thesz

Such state monad with typed environment was possible in Haskell as early as 2009: https://blog.sigfpe.com/2009/02/beyond-monads.html

Sets of types (generalized records) were available in Haskell from 2004: https://okmij.org/ftp/Haskell/dependent-types.html#HList

Basically, it was possible to embed Rust-like language into Haskell from 2009 on, the ideas were all there.

The thesis that I continue to read is about dependencies. These dependencies control effects of the different parts of the program and one of the first more or less successful and practical way to control effects was monads in Haskell and, after that, monad transformers.

From my experience with Haskell, I more than love Software Transactional Memory, implemented as a monad without access to general I/O. You just cannot delete file before reaching consensus between different parts of program on which file to delete. Or, you can match on the state of different channels, where matching requirements may include the presence and/or absence of specific kinds of data, not only in the heads, but also in the channel as a whole. When I read about Go channels and their matching semantics I was greatly disappointed with them.

[1] https://github.com/iczelia/bzip3

Did this Claude-written free compressor take anything from bzip3?

[1] https://jpaykin.github.io/papers/pz_linearity_monad_2017.pdf

Most of dependence tracking can be done with Haskell's type system, as a library.

[1] https://stackoverflow.com/questions/75452073/understanding-e...

Builds of some of projects I've been working on required 20Gbytes and more of disk space.

  > Model SOTA moves faster than chips can be designed or produced.

Here's my analysis of how to etch relatively big LM into silicon: https://news.ycombinator.com/item?id=47109252

Given some amount of work with the fab before main pipeline set (I think a year long process), one can then spew LM-on-a-chip in six months or less and much more than 2 per year, because there can be several LMs in pipeline.

  > Matrix multiplication is after all nothing but an aggregate sum over a cross join.

They converted linear algebra expressions into relational algebra expressions, optimized them using equality saturation and converted back into linear algebra. With some noticeable speedups.

They used relational algebra for optimization because relational algebra has canonical form - there is only one most optimal expression.

And, equality saturation is most efficiently implemented using generic join algorithm.

(the link is a translation to Russian, I was unable to find the original text in English)

Also consider that toxoplasma changes rat's dopamine serum level to such extent that they stop fearing cats. The same happens with humans, changes in dopamine serum level are so profound that toxoplasma-infected people become more appealing than regular people.

[1] https://en.wikipedia.org/wiki/Dopamine_dysregulation_syndrom...

This dopamine dysregulation occurs naturally in people with toxoplasmosis.

This kind of mistake - mistaking dopamine and endorphine, - strongly suggests a fake. I know of one such fake from the former employee of my friend, you can read his account here [2]. My friend was a lead of the "secret" laboratory (next to Kitay Gorod metro station in the center of Moscow, in an institute that needed no documents to enter) that researched how "psychics" and "healers" achieve their results.

[2] https://lit.lib.ru/t/torin_a/text_0100.shtml (it is in Russian, but language is not a barrier at all nowadays)

The laboratory researched what these "extrasensing people" do and found nothing - given very little training an ordinary men can do the same and even better. At the end of USSR, there was a string of visitors from USA there in the lab, and one of those visitors successfully persuaded US government to open similar research program, according to [2].

The fearmongering of "endorphin-producing bugs that make one socialize and spread" looks like, well, fearmongering to obtain funds for Big Pharma's government contracts, etc. It messes up two very different neurotransmitters, a mistake that real scientist would not make.