I kept getting distracted every time I tried to study.

Switching between music, timers, notes nothing really felt right.

So I built Rainwork a simple, immersive space to study without distractions.

Lofi, Pomodoro, notes, all in one place.

We're launching TeamAI today AI employees that get a real Ubuntu desktop, open Chrome, and actually complete tasks for you.

Not just answers. Real work.

The most surprising use case from our early users has been competitive research. They tell the agent "go through these 20 competitor websites and summarize their pricing" and come back in 20 minutes. No copy-paste, no prompt engineering, just done.

Didn t expect this much activity today.

A lot of people trying the product, building agents, sharing ideas. Super interesting to see how differently teams approach it. Some go straight into ops and support, others start with leads or research.

Feels like we re just at the beginning of figuring out how this should actually work.

It's probably more than you realize.

But most of what we watch is from strangers. While the stuff that actually matters to us just sits in our chats.

Hey Product Hunters,

We re a small group of 15-year-olds from Mumbai running Indian Instinct Studios. Every evening we used to open the fridge and ask kya banaun aaj? (What to make today?)

So we built TikkaMate an AI cooking assistant made for real Indian kitchens:

Fridge photo Jain thali in 10 seconds (no roots, no onion, no garlic)

Hey makers. We re tech founders who saw a massive gap in interior design tools. Designers spend days turning 2D floor plans into 3D. We built Foursite to fix that.

Upload blueprints or 2D floor plans.

Get accurate 3D Visualization instantly.

Most dog behaviour issues aren't caused by bad dogs they're caused by inconsistent routines.

Seryn builds a free personalised daily plan for your dog around exercise, enrichment and rest based on their age, energy level and living situation.

One pattern I keep seeing is that logging starts out useful, then gradually becomes inconsistent, noisy, expensive, and harder to trust. Field names drift, context goes missing, dashboards get polluted, and sometimes sensitive data ends up in places it never should have reached.

The deeper problem seems to be that most teams try to fix logging after the data has already entered downstream tools. By then, the cost, risk, and cleanup burden are already there.

I m curious how other teams handle this. What breaks first in practice: naming consistency, missing required context, sensitive fields in logs, alert noise, or ingestion cost? I m building in this area and want to learn where current approaches still fall short.

I built a free tool that simplifies generating Access Tokens for Shopify custom apps.

If you've worked with Shopify's API since their early 2026 update, you know the process got significantly worse multiple screens, manual URL copying, and way too many steps for something that should be simple.

Hey,
We're building an open source tool for creating live, interactive product demos.
Think Navattic or Arcade, but self-hosted and free to run yourself.
The repo is at https://github.com/exploitx3/liv... and our homepage is https://livedemo.ai.

We kept running into the same problem:
you build something great, but getting prospects or users to actually understand it before they sign up (or churn) is surprisingly hard.
Existing demo tools are expensive and make it painful to export or customize anything.
That felt wrong, so we built LiveDemo.ai.
Here's what it does:

Capture and replay your product as an interactive walkthrough, with no heavy instrumentation required Self-hosted so your demo content and user data stay on your infrastructure, not ours Open source under MIT so you can inspect it, fork it, and extend it however you need

We've kept setup as simple as possible.
The repo includes Docker local setup and one-click deploy for kubernetes so you can be up and running in minutes.(Will add the kubernetes manifest soon)