u/hideki-japan

I’m trying to understand how researchers actually track new papers in biochemistry and related fields. PubMed alerts, Google Scholar alerts, journal TOCs, Twitter/X, lab Slack, manual searches — I’m curious what people really use.

A few questions:

1. How do you currently find new papers worth reading?
2. What is the most annoying part of that workflow?
3. Would it help if a tool ranked new papers by relevance to your research topic and explained why each paper is included or excluded?
4. What would make such a tool genuinely useful rather than just another alert system?

I’m not trying to promote anything here — mainly doing user research for a literature-monitoring workflow. Would appreciate honest opinions, especially from grad students, postdocs, and researchers who regularly track papers.

10 months ago, I had basically zero coding experience.

I'm a corporate R&D researcher in Japan. My day job is not software. I have a family, limited time after work, and until last year I never seriously imagined I could ship a SaaS product by myself.

Then I started building with AI agents.

Claude Code, Codex, and ChatGPT changed what felt possible. I still had to make the decisions, debug the mess, learn the architecture, set up infrastructure, deal with email delivery, payments, domains, legal pages, and all the boring parts.

But without AI agents, I honestly don't think I would have gotten this far.

After about 10 months, I launched my first real SaaS product: OpenPharos.

It's a multilingual paper alert tool for researchers, built on OpenAlex. It sends research alerts with translated titles so non-native English researchers can scan papers in seconds.

Launching it felt strange.

On one hand, I had built something real. A year ago, I couldn't code. Now I had a working product, onboarding, email alerts, payments, and a public website.

On the other hand, users did not magically appear.

The strangest part: in a small beta, Latin American testers reacted much more strongly than Japanese testers, even though I'm based in Japan. 7 out of 8 testers from Latin America gave strong positive feedback, compared with 4 out of 10 in Japan.

So the product seemed to resonate somewhere — just not where I expected. But I didn't have the network or channels to act on that signal.

That part hit me harder than expected.

AI made building much more accessible, but it did not remove the need for distribution, trust, positioning, and uncomfortable conversations with real people. In fact, it may have made the gap more obvious.

Shipping is no longer the finish line. It is just the point where the harder, more human work starts.

AI agents turn a motivated non-engineer into someone who can ship. But they don't tell you who cares enough to use the thing, who trusts you, or where to find them.

So now I'm trying to learn the part I avoided.

For those of you who built with AI agents:

• What was the first distribution channel that actually gave you a real signal?
• For non-US-based builders especially, how did you bridge the gap to your real audience?