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.

Using something like mosh would mitigate it, but there was no UDP tunnel available through beyoundcorp.

Cider and MDProxy were running rendering locally so the keystrokes were nearly instant.

I developed a fork of the IntelliJ IDE on my second week at google out of raw frustration over latency. At the time I was commuting 2-3hrs/day SF<>MTV on the gBus.

Connectivity on the bus wasn't optimal, and there was high latency. Cider didn't have deep integration, and wasn't able to let me explore and understand the internal APIs effectively. I found it easier to enter a debug session within Intellij then 'vibe' and explore the internal apis via superComplicatedObject.ini.

Faced with an alien architecture + ADHD-unfriendly flow-crushing remote desktop latency -- and the lack of discoverability, I started hacking at it and without any knowledge of the system and architecture. Just tracing Intellij execution, subprocesses and network calls.

I was able to hack together a prototype in a few days that allowed me to run IntelliJ on my Mac, while the heavy bits ran on my corp desktop. The system would mount the remote filesystem over sshfs, would monitor and patch network connections and setup transparent shim binaries. Half of Intellij was running on the Mac (the front end) and the other half ran on Linux. Intellij didn't "know" that that it was running on a mac. This was initially implemented in a ~250 line shell script that patched everything.

It was called MDProxy[1] and ended getting adopted and supported during COVID as more development went remote. This became a source of many peer bonuses and spot bonuses. circa 2017* remote coding options at the time:

         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

all cloud providers have middle east presence

refineries generate terabytes of sensor data per hour

the population and people there produce and consume a lot of data

https://claude.ai/share/4262fb6b-3ca1-407f-af0d-4d014686e65d

see:

- https://github.com/QuesmaOrg/BinaryAudit/blob/main/tasks/dns...

- https://github.com/QuesmaOrg/BinaryAudit/blob/main/tasks/dro...

What's the point of building skills like this?

                 yours  yours+opt  claude
  ---------------------------------------
  Time (ns)        40.9      36.4    38.7
  sqrt/iter           3         2       1
  Instructions      207       187     241

https://gist.github.com/achille/23680e9100db87565a8e67038797...

this sent me down a rabbit hole -- I asked a few models to solve that same problem, then followed up with a request to optimize it so it runs more efficiently.

chatgpt & gemini's solutions were buggy, but claude solved it, and actually found a solution that is even more efficient. It only needs to compute sqrt once per iteration. It's more complex however.

                   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;

p.s: it seems like Gemini has disabled the ability to share chats can anyone else confirm this?

  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