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I built a tool to make curve-fitting experiments easier
Our take
Introducing a new tool designed to streamline curve-fitting experiments, I aimed to alleviate the common frustrations of traditional workflows. As someone deeply involved in data analysis, I often faced hurdles such as writing repetitive code and blindly tweaking parameters without clear insight. This lightweight app allows users to upload CSV data, explore various fit functions interactively, and instantly visualize changes in fits.
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!
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