The article discusses the growing trend of running Large Language Models (LLMs) locally on personal machines, exploring the motivations behind this shift – including privacy concerns, cost savings, and a desire for technological sovereignty – as well as the hardware and software advancements making it increasingly feasible.
A technical article explaining how a small change in async Python code—using a semaphore to limit concurrency—reduced LLM request volume and costs by 90% without sacrificing performance.
This article details the billing structure for GitHub Spark, covering costs associated with app creation (based on premium requests) and current limits for deployed apps. It also outlines future billing plans for deployed apps once limits are reached.
Learn how to summarize large documents using LangChain and OpenAI, addressing contextual limits and cost effectively. This tutorial covers text preprocessing, semantic chunking, K-means clustering, and document summarization.
