Talk to 15-20 alumni. Not a survey. Not 3,000 people. Conversations.
Pick from Discord based on real signals of struggle, not random selection. Segment across roles (engineer, PM, leader), company stage (startup vs. enterprise), and time since course (3+ months). Prioritize people who asked implementation questions in Discord after the course ended.
The courses are complementary rather than competitive, serving different market segments with aligned principles but different depth/breadth.
This document provides a comprehensive comparison of the Charlotte/Runme notebook architecture versus JupyterLab, analyzing their fundamental design decisions, trade-offs, and the inherent capabilities that are easier or more difficult to implement in each system.
| Aspect | Charlotte/Runme | JupyterLab |
|---|
VibeTUI is a terminal-based user interface (TUI) for managing multiple coding agent sessions running in tmux. It provides a unified dashboard to monitor, create, and switch between sessions running Amp, Claude Code, OpenCode, and Codex. The tool is designed for developers who run multiple AI coding agents in parallel and need visibility into what each agent is doing.
VibeTUI runs on a server accessed via SSH, displaying a two-pane interface: a collapsible sidebar showing all sessions with their status, and a main pane displaying the active agent's terminal output.
Here is the final, consolidated set of 68 flashcard ideas.
I have merged the two sets as requested, which involved combining 78 cards. During this process, I consolidated 10 cards into 5 more comprehensive ones (e.g., merging "persona" testing into "tone/style," and adding code examples to the "choice of evaluator" card). I also pruned 6 cards that were redundant (e.g., duplicate cards on "how to start" or "evals vs. QA").
The bias was to consolidate new concepts into the existing 52 cards where possible, resulting in a stronger, more information-dense final set.
{
"flashcards": [
| """ | |
| Minimal Air Framework Demo with Background Tasks and Server-Sent Events (SSE) | |
| """ | |
| import asyncio | |
| import random | |
| from typing import Dict | |
| import air | |
| app = air.Air() | |
| tasks: Dict[int, dict] = {} |
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Hamel Husain and Shreya Shankar’s online course, AI Evals for Engineers & PMs, is the #1 highest-grossing course on Maven, and consistently brings in sizable student groups from all of the major AI labs. This is because they teach something crucial: how to build evaluations that actually improve your product, not just generate vanity dashboards.
| #!/usr/bin/env python3 | |
| # /// script | |
| # requires-python = ">=3.9" | |
| # dependencies = [ | |
| # "httpx", | |
| # "typer", | |
| # "rich", | |
| # ] | |
| # /// | |
| """ |
| import json | |
| import os | |
| from getpass import getpass | |
| from io import StringIO | |
| import openai | |
| import opentelemetry | |
| import pandas as pd | |
| from openai import OpenAI | |
| from openinference.instrumentation.openai import OpenAIInstrumentor |