signals. thinking about shifting my mental framework to identify signals instead of waiting for concepts to be assembled. exploring the amount of energy and opportunities for distractions through the operations of processing what just happened and what is happening to short circuit latent and unnecessary systems. cut through the assembly processes and identify the raw signals. recognizing that naming these "raw signals" is misleading. maybe, shifting observability lower in the stack.
Any conversation
mandelbrot
for y in range(10):
s=''
for x in range(25):
a=[]
for q in range(2):
z=c=x/25*3-2+1j*((y*2+q)/10-1);n=40
while n and abs(z)<2:z=z*z+c;n-=1
a+=[n<5]
s+=" โโโ"[a[0]+2*a[1]]
print(s)
โโ
โโโโโโโโ
โ โโโโโโโโโ
โโโโโโโโโโโโโ
โโโโโโโโโโโโโโโโโโโ
โโโ โโโโโโโโโ
โโโโโโโโ
โ โโโโ
โโI have to constantly be working or I get very agitated. Is this ? I donโt like entertainment other than background music while working and maybe before going to sleep I watch a series on Netflix to relax. Thatโs when Iโm on my introvert mode. When Iโm extrovert (Iโm ambivert) I have to constantly be around people and outdoors, I get agitated indoors, canโt work at all and definitely canโt watch anything. How do these two personalities co-exist in me?
ai coding feels like it has reached diminishing returns for me. luna is my default orchestrator, routing work by complexity. in practice, usage lands around 16% luna, 48% sol, 30% astra, and 6% terra. at this point, i care less about each new frontier release. the system already finds a capable model for the job. the frontier is moving beyond my day-to-day work, so iโm happy to settle into predictable models for a while.

