Notebook soup: practical Gemini recipes for impatient builders

What it does This repo is a loose cookbook of Jupyter notebooks, Python scripts, and one JavaScript file demonstrating how to use Google DeepMind’s Gemini (and some Gemma) across common integration points. It covers everything from function calling and structured outputs to browser automation, video generation with Veo3, and plugging Gemini into the OpenAI SDK, LangChain, or PydanticAI.

The interesting bit Rather than toy demos, the snippets target production-adjacent patterns: context caching (claiming up to 75% cost savings), sequential function calling, Model Context Protocol agents, and few-shot PDF extraction. It is essentially a curated answer to the question “how do I actually do X with Gemini?”

Key highlights

• Mixes guides (ReAct agents, memory, agentic patterns) with standalone examples (MCP, CrewAI, batch API).
• Includes non-obvious integrations: native image output, text-to-speech, YouTube transcription, and URL-as-context.
• Also covers Gemma 3 27B with the GenAI SDK and function calling.
• One JavaScript example for native image generation.

Caveats

• The README is a flat file list with one-line descriptions; you’ll need to open notebooks to judge depth and quality.
• The author describes the contents as “personal tiny samples,” so expect inconsistency rather than a polished curriculum.

Verdict Grab this if you’re already using Gemini and need a quick reference for specific patterns or third-party framework glue. Skip it if you want a structured course or deep architectural explanations; this is a snippet drawer, not a textbook.