The previous two posts covered how events flow from the SDK to the UI. This post focuses on visualizing one specific type of event: tool calls. Tool invocations are the most frequent operations in an Agent application. A typical task might call tools twenty or thirty times—reading files, writing files, executing commands, searching code. If every tool call renders as the same gray block, it's hard
Post 1 covered how AgentBridge converts the SDK's AsyncStream<SDKMessage> into [AgentEvent]. This post looks at what [AgentEvent] becomes — how TimelineView renders 18 event types, handles scroll behavior, and stays smooth when the event count gets large. TimelineView is the main body of the workspace, filling all the space between the sidebar and the input box. Its view hierarchy is shallow: Time
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