Author: nsledgianowski

Have you written your last for loop? 

Lately I’ve been doing a lot of coding using the AI Agent Claude Code. It is a terminal application which allows you to chat to it live while it attempts to code whatever you direct it to. My game Forward Blaster  https://github.com/Sevii/forward-blaster was written by AI except for a few places where I edited constants. Needless to say the game includes a lot of loops. But I didn’t write any of them. I never felt the need to leave the AI agent workflow and start manually coding. 

Which leads to the question ‘Have you written your last for loop?’ I don’t think I have but it’s very likely that time is coming. AI coding agents are quite good. You can tell them what type of iterator to use if you like. But in general it doesn’t matter a lot. 

AI Agents also assist with debugging and architecture but not to the degree they do at writing code. LLMs can recognize many simple bugs and fix them without much human interaction. Where they really struggle is functionality that is not to specification. Even if you’ve given it the specification it cannot consistently recognize if something works but in the wrong way. And if there is a ‘one right way’ in this particular context your best bet is to tell the AI to use that ‘one right way’ specifically. 

Software engineering is changing a lot. We used to write blog posts about minimizing toil so that engineers could focus on value add parts of the job. Few expected we could eliminate the time spent coding (the fun part) freeing up time for other engineering activities like planning, architecture, estimating, security review, oncall and the like. 

Checkout my latest book Code Without Learning to Code on Amazon!

Vibecoding Demo

I released a short demo of AI assisted vibe coding to the youtube channel. Basic games like this are one of my favorite use cases for vibecoding. LLMs are pretty good at it and the pay off is quick. Back in my teens I tried to learn programming to make games. I’d have gotten a lot farther if we’d had AI then instead of trying to figure out C++ from a two inch think book!

To learn more about AI assisted ‘vibecoding’ check out the book Code Without Learning to Code at https://codewithoutlearningtocode.com.

The original launch of Copilot soured a lot of people on AI for coding

In 2023 I was one of the many software engineers whose management pushed them into trying Github Copilot. I was not impressed. At the time Copilot was basically pointless for Java developers. IntelliJ already had Intellisense which did everything Copilot did, but more deterministically. 

In retrospect autocomplete was not the use case for AI to assist in the programing process. Personally, I find the AI chatbots to be very useful coding assistants for writing scripts and functions. And this last year AI coding Agents have really come into their own. 

I’ve never been a python guy despite using it for light scripting tasks for a decade. But lately I’ve been using it a lot more because Claude can consistently create working 500~ line python scripts in a couple prompts.  The days when AI spit out code that didn’t even run are mostly in the past now. 

AI is just much better at coding now than it was in 2023. And I suspect the ‘autocomplete’ use case is just a bad one for AI. The more powerful models tend to produce output more slowly. You are looking at a 15+ second wait with edge models. In Claude Code and other agents, multiple 15+ second waits is pretty annoying. It’s like compiling huge java projects. For me I find the chatbot model works great. You write up a prompt, provide examples and context then Claude spits out a running program. You test it and iterate. Autocomplete has the problem that edge models are never going to have the latency that you want when you push ctrl-shift-enter. It just feels better to use an agent or chatbot. 

Code Without Learning to Code

https://codewithoutlearningtocode.com

I’m working on a new book on Vibe Coding for people who don’t know how to code. Using LLMs to build simple programs unlocks a lot of programming ability for people who either tried and failed to learn to program or never got started. You no longer need to learn the basics of programming logic or syntax to build useful programs to solve your problems. 

The current target of the book is people who don’t know how to code but are willing to learn to run and test computer programs they create through Vibe Coding. 

As part of this process I’m doing some research comparing free and paid LLM models for programming use. 

For each model I pasted the same prompt in and took the first result. I saved the code into a folder which already had pygame installed via pip and ran it directly.

“Please create a game for me using python and pygame. In the game the player should navigate a 2d space using the arrow keys. In this game there should be a maze like region with rocks and stalagmites. Inside the region should be chests which contain gold. The player should be able to navigate the maze and collect gold from the chests.”

Anthropic Claude Haiku 3.5 (free)

https://github.com/Sevii/vibecoding/blob/master/MakingGames/BlogPost_LLM_Comparison/haiku35_chest_game.py

Anthropic Claude Sonnet 3.7 (paid)

https://github.com/Sevii/vibecoding/blob/master/MakingGames/BlogPost_LLM_Comparison/claude37_sonnet_chest_game.py

Gemini 2.0 Flash (free)

https://github.com/Sevii/vibecoding/blob/master/MakingGames/BlogPost_LLM_Comparison/gemini_2_flash_chestgame.py

Gemini 2.5 Pro Experimental (paid)

https://github.com/Sevii/vibecoding/blob/master/MakingGames/BlogPost_LLM_Comparison/gemini_25_pro_experimental_chest_game.py

ChatGPT (free) 

https://github.com/Sevii/vibecoding/blob/master/MakingGames/BlogPost_LLM_Comparison/free_chatgpt_chest_game.py

It’s interesting to see how paid models differ from free models. But we are getting working code on the first pass from both free and paid models. 

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.