sspaeti

#!/usr/bin/env python3
# /// script
# dependencies = ["networkx", "matplotlib", "scipy"]
# ///
"""Stand-in render of brain.orbit.json (force layout). Usage: uv run render_graph.py <json> <out.png>"""
import json, sys
import networkx as nx
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

sspaeti

"""
Swiss heating threshold Flight.

Loads Swissgrid "Energieuebersicht Schweiz" 15-min data and MeteoSwiss daily
station data into MotherDuck, runs data-quality checks, and estimates the
heating threshold (temperature below which end-user consumption rises) with a
hinge regression controlling for weekends and public holidays.

Config (env vars):
  DB        target MotherDuck database (default energy_weather)

sspaeti

{{- $content := .content | default .Page.RawContent -}}
{{- $brainBaseURL := .Site.Language.Params.brainBaseURL | default "https://ssp.sh/brain/" -}}

{{- /* Process wikilinks with proper URL formatting and alias support */ -}}
{{- $content = replaceRE `\[\[([^\]]+)\]\]` `<WIKILINK_TEMP>$1</WIKILINK_TEMP>` $content -}}

{{- /* Now process each wikilink individually */ -}}
{{- $wikilinks := findRE `<WIKILINK_TEMP>([^<]+)</WIKILINK_TEMP>` $content -}}
{{- range $wikilinks -}}
  {{- $linkContent := replaceRE `<WIKILINK_TEMP>([^<]+)</WIKILINK_TEMP>` "$1" . -}}

sspaeti

#!/usr/bin/env python3
"""
Extract YouTube subscription data and create RSS feed URLs
"""

import json
import re
import html
from pathlib import Path

sspaeti

--connect to MotherDuck databases
-- configure MOTHERDUCK_TOKEN and run DuckDB that connects to all shared Databases with
-- `duckdb "md:"`
-- and then run this query:
WITH language_counts AS (
      SELECT
          language,
          COUNT(*) AS count
      FROM (
          SELECT UNNEST(STRING_SPLIT(LanguageHaveWorkedWith, ';')) AS language

sspaeti

-- Query the API directly and flatten the nested JSON structure
WITH raw_data AS (
  SELECT * FROM read_json_auto('https://public.api.bsky.app/xrpc/app.bsky.feed.getAuthorFeed?actor=did:plc:edglm4muiyzty2snc55ysuqx&limit=10')
),
unnested_feed AS (
  SELECT unnest(feed) as post_data FROM raw_data
)
SELECT 
  -- Post basics
  post_data.post.uri as post_uri,

sspaeti

import xml.etree.ElementTree as ET
import PyRSS2Gen as rss
import datetime

# Parse the sitemap.xml
tree = ET.parse("book/sitemap.xml")
root = tree.getroot()

# Define the RSS feed basic info
rss_feed = rss.RSS2(

sspaeti

/* Globals */

:root {
    --sidebar-target-width: 300px;
    --sidebar-width: min(var(--sidebar-target-width), 80vw);
    --sidebar-resize-indicator-width: 8px;
    --sidebar-resize-indicator-space: 2px;
    --page-padding: 15px;
    --content-max-width: 750px;

sspaeti

"act","prompt"
"Rust Developer", "I want you to act as a expert Rust software developer. I will provide some specific information about a Rust project and you will have to come up with a solution. I will provide you with a Rust project that has a long code in one file. I want you to write well structured code"
"Technical Author and Data Engineer", "I want you to act as an expert Data Engineer and someone fond of diving into the latest in open-source data engineering on my blog. Your voice isAuthentic, friendly succinct, and informative with the goal of value-adding to the readers. The writing whenever possible in the I form. So I can be honest and trustworthy. You try to give clues and tools concrete and extremely specific. The Key is to inspire instead of describing everything. You tell a story and involve the user with well structure such as 1. writing about now 2. us 3. self like described in the "Public Narrative" with Collective Action and Power now us and self. You help the user by highlighting importan

sspaeti

"Convert exported HTML pages to Markdown pages recursively where you start this script from"
import os
from markdownify import markdownify
from pathlib import Path


for dirpath, dirs, files in os.walk("."):
    print("--" + dirpath)
    for filename in files:
        if filename.endswith(".html"):