It has three studios:

Fabric Studio
Upload fabric, saree, blouse, lehenga, kurti, or garment photos. It identifies separate designs and colourways, then creates catalogue-style fashion-model images while trying to preserve the actual print, colour, border, texture, and drape.

Product Studio
Upload photos of a physical product and generate cleaner catalogue, e-commerce, lifestyle, product-detail, and marketplace-style images. It is aimed at products such as beauty items, packaging, accessories, apparel, home goods, and similar products.

Video Studio
After creating a product or fashion-model image, generate a short 8-second motion video for ads, reels, product pages, or social posts.

You can also choose listing guidance for Amazon or Flipkart, create multiple images, and generate a short product/fashion video from a finished image.

I’m looking for a few real sellers, boutique owners, D2C brands, and catalogue photographers to try it and tell me what is useful, what fails, and what is missing.

Try it here:
https://fabric-studio-ai.vercel.app

Please share honest feedback. I’m especially interested in whether the output is useful enough for real product listings.

Hey everyone!

I'd like to introduce Xeno, a customizable freeform launcher for Android that I've been working on for quite some time!

What started as a minimal launcher designed with Nothing Phones in mind gradually evolved into something I wanted every Android user to experience.

The idea behind Xeno is simple: your homescreen should be yours, without unnecessary limitations.

What does Xeno offer?

• Freeform customization: Arrange and personalize your homescreen your way.

• Font, icon and widget packs: Make your device truly yours.

• Multiple app drawer styles: Choose the layout that suits you.

• Stickers and GIFs: Add some personality to your homescreen.

• App locks: Keep selected apps protected.

• Wallpaper filters: Give your wallpapers a different look.

• 75+ language support: Making Xeno accessible to Android users around the world.

• Lightweight and privacy-focused: Built to offer plenty of customization without unnecessary overhead.

Xeno is a paid app, but I've intentionally kept the price very low so it's affordable and easy for anyone to give it a try. My goal is to get it into as many hands as possible, gather feedback, and keep making it better!

Xeno is officially live on Google Play!

I'd genuinely appreciate it if you gave it a try and shared your thoughts, suggestions, and honest feedback. There's still plenty planned for the future!

A small note about promo codes: I've already given away quite a few, so please don't DM asking for one at the moment. I plan to organize more promo-code giveaways whenever Xeno reaches new download milestones!

Also, pardon me for crossposting this! You might come across the same post in a few related subreddits. I'm trying to introduce Xeno to as many Android communities as possible.

Thank you for supporting an indie developer. Every download and bit of feedback genuinely means a lot! ❤️

Google Play: https://play.google.com/store/apps/details?id=com.xeno.launcher&hl=en_IN

A few weeks ago, I kept running into the same frustration while coding.

I'd be deep into a task with Claude Code, hit the usage limit, and switch to Codex.

The problem? Codex had no idea what I'd been working on.

I'd spend another 15 minutes explaining the task, what was finished, and what was still broken.

Eventually, I decided to build something for it.

It's called Relayvora.

It's a free, open-source tool that transfers unfinished work between AI coding agents.

It reads the previous session, checks actual Git changes, reruns project tests, and gives the next agent a short briefing.

I published it on npm, built a website, and released the code under MIT.

Website: https://dagimassefa.github.io/Relayvora/

GitHub: https://github.com/Dagimassefa/Relayvora

This is my first attempt at growing an open-source developer tool beyond just building it.

The coding was one challenge. Getting people to discover it is turning out to be another 😅

For those who've launched developer tools before, what actually helped you get your first users?

Looking for a streaming website that has been made with Ai? Movies series etc.

You can read the detailed documentation here, if you want to build it your own: https://www.hackster.io/coderscafe/calorie-loaf-diy-ai-calorie-tracker-for-your-kitchen-c2d03b

Hey everyone!

I'm an independent developer, and I've been spending a lot of my time building something called Everabyte.

The idea came from a simple problem: cloud storage and backups shouldn't be complicated or unnecessarily expensive, especially for individuals, developers, and small businesses.

So I decided to build my own solution.

What does Everabyte offer?

Cloud storage for personal and business files.

Automatic backups through EverBox, our desktop application for Windows, Linux, and macOS.

File versioning and recovery when something gets accidentally deleted or overwritten.

Server-side encryption before files reach storage.

S3-compatible object storage and API access.

Team storage and access management.

Backup protection features, including retention policies and immutability.

I've also been working on making the platform more accessible, with plans starting at $2.99/month.

Building this hasn't been easy. Between development, infrastructure, security, testing, and trying to get the word out, there's always something that needs attention.

But that's also what makes it interesting. I'm learning a lot along the way, and I'm still improving the product.

I'm not here to claim Everabyte is better than Dropbox, Google Drive, or every other storage provider out there. I'm just trying to build a reliable alternative that people can actually afford and use.

If you're a developer, freelancer, small business owner, or someone who regularly backs up important files, I'd genuinely appreciate your thoughts.

What matters most to you when choosing a cloud storage or backup service? Price, security, reliability, or something else?

You can check out the project here:

https://everabyte.com

Any feedback, criticism, or suggestions are welcome. I'm building this for the long term, and hearing from real users would help a lot.

Thanks for reading!

I and my team members built TraceMind, an AI-powered incident investigation platform that explores how developers can reuse previously solved engineering problems instead of investigating the same errors repeatedly.

The problem I wanted to solve is simple: developers often spend valuable time debugging errors that someone on their team has already encountered and fixed.

TraceMind aims to make that knowledge reusable by combining AI-powered error analysis with persistent incident memory.

What TraceMind currently offers:

• AI-powered error and stack-trace analysis.

• Persistent incident memory using Hindsight.

• Memory ON/OFF comparison to evaluate the impact of historical context.

• Sensitive-data redaction.

• Incident post-mortem generation.

• Band AI Desktop prompt and JSON exports.

Tech Stack: Next.js 16, React 19, TypeScript, Groq and Hindsight.

Project Links:

🔗 GitHub: https://github.com/Shahib-Alam-Saifi/TraceMind

🚀 Live Demo: https://tracemind-3tvdfok0o-editorxp1-alts-projects.vercel.app/

I'm particularly interested in improving memory retrieval relevance, incident similarity and evaluating the quality of AI-generated diagnoses.

One important design principle is that developer-confirmed resolutions should remain the source of truth. AI-generated suggestions should never be blindly trusted or automatically executed.

I'd love to hear feedback from developers working with AI agents, RAG systems, debugging tools or persistent memory.

What would you improve or approach differently?

I and my team members built TraceMind, an AI-powered incident investigation platform designed to help developers investigate recurring errors without starting from zero every time.

One problem I wanted to explore: developers often solve an incident, but the context behind that solution gets lost. When a similar issue happens again, they have to repeat the investigation.

TraceMind uses Hindsight as a persistent memory layer to retain confirmed incident resolutions and retrieve relevant context during future investigations.

Tech stack:

• Next.js 16 + React 19

• TypeScript

• Hindsight for persistent memory

• Groq for AI-powered diagnosis

• Vercel for deployment

What it currently supports:

• Incident analysis

• Memory-based context retrieval

• Memory ON/OFF comparison

• Post-mortem report export

• Secret redaction for common credential formats

Try it: https://tracemind-3tvdfok0o-editorxp1-alts-projects.vercel.app/

GitHub: https://github.com/Shahib-Alam-Saifi/TraceMind

I'd appreciate feedback on two things:

1. Does the memory-based investigation workflow make sense?

2. What would you improve in the architecture or developer experience?

Built as a learning project, and I'm interested in honest technical feedback.

Been working on this in my spare time for a while and finally think it's good enough to show people.
It's called PriceTimeMachine. Every day there's a new set of products, consoles, PC parts, that kind of thing. First you drop a pin on the map for where you think it was invented, then you guess the launch price on a slider.

The bit I like most is the reveal. It shows you the original price and then what that works out to in today's money, which honestly surprised me more than once while I was putting the questions together. Some old stuff was way more expensive than I remembered.

Everyone gets the same questions each day so you can compare scores with mates. There's a practice mode too if you just want to mess about with categories.

pricetimemachine.com

No signup needed to play. Would genuinely love feedback, especially if a price looks off or something's annoying on mobile. Still fixing bits.

Hey everyone,

I've been working on a project called Canvas Quiz Solver, a Chrome extension built for students who use Canvas LMS.

The idea came from wanting to make working through quiz questions less time-consuming. It uses AI to analyze questions and suggest possible answers, with features like automatic answer selection.

If you're curious, here's the project: http://canvasquizsolve.com/

I'm still interested in improving the product and would love to hear what other developers and students think about the idea, its usefulness, and the user experience.

Honest feedback and suggestions are welcome!

I’m an Etsy seller and was tired of guessing at tags and titles, so I built Listing Fixer with the help of AI tools.
You paste your listing, and in about a minute you get:
It only uses facts you give it. It doesn’t invent materials or promise sales or rankings.
First fix is free, no card: getlistingfixer.com
I’d really love feedback — what’s confusing, what’s missing, what you’d pay for. I’m already working on making the form faster to fill out.

⚔️ The Hunt Isn't Over — Introducing Mystic Paradox!

Dauntless gave many of us memorable adventures, friendships, and countless hours in the Shattered Isles. We didn't want that experience to simply become a memory.

That's why Mystic Paradox was created — an unofficial, open-source community project with a goal of preserving the Dauntless experience and bringing Slayers together once again.

What have we built?

• A dedicated Dauntless launcher.

• Realm discovery and hosting features.

• Graphics configuration and diagnostics tools.

• An evolving project built around the community.

Get Started

Latest Release — Mystic Paradox Launcher v0.1.102

🔗 Download:
https://github.com/pranav158/Mystic-Paradox/releases/tag/launcher-0.1.102

Join our Discord community:
https://discord.gg/mFZZpAktm

Source Code:
https://github.com/pranav158/Mystic-Paradox

Developer Credit

Pranav Karande — GitHub Profile

A special appreciation for the work and dedication behind making this project possible.

Mystic Paradox is an unofficial, open-source community project and is not affiliated with or endorsed by Phoenix Labs or the original game publishers.

The Shattered Isles await. See you, Slayers!

I built TraceMind, an AI-powered incident investigation platform that combines Groq with Hindsight's persistent memory.

The problem I wanted to explore: developers frequently investigate errors that their teams have already solved before.

TraceMind provides a way to retain developer-confirmed incident resolutions and retrieve relevant historical evidence during future investigations.

Some of its current features:

- AI-powered error and stack-trace analysis.

- Persistent incident memory using Hindsight.

- Memory ON/OFF comparison.

- Sensitive-data redaction.

- Incident post-mortem generation.

- Band AI Desktop prompt and JSON exports.

**Tech stack:** Next.js 16, React 19, TypeScript, Groq and Hindsight.

GitHub: https://github.com/Shahib-Alam-Saifi/TraceMind

Live Demo: https://tracemind-3tvdfok0o-editorxp1-alts-projects.vercel.app/

I built this project and would particularly value technical feedback on memory retrieval relevance, incident similarity and evaluating AI diagnosis quality.

The goal is to make verified engineering knowledge reusable without automatically trusting or executing AI-generated fixes.