Everyone's using a Grok Bot. I'm building the open, self-hosted, BYOK version with real agent loop, browser control, memory, and all CI gated.

Come break it, and contribute to it:

https://github.com/muhtalhakhan/open-agent

One thing I’ve found frustrating with AI research is that getting an answer is often the easy part. The harder part is figuring out which claims are actually backed by reliable sources, especially when researching competitors, markets, products, or a new business idea.

Curious how other founders handle this.

Do you manually open and verify every source?

Use a spreadsheet to track evidence?

Cross-check several AI tools?

Or just accept a certain level of uncertainty?

What does your research process look like when you need information you can actually trust?

I was working on a small side project that needed birth chart data, so I decided to try MCP instead of building everything around a REST API.

I checked seven astrology APIs, and the MCP support was pretty inconsistent. The biggest surprise was one setup with 224 tools. That felt like a lot for an agent, so I limited it to the six tools I actually needed.

Has anyone else dealt with MCP servers exposing hundreds of tools? How do you handle it?

I got tired of opening a different website for every tiny task, so I built Utilix — one place for CSV, JSON, PDF, image, regex, QR, text and other useful tools.

V1 is live 👇
https://utilix.usetools.workers.dev/

Brutally honest feedback welcome.

Hi All,
Introducing www.guessla.com - it rhymes with Tesla.
Built this stock prediction gaming website.
Guess Stock Closing Price every trading day and win points, redeem these later, end of the month.
It took several days of Replit vibe coding to get to this stage.
Appreciate your feedback.
Please sign up and play.

https://www.guessla.com

Hey everyone! 👋

I’ve been working on Mynqora, a simple personal finance tool designed to make managing money less complicated.

You can track your expenses, budgets, subscriptions, savings goals, and net worth in one place, with a clean dashboard that helps you understand where your money is going.

I focused heavily on keeping it simple and easy to use instead of filling it with complicated financial features.

It’s still growing, and I’d genuinely appreciate some feedback:

What would you add, remove, or improve?

Try it here:
mynqora.com

Thanks! 🙌

Wanted a "hold hotkey, talk, get typed text" tool like Wispr Flow, but didn't want to pay a subscription for something that streams my voice to a server. So I built VoxScribe over a few weeks: hold F9 anywhere on Windows, it transcribes locally with faster-whisper, and types straight into whatever app has focus.

Stack: Python + PySide6 for the desktop app, PyInstaller for packaging, faster-whisper for local transcription. Also built an Android companion (custom keyboard + floating dictation bubble) using a bundled on-device Whisper model via sherpa-onnx.

MIT licensed, no account, no telemetry. Landing page: https://voxscribe-ai-voice-dictation.vercel.app
Repo: https://github.com/ahmedhmam1994/voxscribe-ai-voice-dictation

Happy to talk through any of the build decisions — the mic-handling across different hardware was the trickiest part.

Free, no account, nothing to install. Each lesson is a few minutes of reading and then a drill: build the chord, name the note on the staff, tell two intervals apart by ear, or tap a rhythm back in time. You answer on piano, guitar, bass or violin.

Disclosure: I made it. No ads, no signup, nothing monetized.

harmonygym.app

Most dropshippers see a product hit 500k+ views on TikTok and rush to list it. The issue is cross-platform lag: by the time a video goes viral on TikTok, top media buyers may already have thousands of active ads running on Meta.
Here is the data on 3 products spiking this week:
Product 1: Laser hair removal device
TikTok 7-Day Velocity: +635% views spike.
CJ Dropshipping Status: High Merchant Listing.
The Trap: 800 active ads currently running in the Meta Ad Library.
Verdict: Saturated. High CPC risk.
 
Product 2: Massage pillow
TikTok 7-Day Velocity: +423% views spike.
CJ Dropshipping Status: High Merchant Listing.
The Trap: 4,100 active ads currently running in the Meta Ad Library.
Verdict: Saturated. High CPC risk.
 
Product 3: Automatic cheese grater
TikTok 7-Day Velocity: +202% views spike.
Meta Ad Library: Under 15 active ads found in the Meta Ad Library.
Verdict: Low competition. Viable testing window.
 
Data pulled directly from our cross-platform tracking engine (HermesSync ai). Happy to answer questions on how we calculate velocity vs. ad conflict below.

So I had this dumb idea a few weeks ago: what if asking someone out (or asking them anything, really) came with a "No" button that's physically impossible to click?

That turned into SnapSip — you write a question ("wanna get coffee?", "will you marry me?", whatever), pick a "Yes"/"No" setup, and send the link. When the person tries to hit No, the button just dodges away. Eventually they're stuck tapping Yes, confetti goes off, hearts everywhere, and boom — you got your answer.

A few things I focused on:

No signup, no login, completely free, no ads

Works in English, Arabic (full RTL support), French, Spanish, German, Turkish

One-tap share to WhatsApp/Telegram/X etc, or just a QR code

Mobile-first since that's genuinely 90%+ of how people will open these links

It's obviously a silly little tool, but it's been fun watching people use it for actual proposals, job offers, birthday invites, even "wanna play games tonight" type stuff — way more use cases than I expected going in.

Link: snapsip.fun

Would love brutally honest feedback, especially on the UI/UX side. Also curious if anyone's built something similar and what tripped them up.

Hi! I'm the developer of DayFrame, an Android journal app built around a simple idea: what if you recorded your life like a collection of movies?

Instead of just writing a dated entry, each day becomes a film with its own title, poster, genre, rating, cast, and filming locations.

You can write normally or divide your day into Scenes, with different people and locations for each moment. Over time, your entries build a personal filmography, while the Casting Board keeps track of the people who appear throughout your life.

I built DayFrame because I wanted looking back at a journal to feel more like revisiting your own story than scrolling through old text. It's built with Flutter, stores journal data locally without requiring an account, and supports optional local/Google Drive backups.

The app is free to use on Android, with optional paid ad removal:

https://play.google.com/store/apps/details?id=com.minsoo.raonlog&hl=en

I'm the developer, and I'd especially appreciate feedback on whether the life-as-a-movie concept feels genuinely useful rather than just a visual theme.

I kept running into the same problem with long AI responses, I would reach a later section and need to scroll back up to check an earlier point.

So I built FreezePane AI, a small Chrome extension that lets you freeze one section of a ChatGPT answer at the top while the rest of the answer scrolls below.

The GIF shows the intended use: keep Section 1 visible while reading later sections.

It is live, free, and still early:
https://chromewebstore.google.com/detail/freezepane-ai/jlbnkmijoggbohfciokdkfcbjopkihbo

I am not looking to overbuild it. I’d value honest feedback on one question: would this genuinely improve your workflow with long AI answers, and what would you expect it to do next?

Recently released our app, Trak Vault on Google Play Store and Apple App Store. Our app will let you track various collections across different franchises. Currently, we just have Pokemon, but are adding Yugioh this week, along with other minor collections and a marketplace for purchasing. Part of our goal is to take smaller collections and put them next to bigger ones, allowing for more exposure. We don’t have many users, but we’re looking for some feedback. We’ve added a lot of unique features, like a zoom in/out to expand/shrink the amount on the screen, hold to add, and are working on much more not seen in other apps. Anything is appreciated. Looking forward to also giving our own feedback to other projects posted here and sharing what we know where we can help!

I wanted to tell my life story in a unique way seamlessly transitioning between photos, videos, blogs and ai generated assets like comic books.

You can use the timeline at the bottom to go back in time all the way to my childhood and the filters to get familiar with the people who played a key role in my life.

The project is open source at

https://github.com/paulvisciano/where-is-paul

And live at

https://paulvisciano.com/apps/where-is-paul/

I have built it in a way that makes it easy to customize and make it yours.

Hope it inspires you to build something of your own and ditch traditional social media.

p.s. I briefly tested on an Apple Vision Pro and it worked fairly well, if you have one please test it out and drop me a line with your feedback

I'm the maintainer of Ferrum, an MIT-licensed local LLM inference server written in Rust. I'm posting as the builder, not pretending to be a user.

I dogfooded the public v0.8.3 onboarding path on an M1 Max before writing this. The Homebrew package, model alias, CLI chat, OpenAI-compatible server, normal chat completion, and streaming usage path all worked.

The shortest Apple Silicon path is:

```bash

brew tap sizzlecar/ferrum

brew install ferrum

ferrum doctor

ferrum run qwen3.5:4b-q4_k_m

ferrum serve --model qwen3.5:4b-q4_k_m --served-model-name ferrum --port 8000

```

Then any OpenAI-compatible client can point to http://localhost:8000/v1.

Two rough edges I found while testing:

- The starter model is about a 2.55 GiB download, but the quick start does not make that clear enough.

- Qwen3.5's default thinking is too verbose for a first "Hello" request. It can be disabled per request, but the recommended path should handle/document this better.

Project: https://github.com/sizzlecar/ferrum-infer-rs

I'm looking for blunt, public feedback from Apple Silicon developers. If you try it, please reply here with your Mac chip/RAM, whether you reached the first API response, the first confusing or failing step, and what (if anything) would make you choose it over your current local inference setup.

No waitlist and no request to DM me — I'd rather keep the useful feedback in the thread or in public GitHub issues.

I’m testing a small learning experiment.

The idea: instead of teaching BFS/DFS by showing code first, the user solves a grid manually. If they expand layer by layer, the app labels it as BFS. If they go deep into a branch and backtrack, it labels it as DFS/backtracking. Then it shows runnable code based on the strategy.
Link: https://games.playlooplabs.com/learn/
I’m looking for blunt feedback on:
1. Is the concept understandable within the first 30 seconds?
2. Does the pathfinding interaction feel natural?
3. Would this help someone learning DSA, or is it still too abstract?

Known limitation: this is only the pathfinding POC right now, not a full platform.