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@@ -52,7 +52,7 @@ Open Observe became very bloated in its configurations and I believe that at the
There are a lot of documentation files in the source. Many of these are just as much for humans as they are for the AI agents. The .cursor directory is mainly for the AI to preserve some context about the project and provide examples of how things are done. Typically, each application will have its own ReadMe or other documentation based off of some issue that I ran in to. Most of it is more for reference for me rather than reference for a person trying to do an implementation, so take it for what it is. There are a lot of documentation files in the source. Many of these are just as much for humans as they are for the AI agents. The .cursor directory is mainly for the AI to preserve some context about the project and provide examples of how things are done. Typically, each application will have its own ReadMe or other documentation based off of some issue that I ran in to. Most of it is more for reference for me rather than reference for a person trying to do an implementation, so take it for what it is.
## AI Usage ## AI Usage
AI was used extensively in the process and has been quite good at doing templatey things once I got a general pattern set up. Indexing documentation sites (why can't we donwload the docs??) and downloading source code was very helpful for the agents. However, I am also aware that some things are probably too complicated or not quite optimized in the builds and that a more experienced person could probably do better. It is still a question in my mind on whether the AI tools helped save time or not. On one hand, they have been very fast at debugging issues and executing kubectl commands. That alone would have saved me a ton. However, I may have also wound up with something simpler. I think that it's a mixture of both because there were certainly some things that would have taken me far longer to find that the agent did quickly. AI was used extensively in the process and has been quite good at doing templatey things once I got a general pattern set up. Indexing documentation sites (why can't we download the docs??) and downloading source code was very helpful for the agents. However, I am also aware that some things are probably too complicated or not quite optimized in the builds and that a more experienced person could probably do better. It is still a question in my mind on whether the AI tools helped save time or not. On one hand, they have been very fast at debugging issues and executing kubectl commands. That alone would have saved me a ton. However, I may have also wound up with something simpler. I think that it's a mixture of both because there were certainly some things that would have taken me far longer to find that the agent did quickly.
I'm still using the various agents provided by Cursor (I can't use the highest ones all the time because I'm on the $20/mth plan). I learned a lot about using cursor rules to help the agent, indexing documentation, etc to help it out rather than relying on its implicit knowledge. I'm still using the various agents provided by Cursor (I can't use the highest ones all the time because I'm on the $20/mth plan). I learned a lot about using cursor rules to help the agent, indexing documentation, etc to help it out rather than relying on its implicit knowledge.