Wondering how big of a percentage have global memory across chats enabled. I always feel like those memories would sooner or later have negative impacts on output quality.
Nice write up of your findings. Enjoyed reading an article written by a real human.
Do you have tips on how to get better at design, now that everything looks like the same AI-slop online?
Like how do you come up with a cohesive looking set of components for example?
I use design tokens but they are not enough to get to a coherent design where components look like they belong together and kind of stand out from other designs.
Reading this hurt so much. Please just write articles by hand. I don’t care about perfect grammar and I don’t care about your article sounding „native“ or not.
But I do care about not having to read the word „genuinely“ a hundred times just because Claude likes it so much.
This has been our experience as well. Initially we had a list of tools that the agent could use to manipulate a data structure in certain ways. This approach was quite brittle.
Now we are using a small DSL (domain specific language) and a single tool where the agent can input scripts written in the DSL. We are getting more dynamic use-cases now and wrong syntax can easily be catched by the parser and relayed to the agent.
In addition to your experience I notice that through AI contributions our team reaches sprint goals way too early and PMs aren’t catching up with creating new tasks.
Did you notice a significant decrease in delivery speed (including debugging, fixing stuff in AI contributions)?
I wonder whether reducing AI usage might even help with cognitive load. The last few months I felt increasingly exhausted after pushing through PR after PR (although it is quite fun to do this on personal projects).