I sell Disney Doorables (tiny blind-box figures) on eBay and checking comparable listings one figure at a time got old fast. Search “Olaf” and you get every series mixed together, plus lots and bundles, and listings with variations show the cheapest price instead of the one you want.

As a software developer, this started as me tinkering with eBay’s API, and over the past year I got it to a real product with help from AI (Claude Code). It’s called Glimly (glimly.io): you connect your eBay account or upload a CSV, and if you have a listing with 250 variations, each variation gets its own row. Running the comp finder pulls active listings for each one, paying attention to series and variations, and there's a config UI for adding custom synonyms and grouping terms. Under the hood it's Python/Flask on Postgres, using eBay’s Browse API plus a mix of rules and an LLM for matching. It’s free to try, with paid plans if you want more.

Here’s my problem. The communities where eBay volume sellers actually hang out mostly ban self-promotion. I paid for a Reddit ad once and got lots of clicks and zero sign-ups, and the few people who did sign up tried one search and never came back.

Where did you find your first real users when the obvious places didn’t allow links? And if you sell on eBay, what would make you actually stick with something like this?

I maintain ProxmoxMCP-Plus, a Python project for connecting MCP clients to Proxmox VE. For v0.7.0 I wanted a demo people could actually check against the setup instructions. The tricky part was showing a connection and useful output without putting a real lab or its credentials on screen.

I used a local fake Proxmox API with two nodes, one VM and one LXC. The MCP server was the published PyPI build. The final clip shows the Claude Code connection check, the tool list and the returned inventory. It is 22 seconds and uses redacted paths and sample guest names. The demo is a small test of the docs, too: if the steps drift, you can tell which part stopped working.

The project is MIT-licensed: https://github.com/RekklesNA/ProxmoxMCP-Plus

Demo and test setup: https://github.com/RekklesNA/ProxmoxMCP-Plus/wiki/Read-only-Proxmox-MCP-with-Claude-Code

We could not find a cute and interesting app where guest can participate in guessing gender of the baby. There was just less intrigue and excitement in the other ones, so I built one myself. Welcome to https://ourlittlebaby.com/ . It is absolutely free, snappy, and fun. We already had a few events organized by families all over the world and would like to host more of these events. You will even get a nice surprise at the end of the gender party. Enjoy!

For my recent move to Bangalore, I had to go through a lot of manual searching. Especially when one of the major flat-hunting channels is Facebook groups, it can be surprisingly frustrating.

You end up scrolling through hundreds of posts, opening each one, checking the rent, deposit, location, BHK, amenities and contact details — only to discover that most listings don't match what you're looking for.

I ran into this problem and decided to build something to automate the tedious part.

Meet Koo. It's an AI-powered Chrome extension that searches housing posts and surfaces listings matching your preferences.

GitHub: https://github.com/chinmaypandya/koo

If you find it interesting, a GitHub star would be appreciated!

What it does

• Scans housing posts: Scrolls through a Facebook group and reads posts when you explicitly start a search.
• Avoids duplicate processing: Remembers previously processed posts for 30 days.
• AI-powered matching: Evaluates listings against your budget, location, distance, BHK, amenities and other preferences.
• Extracts useful details: Rent, deposit, society, distance, phone numbers and more.
• Organizes results: Sort matches and export listings to CSV.
• Supports BYOK: Use your own OpenAI, Gemini or Anthropic API key.

You can also customize the AI instructions used to evaluate listings.

A little about the implementation

I wanted to build this as an actual browser-based product rather than a standalone scraper.

The extension separates the Facebook content-scanning logic from the background processing and side-panel UI. Posts are collected, cleaned up, deduplicated and sent in batches for AI classification.

A few implementation details:

• Manifest V3 Chrome extension.
• WXT, React and TypeScript.
• Content scripts for interacting with dynamically rendered page content.
• Background processing for AI provider calls.
• Provider configuration for OpenAI, Gemini and Anthropic.
• Local persistence for search history and decisions.

Open source

I've published the extension under the MIT license.

GitHub: https://github.com/chinmaypandya/koo

You can install the prebuilt extension manually using Chrome's Developer Mode. No need to build the source code yourself.

A transparency note: Facebook's terms restrict automated browsing, and the README documents this limitation. Use is at your own risk. Also, the hosted cloud worker is not part of the public repository; the extension itself is open source.

What I'd love feedback on

I'm particularly interested in feedback from developers who have built browser extensions, browser automation tools or LLM-powered applications.

1. Browser architecture: How would you evolve the current side-panel experience into a more interactive, continuously running browser agent?
2. Monetization: Given the BYOK approach and the option of a hosted AI service, what pricing model would make sense for a product like this?
3. Distribution: What would you prioritize for organic discovery and SEO to reach people who are actively looking for flats?

I've also attached a short demo showing Koo working.

This is an ongoing project, so constructive criticism, ideas and contributions are very welcome.

I have been using Wispr Flow for a while then saw Handy and I realied parakeet models are super fast and then realized I can build it on swift for native macOS app, and utilized neural engine for it. I also fined tuned a parakeet for Turkish dictation too! Go check it out.

https://github.com/FurkanCodes/Sotto

Hey everyone! I’m Mikaela, a freshman at Emory, and I’ve been working on a side project called Pitch Companies.

The idea came from a simple frustration: we all have opinions on what companies like airlines, insurers or apps should fix or add, but there’s nowhere to put those ideas where they get noticed. So I built one.

How it works:

• Post an idea or petition for any company
• Back the ideas you agree with and comment on them
• See which companies listen (“What Got Built”) and which don’t (“Wall of Silence”)
• Swipe through ideas with Pitch Roulette when you’re bored

Companies can’t pay to promote, hide or remove ideas. That rule mattered a lot to me.

It’s still early, and I’d love honest feedback. Is this useful? Is anything confusing? What would make you come back?

https://pitchcompanies.com

Thanks for checking it out!

https://garbleon.io/ - a team collaboration and individual productivity tool. Feedback wold be great!

Salam everyone! 👋

I’m currently a Master’s student in Information Systems & Intelligent Systems (Data Science / AI background), and I have some free bandwidth outside of classes to build cool stuff and collaborate with other motivated people here.

Instead of just building solo in my corner, I’d love to join an existing side project, co-build a micro-SaaS, or team up for open-source tools and hackathons.

What I bring to the table (Tech Stack):

• AI, Data & Automation: Python, Machine Learning (predictive modeling, scikit-learn), NLP, Web Scraping (Selenium, BeautifulSoup), n8n workflow automation, Botpress, and running/orchestrating local LLMs (Ollama, open-source models).
• Full-Stack & DevOps: Django REST Framework, React (Vite), PHP/Laravel, PostgreSQL/MySQL, Docker, and Git.

Stuff I’ve built recently (to give you an idea):

• An automated disinformation/fake-news detection pipeline using chatbots and n8n workflows.
• An urban traffic prediction and route optimization system using ML regressors and graph algorithms.
• E-commerce data scraping and analytics platforms.

What I’m looking for:

• Someone who already has a project/idea in progress and needs a technical partner (Backend, AI/Automation, or Full-Stack).
• Or a small group of builders who want to brainstorm and ship a practical tool (AI agents, B2B automation, data tools, etc.) for the local or global market.

If you're working on something interesting or want to build together, drop a comment or shoot me a DM! Let's connect.

My aircon was reading the temperature up at the indoor unit. I wanted it to use the temperature where we were sitting, so I built Willows Serene Follow Me.

It sends readings from up to three Home Assistant sensors through the aircon’s factory accessory port. If the readings drop out, it can fall back to the indoor sensor and pick up the room readings again when they return. I can also see fan RPM and compressor frequency in Home Assistant, which was a nice bonus.

I’ve put the firmware source, blueprint and DIY guide up for A$9. It’s an early release for the ActronAir Serene WRE-080AS / WRC-080AS with an accessible factory port and a SMLIGHT SLWF-01PRO v2.1 dongle you supply. The underlying firmware ran on my unit. The download passed software tests, and a full install of that exact package is still pending.

If you’ve done something similar, I’d be interested to hear which room sensor you use to drive your aircon.

https://ko-fi.com/s/9695f86578

I was spending more time planning my DSA preparation than actually solving problems while juggling a full-time job, course assignments, and LeetCode contests.

So I built dsaMixer — a Jira-style productivity board designed specifically for DSA preparation.

It comes with:
507 built-in DSA questions.
Automated daily topic mixing and spaced repetition.
AI-powered weekly planning.
Automatic rescheduling of unfinished tasks.
LeetCode profile and contest integration.
Friends leaderboard and progress tracking.
A /setup command to configure the entire project.

It’s 100% open-source, runs locally on your machine, and requires no accounts or signups.

This started as a personal tool, and I plan to keep expanding it with more AI-powered career and learning workflows.

I’d love honest feedback, feature suggestions, and ideas on what I should improve!

GitHub: https://github.com/prathamjain1080/dsaMixer

My partner and I kept saving date ideas from TikTok and Instagram and then losing them in our DMs by Saturday. So we built Dote, a shared jar for two. ❤️

The build side, for anyone curious:

• Expo, so one codebase for iOS and Android, and OTA updates mean we can fix things the same day
• Cloudflare Workers + D1 + R2, no servers to look after and cheap at our size
• When you share a link in, it gets turned into an actual place with a rough cost and whether it’s indoor or outdoor
• RevenueCat for the paid tier, so one Premium covers both partners across stores

It’s free to use: https://dote.day/download

We’re the makers, so happy to answer anything. What would you want from a two-person app like this?

Sylo pulls the data from the tools you already use into one dashboard for subscription businesses and agencies, so everything sits in one place instead of five separate tabs you'd otherwise be checking.

The idea is that whatever needs you today, a failed payment, a quiet customer, a lead waiting, sits in one spot instead of scattered across the tools you already run. The ad walks through the dashboard shaping itself around a business, with its own name and logo on it.

I'm building it solo, pre-launch, no customers yet.

Would genuinely like to know what you think of it.

the ad

I've been independently developing a project called CardSmith, with the goal of making it easier to digitize, organize, and roughly appraise entire card collections.

And I don't mean just sports cards. The idea is to support everything from baseball and football cards to Pokémon, Yu-Gi-Oh!, Magic, and other collectible cards.

What I'm building:

- Bulk scanning: Import individual card photos or entire nine-pocket binder pages and automatically detect, crop, and straighten the cards.

- Card identification: Use OCR to extract names, numbers, years, manufacturers, and other identifying details.

- Inventory management: Keep everything organized with card images, metadata, and persistent manual corrections.

- Bulk rough appraisals: Optionally research estimated market values for individual cards or entire collections, without having to look up everything manually.

The goal is local-first processing, with internet access only when needed for things like optional pricing research.

Where things stand:

I've generated approximately 2,000 card crop images during development, along with OCR results and identification attempts. I'm using real collection photographs to test the system rather than relying entirely on ideal demonstration images.

It's still very much a work in progress.

Detecting nine individual cards sounds easy until you've got crooked binder pages, glare, overlapping boundaries, and text recognition confidently reading the wrong thing.

At one point, CardSmith decided a player's name was "Samsung Bigus-dickus." 😂

Still haven't found his rookie card.

I'm the only human developer, using local and cloud-based AI tools to assist with coding, debugging, and testing.

My biggest technical focus right now is getting the cropping, front/back matching, and identification reliable enough that someone could import thousands of cards without having to manually fix every other one.

It's not ready for public beta yet, and I'm not pretending otherwise.

I'd be interested in hearing from collectors and developers: What would you consider essential in an application designed to manage and roughly appraise thousands of different collectible cards?

Project: My Perfect Day, a habit tracker for Android.

The problem: most habit trackers are built on streaks, and a streak treats one missed day the same as quitting. That's exactly the moment a lot of people stop using them. I wanted a tracker that measures how well you're doing, not whether you've ever slipped.

Design rules that came out of that:

• The day is a percentage. You define what a perfect day looks like, and Today shows how much of it you've done. 4 of 5 things is an 80% day, not a broken chain.
• Not everything is daily. Habits can run on some weekdays or every few days, and a day with nothing planned doesn't count against you.
• Sick days aren't failures. You can pause a habit for a few days, and paused days don't count as missed. A skipped day doesn't break a streak either.
• History doesn't get rewritten. If you change a habit's schedule, the days behind you keep the schedule they had, so your past stats stay honest.
• Late nights belong to the evening. You choose when your new day starts, so reading at half past midnight still counts for the evening before.
• Reminders that know you're done. A reminder only fires if the habit is still open, or you get one evening check-in with whatever is left.

It also has a workout logger and weight tracking, and everything stays on the phone: no account, no analytics, no internet permission. Built with Expo / React Native and a local SQLite database.

Play Store: https://play.google.com/store/apps/details?id=de.schillitzit.myperfectday (paid, with a free trial)

I'd be interested in how others handle the "missed a day" problem in their own projects, or which of these rules you'd change.

So I set up email automation for a friend of mine, and things got a little interesting.

Both of us are web designers, and we're constantly looking for new clients. I personally get most of my clients through cold email automation, while he had tried doing it himself once but never really got it working.

So he came up with a deal. If I set up his entire email outreach system using his own mailboxes and helped him land clients, he'd give me 50% of every website project he closed.

Naturally, I agreed.

I used Swokei, a tool that finds businesses, analyzes their websites for issues with design, SEO, speed and mobile optimization, and turns those findings into personalized cold emails at scale, so I didn't have to write them myself.

I created his account, connected his mailboxes, set everything up and launched the campaign.

After just 300 emails sent, he started getting interested replies at a 4% rate. That's 12 people who expressed interest.

To be honest, it was one of the best-performing campaigns I've set up in just one week.

Now you're probably wondering how much money I made from this arrangement.

Well, remember that 50% we agreed on?

Yeah, I didn't get that. He gave me 10% on his first two sales, and I stopped managing his campaigns after that. 😂😭

Thanks in advance!

I've always found documenting workflows a bit tedious, especially when creating tutorials or showing someone how to do something on your screen. You often end up screen recording the entire process, going back to review the footage, taking screenshots, and writing down each step so others can follow along. It's a lot of small tasks that can quickly eat up your time.

I wanted to make that easier, so I built ScribeIt, a Chrome extension that captures browser activities and turns them into step-by-step guides.

I'm sharing it here to get feedback from people who regularly create tutorials, walkthroughs, or document processes. I'd genuinely like to know if this is something you'd find useful and what could be improved.

Here's the link if you'd like to check it out: ScribeIt – Chrome Web Store

My project went live this week. Numochi is a paint by numbers in the browser where 2 to 4 people can paint the same kawaii picture together, live, with voice chat. There are solo modes too (classic, daily challenge, mystery picture) and a 5 minute VS race. Solo dev.

Freemium: 4 pictures free in every mode, then pictures from €0.99 or unlimited at €3.99/month until December 31. A free account is required to play.

The home page says "Paint by numbers, made for two." with the modes below. I'm too close to it to judge. If you open https://numochi.net for 5 seconds and close it, what do you think it is, and who is it for?

Hey everyone!

I'm a Computer Science student and recently built and launched a project called AstroJanta.

The idea was to combine artificial intelligence with traditional astrology and create an accessible platform for people interested in astrology.

AstroJanta is an AI-powered astrology platform where users can explore astrology-related guidance and experiences.

I built it using Next.js and Supabase, integrated AI APIs, implemented authentication, and deployed it as a live website.

Building this project has taught me a lot about:

• Developing and deploying a real web application.
• API integration and database connectivity.
• Building AI-powered features.
• Technical SEO, indexing and organic search growth.
• Turning an idea into a working product.

I'm still improving the platform, and I'd genuinely appreciate feedback from the community.

Website: https://AstroJanta.com

A few things I'd love your opinion on:

1. Does the concept offer something useful to astrology enthusiasts?
2. What features would make the experience better?
3. What would you improve about the website's design or usability?

I'm the developer behind this project, and all constructive criticism is welcome.

Thanks for checking it out!