Ciro Santilli @cirosantilli  40

Circa 2023, the feed is an unbearable list of stupid suggestions, never-ending idiotic memes, and you just end up missing posts you actually care about from people you actually follow.

www.komando.com/social-media/facebook-customized-feeds/847500/
www.quora.com/How-do-I-limit-my-news-feed-to-friends-only-on-Facebook
www.youtube.com/watch?v=SIA8VydqiNQ OK they split their feed into multiple feeds. However on page follows www.facebook.com/?filter=pages&sk=h_chr you very quickly reach:
You're all caught up on Most Recent posts Check back later for more updates
the history doesn't go back even a few days as of November 2023! And the favorites feed www.facebook.com/?filter=favorites&sk=h_chr is more explicit on their ridiculous timing:
You're up to date on posts from the last 3 days
OMG!

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Gaussian function Updated 2025-07-16

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Elon Musk's family Updated 2025-08-08

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Delft University of Technology Updated 2025-07-16

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Whole-genome sequencing Updated 2025-07-16

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DNA sequencing milestone Updated 2025-07-16

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Most of these are going to be Whole-genome sequencing of some model organism:

1975 by Sanger et al.: 5 kbp of the single-stranded bacteriophage ΦX174 using Sanger's radiolabelling method
1981 by Sanger et al.: 17 kbp of human mitochondrial DNA via Sanger method, known as the Cambridge Reference Sequence
2003: Human Genome Project (3 Gbp)

en.wikipedia.org/wiki/Whole_genome_sequencing#History lists them all. Basically th big "firsts" all happened in the 1990s and early 2000s.

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Ciro's call hierarchy notation Updated 2025-07-16

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This is a simple hierarchical plaintext notation Ciro Santilli created to explain programs to himself.

It is usuall created by doing searches in an IDE, and then manually selecting the information of interest.

It attempts to capture intuitive information not only of the call graph itself, including callbacks, but of when things get called or not, by the addition of some context code.

For example, consider the following pseudocode:

f1() {
}

f2(i) {
  if (i > 5) {
    f1()
  }
}

f3() {
  f1()
  f2_2()
}

f2_2() {
  for (i = 0; i < 10; i++) {

    f2(i)
  }
}

main() {
  f2_2()
  f3()
}

Supose that we are interested in determining what calls f1.

Then a reasonable call hierarchy for f1 would be:

f2(i)
  if (i > 5) {
    f1()

  f2_2()
    for (i = 0; i < 10; i++) {
      f2(i)

    main
    f3
f3()
  main()

Some general principles:

start with a regular call tree
to include context:
- remove any blank lines from the snippet of interest
- add it indented below the function
- and then follow it up with a blank line
- and then finally add any callers at the same indentation level

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Stored procedure Updated 2025-07-16

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Database trigger Updated 2025-07-16

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Cultured meat company Updated 2025-07-16

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UK
- Higher Steaks then renamed to the boring "Uncommon": uncommonbio.co/
  - 2023 $30m: www.uktech.news/foodtech/uncommon-series-a-20230609

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Microscopy connectome extraction Updated 2025-07-16

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This is the most plausible way of obtaining a full connectome looking from 2020 forward. Then you'd observe the slices with an electron microscope + appropriate Staining. Superintelligence by Nick Bostrom (2014) really opened Ciro Santilli's eyes to this possibility.

Once this is done for a human, it will be one of the greatest milestone of humanities, coparable perhaps to the Human Genome Project. BUt of course, privacy issues are incrediby pressing in this case, even more than in the human genome project, as we would essentially be able to read the brain of the person after their death.

As of 2022, the Drosophila connectome had been almost fully extracted.

This is also a possible path towards post-mortem brain reading.

Figure 1. Source. Unconfirmed, but looks like the type of frozen brain where a Microtome would be used.

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Connectome scale Updated 2025-07-16

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A Drosophila melanogaster has about 135k neurons, and we only managed to reconstruct its connectome in 2023.

The human brain has 86 billion neurons, about 1 million times more. Therefore, it is obvious that we are very very far away from a full connectome.

Instead however, we could look at larger scales of connectome, and then try from that to extract modules, and then reverse engineer things module by module.

This is likely how we are going to "understand how the human brain works".

Some notable connectomes: