RandomBK

Give the player a set of AI-powered department heads that manage the day-to-day decisions. The player only gets to write "policy documents" that steer those department, who implement the policies (or not!).

It's worth noting that you need both uniqueness and some form of stability. If you consistently show up as a diffent fingerprint every time you visit or for every different site, then that is a form of privacy as well.

Laziness and weak connections makes sense as differentiators.

However I'm not sure Excel is such a great illustration in that case, as it's neither lazy nor weakly connected; at least at the surface.

One thing I've never fully grokked is how this differs from an observable pattern where one can publish new values to inputs, propagate that through the computation, and push newly computed values to listeners.

I guess there's probably optimizations around change detection and stopping the propagation if there's no change (though observables can do that as well). The stabilize command also makes things interesting as a way to batch changes together before recomputing (but again, doable with observables too).

Is the delta primarily coming from introspection and automatically building the compute graph? Or is there something more fundamental that I'm missing?

I'm curious to hear what bottlenecks you encountered in the traditional path. Of all the compute and data shuffling involved in LLM inference, I would have thought shuffling the raw input/output around would have been a trivial part of the overall cost, and thus not a big optimization target?

As someone who uses gradle largely out of inertia, I'm curious what you would pick as a better alternative.

I've found swearing at a model to be quite effective in getting it to rethink and correct its mistakes. This seems to apply across Codex, Claude, Qwen, and Gemma/Gemini.

I don't know if the model is picking up on a "need to lock in and be more rigorous" signal, or if the model providers are routing to smarter models if they detect a frustrated user. But if a model keeps making the same mistakes, swearing at it often helped kick it out of a glut and onto the right track.

Or it could just be catharsis.

> context with 2.1 bits of entropy per token

Can you elaborate on this? I'm seen estimates of ~1.5bit per English letter, and tokens encode a lot more than that - sometimes full words, with multimodal even more. If KV cache embedding are storing more than just simple tokens but entire concepts with context and nuance, that'll bump the entropy up quite quickly.

VPS comes at the cost of potential for oversubscription - even from more reputable vendors. You never really know if you're actually getting what you're paying for.

One annoyance (I don't know if they've since fixed it) was that Docker Hub would count pulls that don't contain an update towards the rate limit. That ultimately prompted me to switch to alternate repositories.

How well do we understand the tokenization for Claude? I'd posit that the exact human-representation of this markup is likely irrelevant if it's all being converted into a single token.

Code length will itself become a problem. The instruction cache is limited in size and often quite small. Bloating instruction counts with lots of duplicated code will eventually have a negative effect on performance.

Ultimately, there's too many factors to predetermine which approach is faster. Write clean code, and let a profiler guide optimizations when needed.

Additionally, so long as we can be sure the human's output is not actively adversarial, we can xor it into the entropy pool. Entropy can only increase this way.

Good catch; it was somewhat ambiguous in the report.