Quoting Andrew Quinn

2026-05-10 · Source: Simon Willison's Weblog · Field: Technology & Digital — Software Development & Engineering, Mathematics & Computational Sciences · Depth: Intermediate, quick

Summary

Andrew Quinn argues that the "guilt" of reinventing existing tools, like writing a custom search-and-replace instead of using `awk`, is a counterproductive trap for programmers and learners. He posits that reinventing a select few fundamental concepts, perhaps four or five in most domains or twenty to thirty in rigorous fields like computer science, is crucial for reaching the true frontier of knowledge. This hands-on, directed questioning approach, he claims, accelerates understanding and progress far more effectively than passive study, even if the latter consumes significantly more time. The core idea is that practical, limited reinvention is a necessary step in mastering a field.

Key takeaway

For software engineers or students grappling with the vastness of existing tools and libraries, embrace the occasional "reinvention" of a core concept. Instead of always seeking the pre-built solution, try building a few fundamental components yourself. This active engagement, even if the result is less optimized than an existing tool, will deepen your understanding and accelerate your journey to advanced problem-solving more effectively than extensive passive study.

Key insights

Reinventing a few fundamental concepts is essential for deep understanding and reaching knowledge frontiers.

Principles

• Limited reinvention accelerates learning.
• Practical application beats passive study.

In practice

• Build a custom tool before seeking existing ones.
• Ask directed questions during development.

Topics

• Programming Philosophy
• Software Development
• Knowledge Acquisition
• Learning Strategies
• Reinvention Paradox

Best for: AI Student, Software Engineer

Related on AIssential

• You're Using AI to Learn Faster. That's Exactly Why You're Not Learning — Over-reliance on AI for learning impedes deep understanding and skill development by circumventing necessary cognitive struggle.
• The Day I Realized Learning Databricks Is More About Patience Than Speed — Effective learning, especially for complex platforms like Databricks, prioritizes deep understanding and making connections over rapid topic coverage.
• Takeaways from Harvard CS Professor David J Malan — Effective CS education balances engaging pedagogy with foundational principles, adapting to AI's influence on learning and integrity.
• Fundamentals before tools — True software engineering prioritizes understanding fundamentals and business requirements over mere tool proficiency.
• The only abstraction I'm ok with, probably — Abstractions leak, shifting complexity and maintenance burden rather than eliminating it.
• How to stay relevant as a software developer — Human developers can gain an edge over LLMs by staying current with pre-print research and diversifying their knowledge.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.