Sledgeworx Software

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.

Great article on mac setup

Lots of tools are outdated by default. Got to take advantage of this blog post since I got a new laptop this summer.

https://matt.sh/setup-2021-late

Operating Systems use out of date assumptions

Interesting talk on how modern systems on a chip differ from how we imagine computers to actually work. Over the last two decades hardware has gradually abstracted away many features from the actual operating system. The computer board is now a collection of sub-computers which cooperate to mimic the operation of an actual traditional computer. 

The system describe here is reminiscent of a micro service environment where you need to communicate with many different protocols to get the job done. I wonder if we can explore migrating ‘cloud’ microservice techniques down to the CPU level. 

Passive Radar for finding meteors

You can detect meteors using a set of clock synchronized radios. The way it works is you monitor a reference frequency at perhaps 180MHz and can detect changes in it as meteors burn up in the atmosphere. 

Fine grained services

Microservices have been the thing for over 15 years. They are great in large companies with CI/CD environments. But as your situation drifts farther away from the ideal microservice use case traps abound. 

Building a new service for one endpoint 

If you find yourself having a conversation where you need to create a new endpoint somewhere, but adding it to any of your existing services would break the concept of that microservice. Turning it instantly into a ball of mud with no clear purpose. You have fallen into this trap. microservice does not mean each service has only one HTTP endpoint. That use case is better served with Cloud functions like AWS lambda. 

The problem here is that we have gone too far in splitting up the monolith. Splitting a monolith with 100 HTTP endpoints into a dozen or so services with eight endpoints each is great. Splitting up a monolith with 100 endpoints into 100 services is counter productive. Instead of having an actual purpose the single endpoint microservice becomes the xyz endpoint microservice. 

Endpoints are things that microservices empower. An endpoint in of itself should never justify the creation of a microservice.