Thermodynamic Computing from Zero to One
extropic.ai17 pointsby achille3 comments
typing | code
latency | integration
--------------------------
cider low | meh
mdproxy low | great
ssh+vi med | meh
rdp+iJ crushing | great
- local data residency & sovereignty
- latency
- bandwidth
- regulatory climate
- competition
yours yours+opt claude
---------------------------------------
Time (ns) 40.9 36.4 38.7
sqrt/iter 3 2 1
Instructions 207 187 241
yours claude
------------------------------
Time (ns/call) 40.5 38.3
sqrt per iter 3 1
Accuracy 4.8e-7 4.8e-7
// Rotate (c,s) by angle dt, then renormalize to unit circle
float nc = c + dt*s, ns = s - dt*c;
float len = sqrt(nc*nc + ns*ns);
c = nc/len; s = ns/len;
Filename: ..._simple_compose_01kdcxamjmekery2m9tay43szn.png
- "simple_compose" + LSB common (e.g ideogram) output
Resolution: 1536x1024
- Exact native output of GPT-image-1, Gemini/Imagen, Flux models
PNG encoder fingerprint: 0x78 0xDA | single IDAT | 94.7% Average filter
- Matches PIL/Pillow with optimize=True
Steganographic watermark:
- LSB entropy: 3.0/3.0 (maximum)
- Bits 0-3 of RGB channels filled with encrypted payload
- ~1.77 MB of pseudorandom data embedded
Lots of people here snarking would understand if they 'felt' the latency and additional overhead. Not enough thought has been put in carfully slicing the data for individual use cases.
I'd go even further, in his demo of 'generating load by moving the config often' -- I think that 'top' app should have 'jit-ed' more of the rendering on the client such that the only information traversing pi<>client is compresed delta's of the ps hose.