I built a tool to make curve-fitting experiments easier

Hi,

I got tired of how clunky curve-fitting workflows can be sometimes, so I built a small tool to fix it.

Context: I do a lot of data analysis (scientific / experimental), and I keep running into the same friction:

• writing boilerplate code, or Jupyter notebooks, or using spreadsheets
• tweaking initial parameters blindly
• re-running cells to see if a fit improved
• no intuitive way to explore models interactively

So I built a lightweight app where you can:

• upload CSV data,
• try different fit functions quickly,
• immediately see how the fit changes,
• extract the fitted parameters cleanly,
• Get automatic statistics insight,

It’s basically meant to remove the “notebook friction” from iterative fitting.

It’s still early (MVP), so I’d really appreciate feedback—especially from people doing:

• experimental physics
• lab work
• quick data exploration

Main things I’m trying to validate:

• Is this actually faster than your current workflow?
• What’s missing for it to be genuinely useful?
• Where does it break?

Happy to implement suggestions if they’re aligned with the direction.

Thanks!