Auftragstaktik describes a clear purpose / intent. Like: capture the bridge (but: we don’t care how you do it since we can’t foresee specific circumstances)

OODA describes a process of decision making.

So, Auftragstaktik answers who decides what and why.

OODA answers how decisions are made and updated over time.

They’re complementary but different.

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I'm a data scientist with 20+ years experience who enjoys gnarly, avant-garde problems. I saved a German automaker from lemon law recalls. I've worked with a major cloud vendor to predict when servers would fail, allowing them to load shed in time.

Some of the things I've done:

    - Oil reservoir & well engineering forecasting production.
    - Automotive part failure prediction (Lemon law recalls)
    - Server fleet failure prediction allowing load shedding.
    - Shipping piracy risk prediction - routing ships away from danger.
    - Realtime routing (CVRP-PD-TW, shifts) for on demand delivery.
    - Legal entity and contract term extraction from documents.
    - Wound identification & tissue classification.
    - The usual LLM and agent stuff. (I'd love to work on effective executive functioning)
    - Your nasty problem here.

I have years of experience helping companies plan, prototype, and productionize sane data science solutions. Get in touch if you have a problem, my email is in my profile.

NB: Don't contact me if you're doing ads, gambling, or Enshittification.. I'd like to sleep at night.

But you're right OS resource limitations (file handles, PIDs, etc) would be the real pain for you. One problem after another.

Now, the real question is do you want to spend your engineering time on that? A small cluster running erlang is probably better than a tiny number of finely tuned race-car boxen.

Totally agree on many smaller boxes vs bigger box especially for proxying usecase.

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May your glühwein be hot and your cats purring :)

https://kwc.im/wp-content/uploads/2025/12/GKP_2025_26.pdf

>The paper carries a telling Latin motto: "Scire ubi aliquid invenire possis ea demum maxima pars eruditionis est" – "To know where you can find something is, after all, the greatest part of learning."

In the modern era it is acknowledged that people will use online search engines to find the answers but hopefully we all learn something on the way

https://oldcompcz.github.io/jgs/joan_stark/xmas.html#treewit...

And of course, the BBS Documentary: Artscene episode:

https://www.youtube.com/watch?v=oQrBbm5ZMlo&list=PL7nj3G6Jpv...

Merry Christmas and happy new years everyone!

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https://news.ycombinator.com/formatdoc says:

> Text after a blank line that is indented by two or more spaces is reproduced verbatim. (This is intended for code.)

I assume that's how the top level comment posted the tree which is in a <pre><code> structure.

I kinda do want to go back to oldschool bbses, with cool text-mode lightbar interfaces.

https://en.wikipedia.org/wiki/Joan_Stark

I thought it was just a straggler.

I wouldn't be surprised if someone comes up with obfuscated C code that looks like a christmas tree and prints out wishes by the end of the day/season.

Merry Christmas!

[0]: https://www.ioccc.org/

    Test, is this monospaced?
    012345678901234567890123456789

In the 2000s, no one was doing what they were doing.

Obviously he has better food taste than I do, so those too. I will shit like a mink and love it.

This existed in Lisp and Smalltalk systems. Since there's no disk/running program split you don't have to serialize your data. You just pass around Lisp sexprs or Smalltalk code/ASTs. No more sucking your data from Postgres over a straw, or between microservices, or ...

These systems are magnitudes smaller and simpler than what we've built today. I'd love to see them exist again.

This was used to build the first modern language translation systems, testing them going from English->french->english. And in reverse.

You could do similar here , understanding that your language is quite stilted legalese.

Edit: there might be other countries with similar rules in place that you could source test data from as well.

Now I will. Thanks.

But no English so you might not be interested.

What I can think of is that there may be a push toward training for exclusively search-based rewards so that the model isn't required to compress a large proportion of the internet into their weights. But this is likely to be much slower and come with initial performance costs that frontier model developers will not want to incur.

That just gave me an idea! I wonder how useful (and for what) a model would be if it was trained using a two-phase approach:

1) Put the training data through an embedding model to create a giant vector index of the entire Internet.

2) Train a transformer LLM but instead only utilising its weights, it can also do lookups against the index.

Its like a MoE where one (or more) of the experts is a fuzzy google search.

The best thing is that adding up-to-date knowledge won’t require retraining the entire model!

If openAI is spending $500B then someone can get ahead by spending $1B which improves the model by >0.2%

I bet there's a group or three that could improve results a lot more than 0.2% with $1B.

The knowledge compressed into an LLM is a byproduct of training, not a goal. Training on internet data teaches the model to talk at all. The knowledge and ability to speak are intertwined.

https://en.wikipedia.org/wiki/Jaguar_XK_engine

I assume the American s will be by with a pushrod v8 soon.

https://en.wikipedia.org/wiki/Buick_V6_engine

1961-2008.