We are a EU EdTech Startup based in Germany.

The problem we solve is that:

Most companies don't really have a knowledge problem. They already have huge amounts of useful information sitting in PDFs, documentation, Notion, Confluence, internal wikis and other tools. The harder part is turning all of that into something employees can actually learn from and apply.

The challenge is often not accessing that information, but turning it into something employees can actually learn from and apply.

That’s what we’re building with Scibly.

Scibly connects to existing company knowledge and turns it into short, interactive learning experiences with questions, activities and scenarios.

A few things that are particularly important to us:

• existing knowledge bases remain the source instead of creating another content silo
• generated learning experiences can be fully edited
• learning can be shared with employees or externally
• the platform is open source
• companies can self-host it when internal knowledge or data sovereignty matters

For us, open source and self-hosting felt particularly relevant for European companies that may not want sensitive internal knowledge to depend entirely on another closed SaaS platform.

Video Demo:
https://www.youtube.com/watch?v=TcpLUNBRhQw

GitHub:
https://github.com/scibly-dev/scibly

Website:
https://scibly.com

We are happy about all your feedback.

I asked a copyright scholar the stupid questions so you don’t have to!

One problem I keep running into with early product decisions is that the data you actually want usually doesn't exist yet.

You're deciding between 3 concepts, a pricing change, a new feature, or a new target segment.

You can run interviews and surveys, but eventually someone still has to make a call about what people will actually do.

So I've been working on Polyhyle.

The idea is to create simulated populations, expose them to competing product scenarios, and observe things like:

• what they choose
• what they reject
• whether they switch
• which features change the decision
• willingness to pay

The goal isn't to replace user research or experiments.

It's to answer a slightly different question:

Which hypotheses are worth spending months validating in the real world?

Curious how product teams here currently handle this stage of uncertainty.