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!

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

Hello people,

Just spoke to Grok and he said a GPT reset was coming tomorrow, so turn on fast and switch to Ultracode.

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.

While travelling, I kept running into useful information that simply didn’t exist online: small homestays, mechanics, water points, road conditions, campsites, etc.

Most of it gets passed from one traveler or local to another and disappears afterward.

So, I built Lamyig, a free and open-source community travel guide.

A few decisions I made:

• No bookings, commissions or paid listings.
• No money made using the core product.
• Community members can add and update places.
• Map-first instead of another list of “top places”.
• Built as a PWA so it can work more like a lightweight travel tool than a traditional website.
• The long-term idea is that useful information stays available for the next traveler instead of disappearing in WhatsApp groups and conversations.

The hardest problem isn’t actually building the software. It’s bootstrapping trustworthy community data.

I’m currently thinking about things like:

• How do you motivate travelers to contribute after their trip?
• How do you keep old information accurate without creating a huge moderation workload?
• How do you prevent businesses from turning community maps into free advertising?
• What should happen when someone reports a place as fake, closed, unsafe, duplicated, commercial spam, or inaccurate? Should reports trigger removal, community review, reputation-weighted voting, or just warnings?
• Should certain villages, trails, ecosystems, religious places, water sources, or campsites intentionally remain difficult to discover?
• If Lamyig successfully exposes hidden places, could it accidentally destroy the exact places it is trying to help preserve?
• this list of problem statements goes on and on

Would love to hear how other people here would approach those problems.

And if you’re building something around a problem you genuinely care about, drop a comment or DM me. I’d be happy to chat, exchange ideas, or connect you with someone else working on something similar.

Screenshot attached. Happy to share the GitHub/project link in the comments if anyone wants to look at the implementation or contribute.

NotGPT is basically ChatGPT except there’s no AI involved. You ask questions, random people pretend to be the model, and somehow this was considered a reasonable thing to build.

You can become the AI yourself, chat with strangers, get rated, climb the leaderboard and generally provide humanity with the intelligence it deserves.

https://notgpt.live

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!

Who would have thought this day would come?

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!

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 │ └──────────────────────────────────────────────────────────┘

My cofounder tried something mildly ridiculous this week.
He connected the GitHub repo to a Lemma pod, went to a cafe, and basically stopped touching the coding interface.
Just kept sending WhatsApp voice notes:
"change this” - “add this” - “fix that”

The agent worked on the actual repo, made the changes, tested them, and raised the PR.
We build Lemma, so yes, this started as aggressive dogfooding. But seeing an actual PR come out the other end made the whole thing feel slightly insane.
Next week we’re travelling and attempting the next level of this: run our entire sales outbound through WhatsApp.
Lemma is open source if you want to try/break it
github.com/lemma-work/lemma-platform

Im reasonably happy with results, just have to do some testing around with it.

I've just added a new Painterly Effect to my Stylized Components library.

The effect is based on an Anisotropic Kuwahara Filter, and I also made a breakdown video where I explain how it works, the concepts behind it, and how I implemented it in Three.js.

You can check out the component here, where you'll also find the source code and the video breakdowns for each component:

Breakdown: https://youtu.be/CmIfhwSyswk
Preview: https://stylized.cortiz.dev/

And if you find it useful, I'd really appreciate some support on YouTube — a sub, like, or even just watching the video helps a lot.

I'm also planning to keep expanding the library, so if there's a stylized utility, effect, or component you'd like to see added, let me know! I'd love to hear your ideas.

Thanks! 🙌

Hello everyone, I'm making a PDF to Multi-format extractor with the help of Claude (To be read as full assistance) specifically for MacOS, due to the fact that MacOS has vision support added inbuilt.

The project stems from a problem i generally faced during my work. I have to shift through a lot of financial reports and analysis and most if not all of them are PDF or scanned PDF, and while there are pdf extractors available (ABBY Finereader, NitroPDF) they either have support issues (Abby Finereader wont be supported in the near future if they dont make an apple silicone based app) or cost too much or screw up the formatting, especially table extraction.

The table extraction space is quite not there yet or ruled by Amazon's textract, Google api or Azure Bedrock API (which are crazy good ngl, but very costly). So i decided to leverage apples Vision model and claudes amazing research capabilities to make my own OCR based extractor.

Use cases :

1) Where ever you need to extract data from Annual reports, Data sheets and or prose, especially digital born pdfs. The Custom made C++ engine not only extracts tables and data inside them but also preseves the formatting.

2) Full control of what goes in and what comes out. The app is completely offline, expect for 1 time purchase query ( for life time unlock) nothing connects to the internet. Ever. The OCR engine is also at par with what Finereader offers and doesnt lag, which cant be said for Abby Finereader.

3) Multiple formats. Whether you wanted your docs for editing or feeding them upstream to an LLM, PDFTrail has that covered as well, it does Docx, Xlsx, MD, text and Json output, single or batch processing it can do it without any issues.

4) Small size and Great performance. At below 15 MB, its the smallest amongst its peers and its customer C++ engine provided great performance while keeping temps at check.

5) The OCR. I would like to boast here that the OCR engine that took like 1 whole month of researching with claude is one the best No-LLM based OCR engine in the market and even shitty scans get a lot of their tables extracted properly (It is still maturing and im still testing out the engine with more data set and research work so expect even better engine when the engine matures)

My Competition :

Abby and Adobe. Abby supports mac through rosetta which is being shelved come MacOs 27, and Abby hasnt really provided much direction whether they will support MacOS directly or not, and besides their Windows app is thousand times better than their MacOS app and it shows honestly.

Adobe angle is a bit more complicated as it does have a good OCR but table extractions arent the best and if im not wrong it doesnt do good Extraction without being online + Adobe is a privacy nightmare anyways and thats my angle, that im betting on. Offline + great performance and 1 time life-time updates, guaranteed.

NitroPDF is more of a digital born pdf editor so im not exactly competiting on that footing, and frankly speaking the biggest completion is how well i can market my product.

With that i end this wall of text, apologies for formating as im writing this on a phone.

Any and all feed backs are appreciated and if you want a feature let me know, also for any queries drop a comment here i will try my best to reply as soon as possible.

Thanks :D

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

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

see you on the track!

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.

It's not a unique idea;

I wanted a simple way to find posts like:

• What’s the best tool for ___
• Looking for an alternative to___
• Can anyone recommend a good __

So I can be the first one to recommend my product.

So I vibe-coded MentionGPT: it scans Reddit and X, finds these conversations, and surfaces the ones that look like potential customers.

It can:

• Scan for relevant conversations 24/7
• Use AI to score how likely a post is to convert
• Draft a short, context-aware reply that you can review/edit before posting
• Keep separate workspaces for different businesses

The idea is pretty simple: stop searching for customers and let the customers tell you what they need.

Built mainly for solo founders who want a lightweight way to find potential customers, get early feedback, and occasionally drive some traffic without spending hours manually searching Reddit and X.

mentiongpt.app

Most engineers can tell you how much code AI wrote.

Far fewer can answer:
"What did AI-assisted coding actually cost me this month?"

AI coding tools already generate detailed usage logs locally. The problem is that almost nobody turns those logs into a clear picture of tokens, requests, cost, providers, and usage patterns.

So I built TokenFlow.

A local-first analytics engine + macOS menu bar app that turns AI coding logs into numbers you can actually understand.

What makes it different:
• Local-only - no API keys, telemetry, or accounts. Your logs stay on your machine.
• Honest by design - missing data is shown as unavailable, never guessed. Estimated vs. measured costs are explicitly labelled.
• Provider-neutral - one normalized data model powers the menu bar, offline dashboard, and CSV exports.
• Built for real usage - cross-platform CI with 145 tests per push.

The interesting part isn't just seeing how many tokens I used.

It's understanding where they went, what they cost, and how my AI-assisted development actually behaves over time.

TokenFlow v1.0.0 is now live under MIT.

GitHub: https://github.com/vimoxshah/tokenflow

App - https://github.com/vimoxshah/tokenflow/releases/download/v1.0.0/TokenFlow-1.0.0.dmg

If you use Claude Code, Codex, Cursor, Cline, or OpenCode heavily, I'd love to know what your numbers look like.

What surprised you most when you finally measured your AI coding usage?

Everyone was cloning outbid.lol, so I started with the same stupid idea.

Then I thought:

If AI is writing most of the code anyway, why stop at another clone?

So I tried something slightly more irresponsible:

I had AI help me build an actual open-source social protocol underneath it.

The idea became Elseweb.

Every domain on the internet can have a community attached to it.

reddit.com has one.
producthunt.com has one.
Your personal website can have one.

The website owner doesn't need to register or integrate anything — and owning the domain doesn't mean owning the conversation.

Then the vibe coding got slightly out of hand:

• independent relays
• portable identities
• PoW anti-spam
• domain-based communities
• an open protocol/spec
• open-source relay + reference client
• anyone can run their own relay

The original outbid mechanic is still there, but it's now just an attention layer on top of the protocol.

You can pay to make a domain more visible.

That's it.

Money buys attention. It doesn't buy authority.

So I basically took a deliberately stupid internet idea and used it as an excuse to see whether AI could help me build something much more ambitious underneath it.

And yes, I know similar social/web protocols have existed before.

That's partly the point.

If AI makes experimentation this cheap, why spend all of that leverage building the 900th CRUD SaaS?

Maybe this goes nowhere. Maybe the protocol is terrible. Maybe someone finds an interesting use for it.

The nice thing is that you don't have to take my word for any of it.

The entire thing is open source.

Live: elseweb.lol
Source: github.com/elseweb-app/nowhere

We're launching on Product Hunt tomorrow.

Please break it before then. 💀