Everything you wanted to know about Muffin but were too busy converting CSVs to ask.
Muffin is a set of free, browser-based data tools for researchers, developers, and anyone who just needs to convert a file without signing up for yet another SaaS platform. Everything runs in your browser — your data never leaves your device. No exceptions.
No. Not a single byte. All file processing happens entirely in your browser using JavaScript. When you upload a file or paste data, it is loaded into your browser's memory and processed locally — it is never sent to any server, database, or third-party service.
You can disconnect from the internet after the page loads and all tools will continue to work perfectly. That's how local it is.
We don't use any tracking cookies, analytics scripts, or fingerprinting. We don't know who you are, where you're from, or what you converted. We literally have no backend to log this information to.
The only external requests the page makes are to load fonts from Google Fonts and libraries from Cloudflare's CDN — standard stuff that every website uses, and neither of these can see your files.
Yes — because nothing leaves your device. Your CSV, Excel, or notebook file is processed entirely in your browser's memory. Once you close the tab, it's gone. There's no cloud storage, no server logs, no database.
For the most sensitive data, you can even download Muffin and run it locally on your machine with no internet connection at all.
The visualizer accepts any standard flat CSV or Excel (.xlsx) file where the first row is a header row and each column has a name. It works best when your data is structured like this:
It does not support multi-row headers (like merged cells in Excel where one header spans multiple columns). If your Excel file has that, flatten it to a single header row first.
Yes — this is fully supported. The Y Axis selector shows checkboxes for every column in your data. Just tick the ones you want to plot and they all appear as separate colored lines with a legend.
Each series automatically gets a distinct color: purple, green, pink, yellow, blue, orange, and more — always visible on the dark background.
Four chart types are supported:
You can also enable Highlight best value to mark the maximum or minimum point on any line with a star marker.
Yes — click the PNG button above the chart to download it as a high-resolution 1200×700px PNG image, ready to drop into a paper, report, or presentation.
In Google Colab: go to File → Download → Download .ipynb. Then upload that file to Muffin's Notebook Cleaner. It strips all outputs, resets execution counts, and removes Colab metadata — same result as running nbstripout, but no Python or terminal needed.
For most use cases — especially pushing to GitHub — check all three of these:
If you're seeing an error like "the 'state' key is missing from metadata.widgets" on GitHub or nbviewer, also check Remove widget state — this removes the broken widget metadata that causes that error.
Yes. Click the Batch Clean tab, drop multiple .ipynb files at once, and all of them get cleaned with the same settings in one go. Each notebook gets its own download link showing how many outputs were removed and how much smaller the file got.
This is useful if you have a full folder of experiment notebooks you want to clean before pushing to GitHub.
Three reasons: size (notebooks with outputs can be 100MB+), diffs (every run changes timestamps and execution counts making PRs unreadable), and privacy (outputs can accidentally contain API keys, file paths, or sensitive data you don't want public).
Cleaning before every commit is considered best practice. Tools like nbstripout do this automatically — Muffin's cleaner does the same thing on demand, in your browser.
Go to Muffin's Image & PDF Converter and click the Merge PDFs tab. Drop your PDF files, reorder them with the ↑↓ arrows if needed, give your output a filename, and click Merge PDFs. Download button appears instantly.
Yes, completely. Muffin uses PDF-lib to copy pages directly from each PDF into the merged file. The text, images, fonts, and layout are preserved exactly — nothing is re-rendered or compressed. The output is identical to the originals, just combined.
No hard limit — you can merge as many PDFs as you want. The only practical limit is your browser's memory. Very large PDFs (100MB+ each) may be slow, but for typical lecture slides, research papers, or reports it will be fast.
The merging happens entirely in your browser, so no file size is "uploaded" anywhere.
Yes — use the Images to PDF tab in the same tool. Drop multiple PNG, JPG, or WebP images and each one becomes a page in the PDF, in upload order. You can reorder with arrows before creating. Choose A4, Letter, or fit-to-image page size.
The Data Converter supports: CSV, JSON, JSONL (newline-delimited JSON), and Excel (.xlsx). You can convert between any combination — CSV → JSON, JSON → CSV, Excel → CSV, CSV → JSONL, JSONL → JSON, Excel → JSON, and so on.
Really, genuinely free. No freemium tier, no file size limit behind a paywall, no "export to CSV requires Pro". The tools are just HTML, CSS, and JavaScript — they cost virtually nothing to host.
We're two people who built this in our spare time because we needed these tools ourselves. No VC funding, no team of 40. Just two people who got tired of sketchy upload-your-file websites.
The best ways to support Muffin:
We're open to it! Muffin is built for data scientists and researchers — if your product is relevant to that audience, reach out on LinkedIn. We'd rather work with sponsors whose tools we'd actually use ourselves.
We'll never show intrusive ads or partner with anything spammy. Any sponsorship will be tasteful and clearly labelled.
Send a message on LinkedIn — Sneha Chakraborty or Divyansh Pathak. We read every message and take bug reports seriously. Feature requests that come up repeatedly actually get built.