A look at how GitHub rebuilt GitHub Actions’ core architecture and shipped upgrades to improve performance, workflow flexibility, reliability, and developer experience.
This article discusses how NotebookLM can replace several tools in a typical productivity stack, specifically for mind mapping, YouTube learning, progress tracking, and document sorting/organizing.
* **Mind Mapping:** NotebookLM's interactive mind maps are more robust and useful than dedicated diagramming tools like Mermaid, especially for visual thinkers and UX design tasks.
* **YouTube Learning:** NotebookLM streamlines YouTube learning by analyzing transcripts, allowing simultaneous viewing, AI-assisted summarization, and note-taking – eliminating the need for apps like Notion or Obsidian for this purpose.
* **Progress Tracking:** While NotebookLM's timeline feature is gone, it can still generate detailed tables to track projects and tasks based on provided sources, offering insights and improvement suggestions.
* **Document Sorting & Organizing:** NotebookLM helps organize messy documents by prompting it to categorize and sort files, making the manual naming and organization process more efficient.
* **Mind Mapping:** NotebookLM's interactive mind maps are more robust and useful than dedicated diagramming tools like Mermaid, especially for visual thinkers and UX design tasks.
* **YouTube Learning:** NotebookLM streamlines YouTube learning by analyzing transcripts, allowing simultaneous viewing, AI-assisted summarization, and note-taking – eliminating the need for apps like Notion or Obsidian for this purpose.
* **Progress Tracking:** While NotebookLM's timeline feature is gone, it can still generate detailed tables to track projects and tasks based on provided sources, offering insights and improvement suggestions.
* **Document Sorting & Organizing:** NotebookLM helps organize messy documents by prompting it to categorize and sort files, making the manual naming and organization process more efficient.
A post with pithy observations and clear conclusions from building complex LLM workflows, covering topics like prompt chaining, data structuring, model limitations, and fine-tuning strategies.
The workflow triggers on push events on the 'master', 'main', and 'fix' branches, and runs on Ubuntu. It installs Make, caches the Cosmocc toolchain, sets up Cosmocc and Ape Loader, builds the project, makes a specific Llamafile, executes the Llama CLI CPU, and more.
Big claim today: We've benchmarked Windmill to be the fastest self-hostable generic workflow engine among Airflow, Prefect and even Temporal. For Airflow, there is quite a margin, up to 10x faster
