I'm Andrew, based in London, founder and CEO of Kineto, with over a decade in content platforms, music products, and voice assistants. Most recently, before Kineto, I created and led Junie, the JetBrains coding agent, got to scale it from zero, watch it actually start shaping how teams work, and learn what scaling an agent really takes.

Now I'm taking that experience into a different industry: the creator and influencer economy.

Two things I'd genuinely love to talk to people here about:

The creator economy itself. Tools, platforms, business models, all moving fast. I'm a fan of the take that we can't really automate creativity, but there are 20 things around it that agents can genuinely help with. Not using AI is not on the table, that ship sailed. The interesting question is how we build products that take the boring 20 and protect the part people actually got into this for. Always curious to meet others working in this space: image, video, audio, voice, anything adjacent.

Vedic astrology isn't just zodiac signs. There are documented

systems Samudrika Shastra for palm reading, Mukhanga Shastra

for face reading, full Jyotish for birth charts each with

thousands of specific rules that have been studied for centuries.

My name is Eva and I'm from Czechia. I studied Russian translation at university, that was supposed to be my path. For a few years it was: I worked as a project coordinator at a power engineering company. Then things shifted, my field got affected by what was happening in the world, and I had to rethink my career.

I had been curious about programming for a long time, partly because my boyfriend is a software engineer and watching him work made it feel less abstract. There was a boom of programming courses in Czechia at the time, so I took the chance and started from zero, spending evenings building little card games and trying to make sense of JavaScript.

A few months later, I found a job as a frontend engineer at a digital agency. That first year was hard. I was learning fast, building real products for real clients, and constantly feeling behind. Impostor syndrome was a daily companion. But I kept going. My partner became my mentor, and I owe him a lot of who I am as a developer today.

A year later, I joined Native, an American startup building a B2B AI conversation platform. For a language enthusiast like me, it was a dream. Over three years I grew from frontend engineer to senior product engineer, architecting real-time WebSocket systems, driving activation growth, working closely with design and product. That place taught me how much of engineering is actually about people, trade-offs, and listening.

Hi Product Hunt. I am Andreea, founder of 2 Minutes Responder. We launched today.

I built this after watching people make offers on UK properties without knowing the flood zone would make it uninsurable, or that the net yield was negative from month one after real costs. The data has always been public. Nobody had put it together before the offer.

2MR does it in 90 seconds. Flood zone, net yield, crime trend, school rating, 5-year growth. Any UK postcode. Free.

Would love to hear from anyone in property or investing. Happy to run any UK postcode publicly right here in the comments if you want to see it in action.

I'm an engineer. I love solving problems. When working on an AI agent I discovered that LLM are really bad at web automation. They know what steps to perform to complete a task, they just don't know how to do them. I saw this as a fundamental problem that had to be solved before I could create a general purpose agent. So I solved it.

The solution is webSlinger. This is how it works. You show webSlinger how to perform the task in your browser and webSlinger copies you in real time in another tab. You get immediate feedback that webSlinger can perform each action as you do it. Save the resulting "session map", and now webslinger can translate from "what to do" to "how to do it." The LLM then has what it needs to generate an automation script.

Plan -> Record -> Generate -> Automate (repeatedly).

I built in the capabilities I cared about the most, secure login and MFA with locally held credentials and array extraction.

Hey everyone

I'm a developer/operator who spends a lot of time thinking about automation, workflows, AI tooling, and how to reduce repetitive work without creating fragile systems in the process.

Most of my recent work has been around developer tools, data workflows, and helping teams save time on the boring operational stuff that quietly eats hours every week.

I'm especially interested in:

My work for the past few years has been at the intersection of AI and product figuring out what actually gets built vs. what stays on the roadmap, and why those two lists are rarely the same.

Here to learn how builders in this community think. The PH crowd seems to move faster than the environments I've worked in. Curious what that looks like from the inside.

What are you building right now?

Hi, I'm a research data engineer working with academic teams on data collection, cleaning, versioning, and reproducible analysis.

A lot of research data work breaks down before the model, chart, or paper ever happens. Sources change, scripts are undocumented, licenses are unclear, and six months later nobody can explain exactly how the dataset was created.

I'm especially interested in open science, dataset provenance, archival workflows, and practical ways to make research easier to rerun without slowing everyone down too much.

Curious how others here handle this: when you publish or share a data project, what do you document first... raw data, collection timestamps, cleaning scripts, licenses, environment files, or something else?