Where in the past automation often meant spending more time to author scripts than they would end up saving, now we can just tell our computers what to do.
Finding good workflows is still a challenge.
The internet is full of prompts, skills, etc. where it is hardly clear if they result in behavior that is preferable to the default.
I also find it interesting to distill findings and preferences from your current task into reusable skills or instructions so that the next task's output is already more to your liking with the first attempt.
Between model and harness improvements, my own learning, and the improvements to my setup, it's exciting to see significant progress over time.
Before AI, with 10 years on the job, things were a bit boring unless I switched to another stack where I could learn new things.
user43928 · · focus · HN ↗
Claude Code has an output style setting that I set to "Concise", with no apparent effect.
I am told this is merely something in the system prompt that the model tends not to pay attention to with large contexts.
Opus 5.5 writes whole essays at the end of the turn, with the important actionable steps somewhere at the bottom.
When prompted to give a concise summary, it usually overshoots into a super short summary and then you have to dig into the details again anyway.
In general I find Opus 5.5's writing to still have more "ticks" or "Claudisms" than the OpenAI models.
Its explanations often appear overcomplicated for simple concepts.
Sure, it's leagues above the ridiculous writing of Opus 5, but Anthropic still has a long way to go here.
derencius · · focus · HN ↗
user43928 · · focus · HN ↗
I guess I could take some lengthy example explanation, and have it try various instructions and test what results in output that I find preferable.
Maybe I'll give that a try, thanks!
MrsPeaches · · focus · HN ↗
user43928 · · focus · HN ↗
Where in the past automation often meant spending more time to author scripts than they would end up saving, now we can just tell our computers what to do.
Finding good workflows is still a challenge.
The internet is full of prompts, skills, etc. where it is hardly clear if they result in behavior that is preferable to the default.
I also find it interesting to distill findings and preferences from your current task into reusable skills or instructions so that the next task's output is already more to your liking with the first attempt.
Between model and harness improvements, my own learning, and the improvements to my setup, it's exciting to see significant progress over time.
Before AI, with 10 years on the job, things were a bit boring unless I switched to another stack where I could learn new things.