Its one of the options. Other is sniping new pools and copy trading. Im trying to develop zero/same-slot tx landing with shredstreams first, because its basically needed to be competitive.
I would say that in most cases you dont need fancy algorithms and data structures, you just need to stop treating RAM as a rough notebook. Allocating in a loop, not reusing buffers and pointer chasing are a performance killers and are quite easy to avoid before they are introduced to code. Other than that, just be more thoughtful about network and disk usage and nested loops. Its a sum of stupid things that make your program slow.
Software engineering is a domain on its own, just a technical, not a business one. Good luck with looking for a once in a million data race or deadlock bugs. Good luck with synchronizing system distributed over a whole globe. And good luck with keeping your domain knowledge up-to-date while developing and maintaining your vibecoded system. Assuming you can even provide enough details and deterministic specification to AI agent, because understanding business and having knowledge is not equal to being able to write down specific and concise rules to make a system out of it.
And if you think LLM are (or will be) good enough to not care about software part, what makes you think that your domain will not be completely resolved by AI?
You dont need to measure the code complexity, you can completely hallucinate those numbers if you feel that code is too complex and then watch how LLM will respond.
Polishing my dashboard for tracking profit on Solana blockchain for set of wallets: https://imgur.com/813aQsO
I want to extend it with a simple overview of most recent profitable wallets to look for new "metas" that i could profit on. This project may or may not end as open source eventually, but i currently keep it private.
If you're looking for feedback and ideas you can just ask ChatGPT. Or maybe you just crave human interaction and doing something together with other human?