There is a point in many serverless platforms where a Step Functions workflow that once felt elegant starts to feel like a mini application platform of its own. I have seen this happen in teams that are doing many things correctly: they standardized orchestration, they improved visibility, and they moved fragile glue logic out of Lambdas. Then six months later, the workflow has 100+ states, a maze
Overview Let's get our hands dirty. This part covers the full setup and the actual demo: deploy PayLedger to both regions, wire up Route 53 failover, configure the Agent Space, inject three simultaneous faults, and walk through exactly what the agent found. Quick recap from Part 1: PayLedger is a demo payment ledger deployed to ap-southeast-1 (primary) and ap-northeast-1 (secondary) with Route 5
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
[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