My Hugo blog was downloading 3.6 MB of JavaScript and 40 KB of external CSS on every page load. For a static blog with mostly text and a few diagrams, that was absurd. Here is how I fixed it. HTML: 86 KB JavaScript: 3.6 MB (Mermaid + KaTeX) CSS: 40 KB (KaTeX stylesheets) Problem: render-blocking scripts loaded on every page for math and diagrams Adding minifyOutput = true to hugo.toml shrunk HTML
The DataFrame class (from Pandas) is a work of art. Even if you never "do data", priceless lessons can be gleaned by studying this class. It starts simple enough. Usually you will create a DataFrame by ingesting from a CSV file or database table or something. But you can whip up a small one like this: import pandas as pd df = pd.DataFrame({ 'A': [-137, 22, -3, 4, 5], 'B': [10, 11,
When we talk about Data Visualization and Dashboards, enterprise tools like Tableau or PowerBI often dominate the conversation. However, for Data Scientists and Developers, these GUI-based tools can feel restrictive. What if you need complex machine learning integration, custom UI logic, or automated CI/CD deployments? Enter the holy trinity of Python visualization tools: Streamlit, Dash, and Boke
Optimisation HLS pour DOMTOM Ce dépôt documente une approche orientée ingénierie réseau pour optimiser la distribution HLS (HTTP Live Streaming) dans les territoires DOMTOM. L’objectif est d’améliorer la stabilité de lecture, la latence effective et la robustesse face aux variations de gigue, tout en respectant les contraintes de décodage client, de parsing de manifestes, et de routage ISP. Pers
Contournement du flux vidéo FAI DOMTOM — FAQ Développeur (réseau, protocoles, parsing) Point de vue : cette FAQ est rédigée du point de vue d’un ingénieur logiciel/réseau traitant la couche transport, le routage ISP, la signalisation de session et la reconstruction applicative (parsing, dé-multiplexage, adaptation de manifestes), spécifiquement dans des contextes DOM-TOM où les chemins d’achemi
[05] When to Pull the Trigger on FIRE — Monte Carlo Says You're Already Free This is Part 5 of a 6-part series: Building Investment Systems with Python "You need 25x your annual expenses." That's the standard FIRE rule. For ¥9.6M annual expenses, that's ¥240M. Most people see that number and think: "I'll never get there." But the 25x rule assumes a fixed 4% withdrawal rate, zero income, zero ada