r/indiehackersindia

Problem

Windows Search has always felt too limited to me.

It can open apps and sometimes find files, but when I actually want to search my PC properly, it usually falls apart.

I want to search and use features like:

- Text inside files, code, and images

- Browser bookmarks and history

- Clipboard history

- Git commits

- Windows settings

- Local commands

- Local agents for Windows

Windows Search is not powerful enough for this workflow.

So I Built OmniSearch

OmniSearch is a fast, lightweight, local-first Windows launcher that opens with:

"Alt + Space"

You can also set your own custom hotkey.

It gives you one search box for your PC.

Instead of only searching apps or basic file names, OmniSearch can search across:

- Apps

- Files and folders

- Content inside files, supporting 50+ extensions

- Image OCR text

- Browser bookmarks and history

- Clipboard history

- Git commits

- Windows settings and Control Panel pages

It also features an AI agent powered by Hermes and includes a powerful clipboard manager that gives you features no other Windows clipboard manager provides.

The goal is simple: Find everything on your PC from one shortcut.

Why is OmniSearch better than Windows Search and other popular launchers?

- Free and open source

- Local-first

- Lightweight

- Designed to run easily on low-end Windows PCs

- Image OCR text search

- Blazing-fast search of content inside files, supporting 50+ extensions

- Blazing-fast search over centralized PC history, including browser history, Git commit history, clipboard history, and file history

- Hermes agents for local Windows tasks and long autonomous tasks

Links

Free and open source.

GitHub: https://github.com/PranshulSoni/omnisearch

Website: https://omnisearch-windows.vercel.app/

Feedback

I am currently maintaining OmniSearch, and honestly, I cannot find and fix every bug alone because building a launcher like this on Windows is genuinely hard.

I would love feedback from people who use Windows every day.

If OmniSearch solves a problem for you too, please consider leaving a star on GitHub.

If you have ideas, find bugs, or want to improve something, feel free to open an issue or contribute to the project.

Your feedback is always appreciated.

Most "AI reviews your PR" tools annoy me. They hallucinate nits, the output changes every run, and you can't tell if a flag means anything. So I wanted to know if you can get a useful review signal the boring way. Static metrics plus git history, without any LLM in the loop, so same input gives the same output every time.

The question I actually wanted answered: do deterministic code-health metrics predict which files get bug-fixed later?

How I set it up :

• Score every file at a historical commit (T0).
• Count bug-fixing commits to that file over the next 6 months.
• Correlate the T0 score with the bug-fixes that came after. No file sees its own future.

Metrics are the usual suspects plus churn signals. McCabe complexity, deep nesting, LCOM4 cohesion, god classes, clone detection, function-level churn, code age, ownership spread, change entropy. 25 of them, combined into a 1-10 per file. I ran it across 21 OSS repos, 9 languages.

What came out:

• Mean ROC AUC 0.74 at picking the files that go on to get bug-fixes. Up to 0.90 on some repos, weaker on others.
• It survives controlling for file size (partial Spearman -0.16). So it's not just "big files have more bugs," which was my first worry.
• Out-ranks churn alone by about +0.10 AUC, and prior-defect history by +0.12.
• Held on an external dataset I never touched (PROMISE/jEdit), AUC ~0.77.

I ran it head to head against CodeScene too, same 2,770 files, same commit, same labels, since it's the closest established tool. Discrimination was close (0.73 vs 0.71). The gap showed up on effort-aware ranking. Under a "you only have time to review 20% of the changed lines" budget, the deterministic score surfaced more of the real defects (recall 0.17 vs 0.07, Popt 0.61 vs 0.46). Similar at telling risky from safe, better at ordering what to look at first.

Where it falls down, because 0.74 is not magic:

• It tells you where bugs cluster, not what the bug is.
• Ranks files, doesn't read them so not a review replacement.
• Defect labels come from bug-fix commits, which is a noisy heuristic. Some "fixes" aren't, some bugs never got a clean fix commit. That ceiling is baked in.
• The 6-month window is a choice. Move it and the numbers move.
• The CodeScene edge is specifically ranking under a budget. On raw "is this file risky, yes or no," they're close.

Where I landed: the signal is real and it's reproducible, which is the part I care about for PR gating but it won't catch a logic bug in a 4-line diff.

It's good at "this PR touches a file that's been a problem for a year with one owner, slow down." Different job than line-by-line review, and I think it's the job the deterministic approach is actually good at.

I have also added agent provenance to the same which allows it to determine if an AI PR is worse than human

If anyone wants to take a look at the repo: https://github.com/repowise-dev/repowise

Also, if you have any feedback on the metrics itself or if I can try some new metric, would love to try that

If you hand your laptop to someone for a few minutes, they can still open Messages, Photos, Notes, Mail, WhatsApp, browsers, password managers, and other personal apps. I wanted a way to protect specific applications without constantly locking my entire Mac.

I looked around for solutions, but most were outdated, paid, abandoned, or didn't feel native to macOS.

So I built FaceGate.

FaceGate is a native macOS app that lets you lock individual applications and unlock them using Face Unlock, Touch ID, or a password.

A few things I focused on from day one:

• Everything runs locally on your Mac
• No cloud processing
• No accounts
• No telemetry
• No subscriptions
• Fully open source

Features:

• Face Unlock powered entirely on-device using Apple's Neural Engine - little impact on cpu and gpu resources.
• Fast authentication with very low memory and CPU usage
• Liveness detection to prevent photo and video spoofing attacks
• Touch ID and password fallback
• Per-app unlock timers
• Automatic re-lock on sleep, wake, or screen lock
• Custom schedules for automatic lock/unlock periods
• Tamper protection that prevents FaceGate from being quit, disabled, or uninstalled without authentication
• Runs quietly from the menu bar with minimal system impact.

The entire project is written in Swift and designed specifically for macOS.

This is still actively being developed, and I'd genuinely love feedback from Mac users.

Some questions:

• Is app-level locking something you've wanted on macOS?
• Which apps would you personally lock?
• What security or privacy features would you like to see added?

Website: https://facegate-applocker.vercel.app/

GitHub: https://github.com/dweep-desai/FaceGate-Mac

If you think I did a good job, please feel free to leave a star on my github repo - means a lot to me.

Feedback, feature requests, bug reports, and contributions are all welcome. I'd love to hear what you think.

If you've ever wanted to turn boring screenshots into beautiful, scroll-stopping visuals in seconds, I'd love for you to check it out.

Your support, feedback, or an upvote would genuinely mean a lot. ❤️

👉 Product Hunt Link

Thank you for being part of the journey! 🚀

A few times ago, when I started to learn about the RAG models, I very disappointed about it response, I tried almost all the possible tricks and method to make it better, but still in some place, it's response got bad.

Then I searched about a lot of RAG model optimization, that method also not worked well, then finally I try to make a better version of RAG model.

Why I need a better version? I'm a building SupportGPT.

>

For that I need a better RAG architecture than Standard RAG, then I built QIndex RAG.

>Q-Index: Ingest generates questions per chunk, embeds questions (not chunks), stores chunk as payload. Query matches question-to-question.

Why this is better than Standard RAG?

1. This stores the hypothetical questions that a user can ask and a particular chunk contains that means. In return it increases the amount of accuracy and similarity, because now db also have the words or actual sentence a user can ask, because docs not built to answer the single question, instead docs focus to give you understanding and uses method for the product.
2. This increase the similarity scores from ~ 0.60 to ~ 0.90 - with the exact chunk that I need to answer the user question
3. Qindex uses Context aware or Semantic Chunking to keep the context across the chunks.

Cons (that I want to highlight and still working on that):

1. As most of the common questions contains the same starting like "How I ..", "What is ..", "Should ...", "How do I ...", etc. So these types of questions sometime attract the arbitrary chunks.
2. User ask directly question instead of making it a proper question, instead of asking "How can I reset password?" they ask "reset password", so to tackle this - I'm building a question optimizer that add useful phrase based on the user question.

I published this on GitHub and you can use this in your local with Ollama or your Live API keys.

Easy to use and implement, built in Python.

Link: https://github.com/ameghcoder/qindex-rag

For any recommendation or suggestion you can raise an Issue in GitHub repo or comment to this post.

Thanks

I've spent the last few months building something and I'm finally at the point where I want to share it properly rather than just quietly hoping people find it.

The idea came from a frustration I kept seeing (and feeling myself): SQL tutorials teach the syntax fine but there's never a reason to care about the answer. You filter a table called employees, get a result, and nothing happens. Your brain doesn't bother keeping it.

I wanted to try a different approach. QueryCase teaches SQL through detective investigations. You get a briefing from Chief Fox (our mascot), a real database to query, and a mystery to crack. The JOIN matters when a suspect has an alibi. The WHERE clause matters when you're trying to find who entered the building at 22:13. The SQL is the tool for solving something, not the point in itself.

Here's what's actually in it:

• A structured learning path across 54 cases, going from Recruit through Rookie, Detective, Senior Detective, and Chief Detective. Each rank has drills and a level exam to pass before you progress.
• Sandbox mode where you can explore real datasets (IMDB movies, Spotify, sports stats, Steam games) and run whatever you want with no pressure and no mystery attached. Just free exploration against actual data.
• Everything runs in the browser using DuckDB WASM so there's nothing to install.

I'm a solo developer and this is genuinely early days. I'm sharing here because this community is exactly the kind of people I built it for, and I'd rather get honest feedback now than find out later I've built the wrong thing.

What's missing? What would make you actually stick with something like this versus what you've used before?

querycase.com if you want to take a look.

Any feedback appreciated!

Just launched my chrome extension and started getting spam emails within 2 days. They didn't even check what my extension is about and want to promote it with me lol.

Do they really think these tactics work in 2026? And I'm sure they were not looking for similar tools on chrome extension either 😂

I wasn't into credit cards at all. For years, I didn't use them. Then one day my bank RM called and said: "We have a no-fee card with reward points." I was curious, so I took it.

That card was an HDFC Millennia card. Quarterly spend of ₹1L got me a ₹1k voucher. So I started routing my expenses through it. I switched a few jobs, and while opening a new salary account I was offered a new credit card. I also got a few on my own. All free from joining fee or annual fee. so I thought, what am I losing? :)

The Real Problem Started

But here's what I didn't realize: having multiple cards only helped if I actually knew which one to use for each payment.

I didn't. Not really.

I'd pull out a random card at checkout, make the payment, and months later wonder: "Did I earn the best reward on that purchase?" The answer was usually - I don't know.

Each card had different benefits buried in different T&Cs. I couldn't quickly check which one was optimal for groceries vs fuel vs movie tickets vs travel. I was leaving money on the table without even knowing it.

Why I'm Building PaySmarter

So I decided: I'm going to build the tool that tells me the right card to use, right when I need it. AI is here, and people are saying building stuff is easier than earlier, so I decided to try it out.

PaySmarter.in is simple — you add your already owned cards, tell it what you're buying, and it says: use this one. No guessing. No mental math.

It's free, privacy-first (not collecting credit card numbers), and India-focused because that's where this problem is real.

Where I Am Now

I'm building it during generally on weekends. Early stages, but genuine because I'm solving my own problem first.

If you've felt this pain — staring at multiple cards and wondering which one actually pays you best — then you get why I'm building this.

You can give it a try - PaySmarter.in
Let me know what do you think about this!

A few weeks ago I looked at my Downloads folder and realized it had turned into complete chaos.

300+ files. Old installers, random PDFs, ZIPs, screenshots... stuff I'd downloaded months ago and completely forgotten about.

I'd always tell myself, "I'll clean this up later."

Of course, later never came.

The problem is that when you download something, you already know whether it's important or just temporary.

That PDF from your bank? You'll probably want to keep it.

That random setup.exe you needed once? You'll probably never touch it again.

But by the time you're cleaning your Downloads folder weeks later, you have no idea what half the files are anymore.

So I built a Chrome extension called KeepTrack.

It quietly classifies every download as either Keep, Temporary, or Needs Review.

It doesn't use AI or send anything to a server. It's just a bunch of local heuristics.

It looks at things like:

• the file type (.pdf is usually worth keeping, .exe usually isn't)
• the filename (invoice, receipt, resume, etc.)
• where the file came from (your bank vs. a software download site)

Each signal contributes to a score.

If it's confident, it classifies the file automatically. If it's unsure, you get a small notification asking whether you want to keep it or treat it as temporary.

Temporary files stick around until you decide to clean them up. After two weeks they'll appear in the extension popup, where you can delete them individually or all at once. If you're feeling productive, there's also a Clean Up Now button.

A few things people here might care about:

• Everything runs locally.
• No accounts.
• No telemetry.
• Works offline.
• Open source (MIT).
• Built with plain JavaScript (Manifest V3 + service worker).
• On first launch it only shows you a preview of how it would classify your existing downloads before enabling anything.

I also made a small landing page because I thought it'd be fun to package it like a real product.

Website: https://priyanshu-byte-coder.github.io/keeptrack/

GitHub: https://github.com/Priyanshu-byte-coder/keeptrack

I'd genuinely love feedback—especially if you find files that get classified incorrectly. The rules are intentionally simple and easy to improve, so real-world edge cases are super helpful.

I’m validating a new idea and would love your feedback.

NewsToPost helps you turn a news article into ready-to-post content for X, LinkedIn, and Instagram, plus a matching AI image.

Try it: https://newstopost-eight.vercel.app/

A few questions:

Would you use this?
What’s missing?
Who do you think it’s for?

Thanks for your honest feedback!

Hi everyone,

Over the last few months I've been working on a small side project called Lullaria.

The idea was simple: create a place where parents can easily access lullabies, white noise, nature sounds, and other calming audio for bedtime.

After a lot of testing and polishing, I finally launched it on Google Play this week.

I'd love to hear honest feedback from parents and fellow developers. Google Play:

https://play.google.com/store/apps/details?id=com.lullaria.app

A few things really challenged my assumptions:

• Almost everyone agreed that information overload is a real problem.
• Many pointed out there are already plenty of monitoring tools.
• The biggest concern wasn't finding information—it was trusting the system to know what actually deserves your attention.
• One comment really stuck with me: "The product has to earn the right to interrupt me." I think that's exactly right.
• Another good challenge was whether founders would actually pay for monitoring, or if this is just a "vitamin" rather than a "painkiller."

That completely changed how I'm thinking about the product.

Instead of trying to monitor everything, I'm now exploring whether the value is in personalized decision support—understanding your startup's goals, competitors, and context, then explaining why something matters instead of simply notifying you that it happened.

One question I'm still trying to validate:

If you already track competitors or market changes, what does your workflow actually look like?

• Google Alerts?
• RSS feeds?
• Competitor spreadsheets?
• Reddit searches?
• Product Hunt?
• Something else?

Or do you mostly check things manually when you remember?

I'm far more interested in what founders actually do than what they think they should do.

I've started building a prototype based on all the feedback. If you're interested in trying it or following the progress, I've put together a small waitlist: https://spectre-black.vercel.app/

And if you have more thoughts (or think this is a terrible idea), I'd genuinely love to hear them. The discussion so far has been far more valuable than people simply saying, "I'd use it."

Hey everyone,

I’m building linkinseconds.com — a small tool to upload and share PDFs, HTML pages, markdown notes, images, or quick docs as a live link in seconds.

Why I built it:
Many times I wanted to share a quick PDF, demo page, notes, or small HTML file without setting up hosting, Google Drive permissions, or sending files again and again.

What it does right now:
- Upload PDF / HTML / markdown / image
- Get a shareable live link
- Choose view-only or downloadable
- Useful for quick docs, demos, portfolios, landing pages, notes, and client files

I’m still early and would love honest feedback from Indian indie hackers:

1. Is the positioning clear?
2. Who do you think needs this most?
3. Should I focus more on PDFs, HTML demos, or markdown/docs sharing?

Link:
https://linkinseconds.com

Happy to take any blunt feedback.