🔗 My code used 10GB RAM. Now it uses 300MB.

Years of writing inefficient Python code taught a valuable lesson: better hardware isn't always the answer. In a recent project involving document ingestion from PDFs, a developer experienced 10GB of RAM usage, leading to slow performance and frustrating delays. By implementing three key tricks—avoiding memory-heavy libraries like Pandas, leveraging generators for efficient data handling, and enforcing garbage collection—the same process now operates at only 300MB RAM usage. These changes not only streamlined execution but also significantly reduced runtime, from over 94 minutes to just 41. The article serves as a reminder that careful optimization can transform performance without reliance on advanced tools. Simple techniques often lead to impactful improvements, proving that sometimes the best solutions are hiding in plain sight.