I learned about the post-WW2 history of the letter Å in Scandinavian alphabets, the construction of Zalgo text through Unicode characters, and Artificial Analysis for benchmarking LLM API performance metrics like speed and cost.
In Scandinavia, Århus comes after Zürich because Å is a different letter. It was added by the Dutch after WW2 to distance themselves from the Germans. via
Zalgo text is where we combine multiple Unicode combining characters
I investigated Amazon Nova model costs, surveyed why employees avoid internal LLMs, and evaluated JavaScript text splitters. I also learned about Unicode characters in ChatGPT citations and how to install Docker on Windows without admin rights.
I found DuckDB significantly faster than Pandas and analyzed Claude 3's cost-effectiveness. I also learned about the Tavily search API, Vertex AI’s Model Garden, and how the isTrusted property prevents scraping on sites like Oracle Service Cloud.
I discovered Luma Labs Dream Machine for AI video generation and the LLM DataHub for training datasets. I also learned to prioritize duration over returns in compounding and received tips for running distraction-free workshops.
I explored Agentic RAG for complex retrieval, fine-tuning with LoRAX, and practical LLM strategies. Key takeaways include using N-shot prompting before scaling models, automating workflows via disposable apps, and leveraging context caching to significantly reduce inference costs.
I explored GPT functions for spreadsheets, Xata's free PostgreSQL API, and Nginx's least_conn load balancing. I also looked into GitHub Copilot's prompt construction and the importance of tracking evolving LLM capabilities and hardware-specific package managers.