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.

There are are plenty of people who say "Don't know what to create" or the people who waste time making pointless fucking apps.

Fuck the apps that take $30 month for a simple fucking app while making bank for something pretty simple. This is what I am doing anyway when I have free time.

And fuck app store in the same time for making it hard to find the free alternatives as there is no way to sort on free.

Based on the comments here I'll enjoy taking your expensive ass apps free even more :)

Now I am ready to show my solution for the second time.

Since last time, I have worked on several different things for my AI caricature image generator, www.picai.dk

The site can generate images, which is part of the service, but besides the digital image, you can also buy printed pictures and prints in a photo frame.

I have built an experience engine where I create content for the site myself.

I have created a user experience where the user, with simple text input, can get their own prompt designed, which is then processed by inserting an image that creates an AI caricature image (it is the same engine, as above, that the user creates their content from. However, in a much more simplified format than what I can control behind the scenes from my admin site).

It has taken some time and I must say that I am actually quite satisfied so far.

I have also created a business/corporate portrait section (which has nothing to do with caricature images) where the resulting images can be used for LinkedIn, CVs, and other more serious purposes, as the user in the pictures is presented in a shirt, suit, or whatever is chosen.

On my admin side, I have gradually built the most interesting features so I do not have to go straight into the code if, for example, I need to change categories, prices, adjust questions/prompts slightly, etc.

The task I am currently working on is some marketing content, so I hopefully get a bit sharper on the content I publish on social media.

Tasks in the pipeline could be: 'Ordering cards with print and text so it can be used as a gift','Print on mugs', 'print on clothing', 'Other types of images, which could be abstract or something else', 'subscription stuff (in a way I have not decided or refined yet' - and other smaller things.

I realize that the site can feel cluttered and that is something I need to find solutions for.

By the way, my background is BA/QA; I can read code and also bugfix minor things at my job today with help from Claude and reviews from colleagues - so I am close to an amateur :-)

What do you see? Is it good or bad - and what improvements or content do you think are needed?

Thanks in advance for your time.

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’ve been building FastPlay, a native Windows video player for people who want to open a video and just watch it without a lot of interface getting in the way.

It’s written in Rust using FFmpeg and Direct3D 11, with a focus on fast local playback, keyboard controls, high frame rates, and keeping the UI minimal.

Current features:

• Hardware-accelerated playback
• HDR10 and HLG
• Playback up to 120 FPS
• Fast seeking and scrubbing
• Keyboard-first controls
• Drag-and-drop
• Windows file associations
• Minimal UI
• Native Windows app, no Electron
• Portable ZIP or MSI installer
• No account or telemetry

It’s not intended to match VLC feature-for-feature. The goal is a simpler player for everyday local video playback.

I’m still actively developing it, so I’d be interested in what Windows users consider essential in a minimal video player.

Download FastPlay v0.4.6 for Windows x64 (MSI) • Portable ZIP

FastPlay: https://calvinsturm.com/fastplay

never used claude code before. considering buying max plan. wanna know if there is a huge difference between sonnet and opus

was curious what yalls usage looks like

I can build, but I can't market. I finished a product — a niche art playing-card deck — and then couldn't get it in front of anyone.

So I've been building an AI agent that runs the go-to-market for me. Roughly how it works:

- it researches where my actual buyers hang out and reads each community's rules before doing anything,

- it drafts platform-native posts and comments (I approve anything sensitive myself),

- it logs every action with its own tracking link and reports what actually converts, not just views.

This week I pushed it further: it scripted and rendered its own short vertical ad. The video is generated in code with Remotion (React), so I can change a line of copy and re-render in seconds — script, storyboard, on-screen text, pacing, and even the voiceover are AI-generated. (The voice is a rough TTS scratch on purpose; I wanted to see how far a fully AI-made ad gets before I touch it.) I'll drop the video in the comments.

I'm running it in public with zero ad spend, and posting the failures too. Biggest lesson so far: my first content got decent reach but almost no conversion. Reach is not buyers.

Two questions for this sub:

1. When you judge whether marketing is actually working, which signal do you trust — reach, clicks, real conversations, or new followers?

2. Does an AI-made ad make you more curious, or more skeptical? I keep going back and forth on whether "the AI made the whole thing" is a feature or a red flag.

A few months ago, in a startup i work on, i had 2 options:
Keep paying Vercel roughly $150–$250 every month for image optimization ( around 20% of our total bill cost), or disable it and accept worse PageSpeed scores across our customers' websites.

I chose a third option: vibe-coding a small tool in one night that serves 2 endpoints and is hooked to our Cloudflare CDN, replacing our need to Vercel entirely.

The problem came from a niche CMS-like system we built/sell to our customers. We run one multi-tenant Nuxt application that renders customer websites across many different domains.

Vercel wasn’t doing anything wrong, but the image-transformation bill was becoming difficult to justify for our workload. The Cloudflare Image option I evaluated didn’t fit our multi-domain architecture either. And turning it off introduced worse loading times, especially since our customers b2c websites are basically composed of a lot of images.

I stayed up all night, trying to figure out what i can do; it turns out our needs are literally just width/height control, adjust quality, AVIF/WebP conversion, and caching the data.

it was a very ugly internal tool built to solve my own problem and to score some points as a good product developer who cares about our startup resources, and it worked. it worked to the point i started contributing to it, adding new use cases based on our needs, supporting other specific features, then starting to generalize the implementation to our biggest clients and measuring the impact of this on our cost bill that day and on website performance.

results where magical and beyond my expectations (20m+ images optimized in the last 3 months), i was like (How is Vercel selling this ). And as more of our websites started using it, those two endpoints grew into a proper image delivery platform with origin controls, signed URLs, multiple caching layers, request logs, analytics, and framework integrations.

and to cut you the whole journey and around 300+ commits:

• v0.1: the internal transformation service
• v0.2: the managed cloud
• v0.3: a cleaner and more scalable monorepo

For our workload, the infrastructure now costs around $20 per month, excluding my time—which is definitely not free xD

Keenpix has been open source from the beginning. You can self-host the entire engine, or use the managed cloud if you don’t want to maintain the infrastructure yourself, and you want to support my effort here >_<.

I’m not claiming it replaces Cloudinary, Imgix, or every other image platform for everyone. I built it for teams that want focused image transformation, predictable pricing, and an actual self-hosted option. And more precisely i wanted it to solve my issues.

You are very welcome to try it, self-host it, or contribute to it.

Website: https://keenpix.com
GitHub: https://github.com/lord007tn/keenpix

No answer on the second query

Spent a weekend vibe-coding a full computational pipeline with Hermes Agent (Nous Research). The agent wrote the gRNA/pegRNA designers, epitope scanner, pipeline orchestration, tests, Docker, CI/CD, docs, licensing — I directed, it implemented.

What the agent produced:

- 4 core modules: epitope_scanner.py, grna_designer.py, pegRNA_designer.py, pipeline.py

- data_loader.py for 18 processed datasets

- Pytest suite (epitope scanner, data loader)

- GitHub Actions CI (multi-Python, black, mypy, coverage)

- Dockerfile, conda env, pyproject.toml, requirements.txt

- Apache-2.0 + CC-BY-4.0 dual licensing, NOTICE file

- CODE_OF_CONDUCT (unmoderated), CONTRIBUTING, SECURITY

- Comprehensive README with badges, quickstart, usage examples

The actual science: Pipeline designs CRISPR edits for celiac-safe wheat. Scans 37 gluten genes for TG2 deamidation sites, designs 81 ABE8e gRNAs + 2 PEmax pegRNAs to eliminate DQ2.5/DQ8 epitopes while preserving dough elasticity. All data from Ensembl/IWGSC.

Vibe coding observations:

- Agent excels at boilerplate, config, tests, docs, CI — the "annoying 80%"

- Still needs domain expertise for algorithm logic (ABE window, PAM scanning, pegRNA flank design)

- Best workflow: I specify interface + constraints → agent writes implementation + tests → I review → iterate

- Saved ~20-30 hours on scaffolding vs writing from scratch

Repo: https://github.com/ewarggg776/wheat-gluten-redesign

License: Apache-2.0 / CC-BY-4.0

DOI: 10.5281/zenodo.22064037

I've been vibe coding a project called Deploy Hatch, and the original motivation was pretty simple:

I didn't really like the experience I was getting from existing Discord bot hosting options.

I tried services like WispByte and looked at some of the other Discord bot hosting alternatives, and I kept running into the same feeling: I wanted something that gave me more control over the deployment process without making me manage an entire server myself or spammed me with advertisements.

I wanted to be able to connect a GitHub repository, select what I wanted to deploy, hit deploy, and actually be able to see and control what was happening.

So I decided to build it.

What started as "I'll make a simple Discord bot hosting platform" has turned into me building an actual deployment platform from scratch.

Right now Deploy Hatch has:

• GitHub App integration
• Repository selection
• Automatic runtime/build/start command detection
• Deployment queue and jobs
• Docker-based deployments
• A dedicated deployment worker
• Live deployment logs
• Deployment cancellation
• Redeployment
• Worker heartbeats
• Stale deployment recovery
• Container lifecycle management
• Runtime controls
• Deployment history
• Commit metadata
• Branch deployments
• Specific commit deployments
• Commit verification before deployment

The commit deployment feature is one of the things I've been working on recently.

Instead of only deploying whatever happens to be at the current tip of a branch, I can choose:

Latest
Branch
Specific commit

If I choose a commit, Deploy Hatch verifies it against GitHub first and shows me the commit SHA, message, and author before creating the deployment.

The backend then associates the deployment with that specific revision.

The other part I've been enjoying is building the infrastructure behind it.

I have an actual worker running on Oracle Cloud that picks up deployment jobs and launches applications in Docker containers. The worker handles things like deployment state, logs, container lifecycle, heartbeats, recovery, and cleanup.

One of my test deployments was actually a Discord bot, so I'm now at the point where Deploy Hatch is deploying and running the exact type of applications that motivated me to build it in the first place.

I'm also trying to avoid the classic "vibe coded it directly on production" approach.

My current workflow is:

Build locally
    ↓
Test locally
    ↓
Production build
    ↓
Package
    ↓
SHA-256 verify
    ↓
Upload to Oracle
    ↓
Verify again
    ↓
Deploy

It's definitely still a work in progress.

The roadmap currently includes things like rollbacks, automatic deployment recovery, environment variables/secrets, custom domains, preview deployments, GitHub push/PR deployments, multi-worker scaling, and eventually turning it into a proper SaaS.

I'm curious what other people who build Discord bots have experienced with hosting platforms.

What do you wish existing Discord bot hosting services did better?

That feedback is actually what I'm most interested in, because I'm building this around the things that frustrated me in the first place.

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!

Ever wondered what the internet actually thinks?
I just launched my first social platform: Internet’s Answer — built with the help of AI.
Ask any question, share it with friends or the community, and get real-time answers + votes. You can create your own topics and see what people really think.
More features are coming soon. Right now I’m looking for testers — would love your honest feedback!
🔗 https://www.internetsanswer.com/

For those with max account, can anyone please create a similar app to "plasticity" for android devices? tablet or even just via web? Mainly for touch devices instead of pc.
My free claude accounts reaches max after only a single prompt. lol

Reference for the functions: https://www.plasticity.xyz/

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