AutoPlot is a Mac native scientific workspace for plotting and exploratory data analysis. Import tabular datasets, create variables, build X&Y plots, histograms, heat maps, correlations, 3D plots, and charts, run fits, then compose figures with vector export. Metal-backed GPU rendering keeps 3D scenes and correlation networks interactive at full dataset size — rotate, zoom, and re-fit without waiting on a redraw. Raw data values never leave your Mac.
Hey Product Hunt 👋
We built AutoPlot because we kept doing the same thing over and over: export data from an experiment, open a notebook, paste in the same 200 lines of plotting and fitting code, tweak a bin width, re-run everything, repeat. The analysis was the interesting part. The plumbing wasn't.
AutoPlot changes how you interact with your data: you describe the analysis, and the app writes the Python, runs it, and draws the figure.
It's a native macOS app with a real Python engine underneath — and the AI drives that engine in two ways:
Agent mode — "create z = sin(x)·cos(y), then add a 3D surface" runs actual Python and builds the cards, variables, and fits in your workspace. Every generated script pauses for your confirmation before it runs, and accepted code is saved to a reproducible trace.
File Operations mode — point it at a folder and it manipulates the files themselves: skip header rows, split by threshold, write new .dat files, then import them. It's sandboxed to that folder only — no shell, no network.
The analysis surface is built for real work: X&Y, histograms, PDF/CCDF, custom fits, heat maps with Gaussian-mixture fitting, 3D surfaces, correlation matrices, and power-law tooling (log-binned fits, xmin diagnostics, FSS collapse, scaling relations). Plus a Compose board for publication figures, and workspaces that autosave and reopen exactly as you left them.
macOS 15+. Tell me what breaks and what's missing — We are reading every comment.
The reproducible-Python export is the right call, because with AI-written analysis the plot that renders cleanly is the one you stop checking. Every bad LLM-generated analysis we hit was silent, never a crash: NaN rows quietly dropped before a mean, or a default bin width smearing a real bimodal peak into one hump. The chart looks fine so nobody re-reads the code. Do you surface the assumptions the model made, rows dropped, null handling, aggregation choice, somewhere near the figure itself, so a wrong-but-pretty plot is catchable without opening the export every time?
as someone who's bounced between a notebook for the analysis and a separate app for publication-quality figures, the vector export at the end of the pipeline is the part that stands out to me. what format does that export to - straight SVG/PDF, or does it keep layers editable in something like Illustrator? and does "raw data never leaves your Mac" mean fits run fully on-device too, or just the raw import step?
Unifying analysis and plotting in one place is a real pain point — too many tools force you to jump between a notebook and a separate charting tool. Does it support live data sources, or is it mainly for static datasets right now?
I lose whole afternoons to bouncing between spreadsheets and notebooks and image editors, so the idea of it all living in one calm place lands well. Keeping the raw data on my own machine makes it feel even more trustworthy. Thanks Lale!
Would love a built-in scripting pane so I can automate repetitive fits and re-run an analysis when the underlying CSV gets updated. Right now I have to re-click through the same steps every time the dataset refreshes.
the Metal-backed 3D plots are genuinely smooth, way better than fighting with matplotlib on a big dataset. kind of nice that everything stays local too
Honestly the 3D rotation on big correlation networks is super smooth, no lag at all on my m1. Felt kind of like having origin but actually fast and local, which is basically what i always wanted
Would love a Jupyter notebook style export so I can pair each plot with the exact code or transformation steps that produced it. Also makes it way easier to share reproducible figures with collaborators who don't have AutoPlot.
About Autoplot on Product Hunt
“Unify data analysis and plotting in one powerful workspace”
Autoplot launched on Product Hunt on July 20th, 2026 and earned 106 upvotes and 15 comments, placing #20 on the daily leaderboard. AutoPlot is a Mac native scientific workspace for plotting and exploratory data analysis. Import tabular datasets, create variables, build X&Y plots, histograms, heat maps, correlations, 3D plots, and charts, run fits, then compose figures with vector export. Metal-backed GPU rendering keeps 3D scenes and correlation networks interactive at full dataset size — rotate, zoom, and re-fit without waiting on a redraw. Raw data values never leave your Mac.
Autoplot was featured in Productivity (656.7k followers), Data Science (3.9k followers) and Data Visualization (3.6k followers) on Product Hunt. Together, these topics include over 149.9k products, making this a competitive space to launch in.
Who hunted Autoplot?
Autoplot was hunted by Lale . A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Autoplot stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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