I manage socials for Aisle, myself, and a few other founders out of a single knowledge base. One piece of that is a "tweet log master" context file, and the job behind it never changes: pull a user's recent content and engagement, then write it back to a memory file. I've built that same job three different ways over time, and the gap between them says a lot about where Aisle has gone.
Two-prompt legacy workflow
A scheduled workflow loops over a list of users, pulls the data with one prompt, then hands it to a second prompt that merges the new data with the existing file and reprints the whole thing. Clean and simple to build. The catch is that it reads and writes the entire file every run, around 50,000 tokens, and that number only climbs as the log grows.
Single-prompt legacy workflow
Same result, one prompt. I dropped the second prompt in favor of two loops, some variable manipulation, and a regex lookup to slot templates into the right spots. Far more efficient at roughly 5,000 tokens. It's also a genuine pain to build, debug, and maintain.
Aisle Tasks
The whole thing written natively in Python: set up the variables, pull the file, run the prompt, splice by section headers, write back to the memory space. Same ~5,000 tokens as the single-prompt version, but much easier to reason about, debug, and maintain. It runs faster too.
If you know a little Python, this is where it's landed for me.
How are you handling your automations? Reply and tell us.