The problem that setup creates

If posts execute Python, the build server needs Python. And not just Python — the same versions of the same libraries, and sometimes the data, or the build fails on a machine that is not mine. That is a brittle dependency to hang a personal site on.

The fix is one line of configuration:

execute:
  freeze: auto

With freeze, Quarto executes code locally and stores the results in _freeze/, which is committed to the repository. Continuous integration then renders the site from those stored results. It installs Quarto and nothing else — no Python, no libraries, no data. Code is re-executed only when I change the document that contains it.

So the environment lives in exactly one place: my machine, pinned with uv.

requires-python = ">=3.12"
dependencies = ["jupyter>=1.1", "pandas>=2.2", "matplotlib>=3.9"]

Proof it actually executes

The figure below is not an image file. It is drawn when this page is rendered, from the SHAP importances of my Alicante housing market analysis — the five features that move property prices the most in that dataset.

import matplotlib.pyplot as plt

features = ["Sea views", "Elevator", "Longitude", "Latitude", "Surface area (m²)"]
importance = [0.08, 0.08, 0.13, 0.13, 0.31]

SURFACE, SERIES, INK, MUTED = "#fcfcfb", "#2a78d6", "#0b0b0b", "#52514e"

fig, ax = plt.subplots(figsize=(7, 3.1), facecolor=SURFACE)
ax.set_facecolor(SURFACE)
bars = ax.barh(features, importance, height=0.55, color=SERIES)

# Direct labels replace the x-axis: five bars do not need a scale.
for bar, value in zip(bars, importance):
    ax.text(value + 0.007, bar.get_y() + bar.get_height() / 2, f"{value:.2f}",
            va="center", ha="left", color=MUTED, fontsize=10)

ax.set_xlim(0, 0.37)
ax.set_xticks([])
ax.tick_params(axis="y", length=0, colors=INK, labelsize=11)
for side in ("top", "right", "bottom"):
    ax.spines[side].set_visible(False)
ax.spines["left"].set_color("#d8d8d4")

plt.tight_layout()
plt.show()

Surface area dominates, but latitude and longitude together carry just as much weight — in Alicante, location is not a tiebreaker between similar flats, it is a primary driver of price.

What the pipeline looks like end to end

1. Write a post, run quarto preview, see it execute locally.
2. Commit the post and _freeze/.
3. GitHub Actions installs Quarto, renders, and publishes to GitHub Pages.

The build is fast because nothing is computed twice, and it is reproducible because what CI publishes is exactly what I saw locally.