Final part of my #Jupyter series is live! 🐍🧪
Part 3: Real-World Code Examples
I’m sharing 4 practical ways senior engineers use notebooks:
1️⃣ API Archaeology (inspecting raw JSON)
2️⃣ Performance Audits (visualizing log latencies)
3️⃣ Algorithm Sandboxing (logic before implementation)
4️⃣ Interactive Runbooks (safe, documented migrations)
It’s "Literate DevOps" in action.
Read more: https://g.omid.dev/tyBMsXq

Jupyter, ChatGPT, Copilot (Part 3): Real-World Code Examples
This is Part 3 of a series on modern development workflows. Part 1: The Strategic Value of Thinking in Notebooks and Part 2: The Technical Guide to Jupyter Setup set the stage. Now, let’s look at actual code. In the previous parts, we discussed why Jupyter is a “thinking environment.” In this final part, we’ll walk through four concrete scenarios where a notebook outperforms a traditional IDE for a senior engineer. 1. API Archaeology: Mapping the Unknown When you’re dealing with a complex API, you don’t want to build a full client just to see what the data looks like.



