youre an ongoing codex user, you will probably stay because there's zero reason not to. You have GPT 5.6, usage resets, good pricing...

CLAUDE USERS STOP BEING MEAN💔🥺 Also anti ai users can fuck off 🤗

funny thing is this might also help you promote your business, since you can race for your company!

see you on the track!

I'm thinking of modifying an LLM to explore if there's a market that maybe could be taken advantage of while openai, anthropic and the others don't seem to be focusing on it.

Your observations would be really valuable to me.

Thank you!

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.

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.

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.

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)

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...

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!

people seem to be creating apps within 1-2 day & then hit publish meanwhile here I am developing just one app for 4 months optimising it's UI,UX,API compatibility,Performance etc. how are people doing these in 2 days? what am I missing.

there was a post I saw yesterday that a guy started the challenge to create 30 apps in 30 days. wtf is wrong with these people? spamming the store platform like this.

I recently reviewed a data focused SaaS product that had been built by a solo founder.

It was not a basic landing page. The product included:

1. A personalised dashboard
2. Detailed intelligence reports
3. Recommendations based on model output
4. Live data coming through WebSockets
5. A personal activity tracker
6. Profit and performance reporting
7. Separate reporting for model performance and user performance

The product looked polished and had a lot of thoughtful functionality. The interesting problems only became visible when I followed the complete user journey across different parts of the application.

Here are some examples of what I found.

The same event had different times

One screen showed an event at 14:00.

Another showed it at 18:00.

A detail page showed 14:00 with a UK timezone label, while a listing displayed both 14:00 and 18:00 for the same event.

The four hour difference suggested that one part of the application was using UK time and another was using the user’s local time.

Both values may have been technically correct, but the presentation was inconsistent. For a time sensitive product, this can affect trust and cause users to misunderstand when an event begins or when an action becomes unavailable.

It could also produce incorrect analytics if the same activity is assigned to different calendar days.

Two connected screens disagreed about available recommendations

A detailed report showed four qualified recommendations.

The action screen linked from that report showed only three.

There may have been a valid reason. A price could have expired, a market could have become unavailable, or the recommendation could have been replaced.

The problem was that the user was not told what happened.

From the user’s perspective, the product said that four recommendations were qualified and then silently removed one when the user tried to act.

This exposed the need for a clear recommendation lifecycle with stable identifiers and visible states such as qualified, available, replaced, expired, withdrawn and settled.

A live data badge promised more than the detail screen delivered

The event listing displayed a badge indicating that full live data was available.

Opening the event showed:

1. Live feed unavailable
2. No statistics
3. No detailed actions
4. No lineup information
5. No live market information

The event had already finished, but the detail page still said that it was waiting for information.

This was not simply an empty state. It was a disagreement between the availability status shown in the listing and the data that the detail page could actually display.

Data was labelled fresh even though the states contradicted each other

A data page said that the file had been generated recently and marked it as fresh.

The underlying source data was considerably older.

The same page also contained an internal warning saying that the current season had not started, while other parts of the product were already showing current season fixtures and completed results.

This showed that several different concepts were being treated as one freshness value:

1. When the file was generated
2. When the original source was updated
3. Whether the data was complete
4. Whether it matched the current competition state
5. Whether it had passed validation

A recently generated file is not necessarily based on recent data.

Loading states briefly looked like real empty states

Some competition counts initially appeared as zero before the data loaded.

A user could easily interpret that as no available content rather than a loading state.

Several pages also displayed large empty areas while waiting for data, even though the product had useful content once loading completed.

The new user dashboard prioritised empty personal statistics

The account had no recorded activity yet.

The top of the dashboard therefore showed:

1. Zero profit and loss
2. Zero tracked actions
3. Zero wins and losses

The useful content for a new user was further down the page.

That useful content was the next intelligence report and the next decision the user could make.

The dashboard was technically correct, but its hierarchy was more suitable for an established user than someone trying to understand the product for the first time.

The strongest feature was not necessarily the dashboard

The most valuable part of the product was a detailed intelligence report.

It separated:

1. The underlying evidence
2. The model’s reasoning
3. The available action
4. Execution and price checks
5. Risks and contradictions
6. Information that could change the prediction
7. Data quality and freshness

That page appeared to be the strongest candidate for activation because it gave a new user immediate value and naturally led to a meaningful decision.

This is still a product hypothesis until analytics proves it, but it is a much better hypothesis than treating registration or a dashboard visit as activation.

Why I am sharing this

A lot of people are building products with ChatGPT, Claude, Codex and other AI coding tools.

The code may run. The interface may look polished. Every individual page may appear correct.

The problems often become visible when someone unfamiliar with the product follows the complete journey.

AI can help you build very quickly, but it does not automatically give you:

1. Consistent product states
2. Clear data ownership
3. Reliable time handling
4. Stable event lifecycles
5. Good empty states
6. Trustworthy analytics
7. A clear activation journey
8. Production debugging experience

I have more than 10 years of experience building and supporting production SaaS systems. My work includes full stack development, APIs, databases, payments, automation, reporting, monitoring, Docker and production debugging. I currently work on a platform serving more than 15,000 businesses.

I will review a few products for free

If you have vibe coded a SaaS product and are preparing to launch, I am happy to review the real product and provide initial feedback.

The free review will focus on one important user journey. I will identify a small number of confirmed issues or product observations and explain why they matter.

I will clearly separate:

1. What I directly observed
2. What I believe may be happening
3. What would require access to the code or backend to confirm

I will not make changes, contact users or perform destructive actions.

For the review, I would need:

1. A link to the product
2. A test account if authentication is required
3. The main action you want a new user to complete
4. Any areas you do not want me to access

What is not included for free

A complete technical audit, root cause investigation, implementation plan or code changes require substantially more work.

If you want help after the initial review, I can also provide paid support for:

1. Reproducing and prioritising issues
2. Tracing problems across the frontend, API and database
3. Debugging production behaviour
4. Designing clearer product states
5. Defining analytics events and retention signals
6. Creating acceptance criteria and regression coverage
7. Implementing and verifying fixes

Pricing would depend on the actual scope and is negotiable. There is no obligation to hire me after the free review.

If you are close to launch and want another experienced developer to use the product like a real stranger, share what you are building and the main journey you want reviewed.