The Problem: As a nomad, I hated cashing out my USD/EUR only to see the rate jump 1% the next day. It s like taking a pay cut just for having bad timing.

The Solution: I built Payding a simple, privacy-first utility that tells you when to cash out.

You log your payment, and Payding benchmarks it. Instead of just "tracking rates," it tracks your profit.

It s the third week of working on my little side project, SimploMail.com, and to speed things up I ve been doing a lot of vibe coding. It s been fun, but a few things became obvious pretty quickly.
I stopped using the auto model setting in the IDE. When it silently switches models, the quality drops fast. I can feel when the agent all of a sudden looses its intelligence . So now I just pick one model I trust and stick with it.
I also try to keep each AI session focused on one small task. One feature, one change. After it writes the code, I go through everything myself. I check for hard coded config, make sure it didn't quietly delete a unit test to make something compile, and etc. Sometimes it does update unit test just to make it pass .
And I never commit without reviewing. The AI is helpful, but it can also remove something important without telling you. I've seen it happen enough times now .

We ve worked in two other eco-systems (India & France), and each has clear strengths and trade-offs in terms of talent density, cost of building, access to capital, speed of decision-making, and openness to risk all vary a lot.

Curious to hear from founders and operators who ve built outside the US:

• Which ecosystem punches the most above its weight today?

• Where do you see the best balance between talent, capital, and customer access?

• Are there cities/countries that are especially strong for specific stages (0 1 vs scaling) or specific verticals (AI, fintech, climate, SaaS, deep tech)?

News in the tech world gets old quickly. It's no longer relevant in 10 minutes.

What helps you quickly navigate the world of AI?

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