Anyone who has tried to share a screen recording or screenshot while documenting a tool or internal process has probably run into this problem: API keys or account information are visible in the footage, so you can't publish it as-is.
Recently, locally-running LLMs (Large Language Models) have become quite sophisticated. With models like Llama, Qwen, and Mistral publicly available, you might find yourself wondering: "Which model best fits my use case?"
Guide to Integrating Google Colab with GitHub
LLM services like ChatGPT and Claude are incredibly convenient, but there are moments when you hesitate to type in confidential business information or personal content. At the same time, new local LLM models keep appearing and there's a natural urge to try them out casually. Yet spinning up Ollama on a local GPU is a high bar — the setup effort and hardware cost are both real obstacles.
I built a tool that runs ffmpeg inside Google Colab's free environment and converts screen recordings directly to GIF or MP4.
When using coding assistants like Claude Code or Continue, you often run into situations where API costs add up quickly, or you'd rather not send your code to an external service. There's also the frustration of wanting to try out lightweight local LLMs but finding that your local GPU is too slow to be practical.
Recently, AI tools that "get smarter by reading your data," such as NotebookLM and Claude Projects, have become incredibly useful. When researching how to use new libraries or brainstorming specifications, feeding "official documentation" into these AIs drastically improves the accuracy of their responses.