Your agents are only as reliable as the context you provide. But no one knows how accurate their context is.
Falconer Knowledge Health solves this.
Accurate context means greater speed, less tokens, shorter prompts, and high-quality output from your agents.
on the bus in SF. only 10-20% of people bother to pay at all. regardless of socioeconomic status!
guy working on a macbook pro in the back? didn't tap.
woman in an arcteryx jacket? didn't tap.
dude wearing a tech co sweatshirt and different tech co backpack? didn't tap.
OpenAI Image V2 - Maskingtape Alpha
failed the test as usual and current all gen also failed it , i hope one day we will get this passed
Prompt : A validly scrambled Rubik's cube placed by a mirror, clearly showing its mirror reflection. No harsh light reflections.
@trq212 one observation: claude does not manage its own context well, at all, when it comes to subagents.
I wonder how much of this could be solved by teaching Claude to effectively context manage subagents, including continuing/steering them instead of just for 1-shot tasks?
/btw in claude code is incredibly helpful when working on unfamiliar domains. i can let claude run wild and simultaneously ask really in-depth questions with full context. fantastic idea, i hope it makes it to all the harnesses soon.
@tenobrus@akorinek "us" is very load bearing here. it's about what you tax, not how much
as long as tax revenue scales with something that scales with AI output, we'll be in an acceptable place. human income is probably not the right thing.