In my previous article about treating architecture documentation as a first-class asset, I had a great discussion in the comments about enforcing architectural rules. I promised to share materials from my recent Google Developer Groups workshop. The workshop is now finished! Here is the story of how I built an AI Quality Gate, how it helped me solve the internal "CEO, CTO, CFO, CISO" conflict, and
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,
In my previous article, I documented how I installed Terraform on macOS using Homebrew and fixed a Zsh autocomplete issue. In this article, I am going to be using terraform to provision, update, and destroy a simple set of infrastructure using the sample configuration provided by hashicorp The goal is to understand the basic Terraform workflow: Write configuration Authenticate to Google Cloud Ini
On April 30th I got an email from Google about something called GEAR, their new program for building AI agents using ADK, the Agent Development Kit. I signed up, watched the intro video, and had a strange feeling of recognition. The pattern was familiar. Define tools. Write descriptions. Connect an AI model to those tools. Let the model decide which tool to call based on what the user asks. I buil
VotePath -- an AI-powered multilingual voting guide for first-time voters. The Problem: Why Don't People Vote? What is VotePath? 🤖 Gemini-Powered AI Assistant: A conversational AI built with the Google Gemini API that answers specific election queries in real-time. 🛠️ The Tech Stack Building the UI components and wiring up the Gemini SDK went smoothly using an intent-driven development approach.
For years, the dream of a truly autonomous, always-on AI assistant has felt just out of reach — a concept relegated to-fi or limited by fragile, stateless nature of most chat interfaces. We’ve grown accustomed to assistants that forget us the moment we close the browser tab. But what if we could change the fundamental architecture? What if we could build an AI agent that doesn't just converse, but
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
[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