Python

Your Excel says “we’ll sell 100 units.” A round, clean, deterministic number. What if you sell 120? Stockout, unhappy customer, contractual penalty. What if you sell 50? 50 units sitting in your warehouse, immobilizing capital that could be generating returns. The problem isn’t the forecast itself. It’s the arrogance of the single number. In Chapter 1 we built a “Quality Valve” that filters ERP noise. Now that we have a pure signal, we’re going to do something Excel can’t: measure uncertainty. ...

Engineers build complex systems but hate marketing. I built an autonomous agent army with Gemini to distribute my content. Here is the technical breakdown of what worked, the friction I encountered, and how it shapes my roadmap.

The ‘Organic Traffic Plateau’ is real. To overcome it without losing focus on engineering, I’m launching an extreme ‘dogfooding’ experiment: automating Datalaria’s distribution using Gemini, CrewAI, and GitHub Actions. This is the Master Plan.

While Project Weather’s AI prediction model is a highlight, its true robustness lies in its meticulously designed infrastructure. This post explores the significant enhancements in data collection, multilingual support, dynamic data filtering, and UI/UX that elevate the application to a production standard, demonstrating a holistic approach to complex web development.

From data collection to dynamic dashboards, now it’s time to predict! This post explores integrating OpenWeatherMap’s 5-day forecast and building our own 1-day AI prediction model using historical data, all visualized in our interactive frontend.

We kick off our weather project by building the engine: a Python script that talks to an API, saves historical data, and runs daily thanks to GitHub Actions. I’ll share the tricks and challenges!