Reducing toil with AI

LLMs are at the point where they can reduce toil for software engineers across a host of use cases. Here we will explore a few I’ve thought of.

Reduces time spent on toil

• Resize this button -> AI
• Refactor this function -> AI
• Connect these endpoints -> AI
• Write more tests -> AI
• Add more comments -> AI
• Create PlantUML diagrams from a sketch -> AI
• First pass code reviews -> AI

Reduces time spent researching small things

• AI is a better stack overflow
• Examine stack traces 
• Easier to write architecture documents 
• Faster development of small utilities

What AI still cannot do

• Test if a library will work for your use case 
• Respond to outages 
• Decide the product direction
• Argue with stakeholders 
• Yell at people who want to do stupid things

A few opportunities to reduce operational toil

• AI can review graphs and notice changes
• AI can check if a website is down
• File bug reports 

For me the most valuable use of Claude as a coding assistant has been how it makes getting started much easier. Usually, to program a swift game I’d have to spend a couple hours breaking into swift development and building up my program off of examples. Claude was able to create a basic version of the game I wanted off of a detailed prompt. It didn’t manage to create the game in one shot, I had to edit the code a bit to get it to run. But it turned a project that would have taken me a few days into one that took a few hours.