Ciro Santilli @cirosantilli 40

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Monero Updated 2025-07-16

Cryptocurrency with focus on anonymity. Was almost certainly the leading privacy coin since its inception until as of writing in the 2020s.

Ciro Santilli has received and held considerable quantities of Monero, notably 1000 Monero donation. so bias alert.

As mentioned at Section "Are cryptocurrencies useful?", Ciro Santilli believes that anonymity is the most valuable feature that really matters on crypto coins, and therefore if he were to invest in crypto, he would invest in Monero or some other privacy coin.

localmonero.co/knowledge/monero-stealth-addresses?language=en gives an overview of the privacy mechanisms:

ring signatures, which hide the true output (sender)
localmonero.co/knowledge/ring-signatures Gives an overview. Mentions that it is prone to heuristic attacks.
Uses a system of decoys, that adds 10 fake possible previous outputs as inputs, in addition to the actual input.
So the network only knows/verifies that one of those 11 previous outputs was used, but it does not know which one.
It's a bit like having a built-in cryptocurrency tumbler in every transaction.
TODO so how do you know which previous outputs were spent or not?
RingCT which hides the amounts.
stealth addresses, which hides who you send to
This forces receivers to scan try and unlock every single transaction in the chain to see if it is theirs or not.
The sender therefore can know when the money is spent, but once again, not to whom it is being sent.

Based on en.wikipedia.org/wiki/CryptoNote and like Satoshi Nakamoto created by under the pseudonym "Nicolas van Saberhagen" www.reddit.com/r/Monero/comments/7v2obe/offering_a_bounty_for_a_video_of_the_speech_by/

Coinbase has actually stayed away from trading it even as of 2019 when Monero was the third largest market capitalization crypto because of fear of regulatory slashback: decrypt.co/36731/heres-why-coinbase-still-hasnt-listed-monero. Although it must be said, the value of privacy crypto is greatly reduced when everyone is trading it on exchanges, which require a passport upload to work.

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Most important superconductor material Updated 2025-07-16

As of 2023 the most important ones economicaly were:

Nb-Ti: the most widely used one. Used e.g. to create the magnetic fields of the Large Hadron Collider Up to 15 T.
Nb-Sn: more expensive than Nb-Ti, but can reach up to 30 T.

The main application is magnetic resonance imaging. Both of these are have to be Liquid helium, i.e. they are not "high-temperature superconductor" which is a pain. One big strength they have is that they are metallic, and therefore can made into wires, which is crucial to be able to make electromagnetic coils out of them.

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Mountain used for cosmic ray experiments Updated 2025-07-16

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Moving magnet and conductor problem Updated 2025-07-16

This is a well known though experiment, which Richard Feynman used to emphasize

infinite wire with balanced positive and negative charges, so no net charge, but a net magnetic field
a single charge moves parallel to wire at the same speed as the electrons

In the above experiment:

from the wire frame, the charge feels electromagnetic force, because it is moving and there is a magnetic field
from the single charge frame, there is still magnetic field (positive charges are moving), but the body itself is not moving, so there is no force!

The solution to this problem is length contraction: the positive charges are length contracted and the moving electrons aren't, and therefore they are denser and therefore there is an effective charge from that frame.

This is also mentioned at David Tong www.damtp.cam.ac.uk/user/tong/em/el4.pdf (archive) "David Tong: Lectures on Electromagnetism - 5. Electromagnetism and Relativity" "5.2.1 Magnetism and Relativity".

How Special Relativity Fixed Electromagnetism by The Science Asylum (2019)

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E. Coli K-12 MG1655 gene thrL Updated 2025-07-16

UniProt entry: www.uniprot.org/uniprot/P0AD86.

NCBI gene entry: www.ncbi.nlm.nih.gov/gene/944742.

The first gene in the E. Coli K-12 MG1655 genome. Remember however that bacterial chromosome is circular, so being the first doesn't mean much, how the choice was made: Section "E. Coli genome starting point".

Part of E. Coli K-12 MG1655 operon thrLABC.

At only 65 bp, this gene is quite small and boring. For a more interesting gene, have a look at the next gene, e. Coli K-12 MG1655 gene thrA.

Does something to do with threonine.

This is the first in the sequence thrL, thrA, thrB, thrC. This type of naming convention is quite common on related adjacent proteins, all of which must be getting transcribed into a single RNA by the same promoter. As mentioned in the analysis of the KEGG entry for e. Coli K-12 MG1655 gene thrA, those A, B and C are actually directly functionally linked in a direct metabolic pathway.

We can see that thrL, A, B, and C are in the same transcription unit by browsing the list of promoter at: biocyc.org/group?id=:ALL-PROMOTERS&orgid=ECOLI. By finding the first one by position we reach; biocyc.org/ECOLI/NEW-IMAGE?object=TU0-42486.

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Mr. SQUID Updated 2025-07-16

starcryo.com/mr-squid/

This is the cutest product name ever.

Since 1992, Mr. SQUID has been the standard educational demonstration system for undergraduate physics lab courses.

Used e.g. at Video "Superconducting Quantum Interference Devices by UNSW Physics (2020)"

Their manual: www.phys.ksu.edu/personal/cocke/classes/phys506/squidman.pdf

YBCO device, runs on liquid nitrogen.

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E. Coli K-12 MG1655 gene yaaX Updated 2025-07-16

The fifth gene, and the first E. Coli K-12 MG1655 gene of unknown function as of 2021.

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E. Coli K-12 MG1655 promoter Updated 2025-07-16

biocyc.org/group?id=:ALL-PROMOTERS&orgid=ECOLI

From this we see that there is a convention of naming promoters as protein name + p, e.g. the first gene in E. Coli K-12 MG1655 promoter thrLp encodes protein thrL.

It is also possible to add numbers after the p, e.g. at biocyc.org/ECOLI/NEW-IMAGE?type=OPERON&object=PM0-45989 we see that the protein zur has two promoters:

zurp6
zurp7

TODO why 6 and 7? There don't appear to be 1, 2, etc.

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E. Coli replication time Updated 2025-07-16

20 minutes in optimal conditions, with a crazy multiple start sites mechanism: E. Coli starts DNA replication before the previous one finished.

Otherwise, naively, would take 60-90 minutes just to replicate and segregate the full DNA otherwise. So it starts copying multiple times.

biology.stackexchange.com/questions/30080/how-can-e-coli-proliferate-so-rapidly
stochasticscientist.blogspot.co.uk/2012/02/how-e-coli-grows-so-fast.html
www.ncbi.nlm.nih.gov/pmc/articles/PMC2063475/ Organization of sister origins and replisomes during multifork DNA replication in Escherichia coli by Fossum et al (2007)

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E. Coli Whole Cell Model by Covert Lab Updated 2025-07-16

github.com/CovertLab/WholeCellEcoliRelease is a whole cell simulation model created by Covert Lab and other collaborators.

The project is written in Python, hurray!

But according to te README, it seems to be the use a code drop model with on-request access to master. Ciro Santilli asked at rationale on GitHub discussion, and they confirmed as expected that it is to:

to prevent their publication ideas from being stolen. Who would steal publication ideas with public proof in an issue tracker without crediting original authors? Academia is broken. Academia should be the most open form of knowledge sharing. But instead we get this silly competition for publication points.
to prevent noise from non-collaborators. But they only get like 2 issues as year on such a meganiche subject... Did you know that you can ignore people, and even block them if they are particularly annoying? Much more likely is that no one will every hear about your project and that it will die with its last graduate student slave.

The project is a followup to the earlier M. genitalium whole cell model by Covert lab which modelled Mycoplasma genitalium. E. Coli has 8x more genes (500 vs 4k), but it the undisputed bacterial model organism and as such has been studied much more thoroughly. It also reproduces faster than Mycoplasma (20 minutes vs a few hours), which is a huge advantages for validation/exploratory experiments.

The project has a partial dependency on the proprietary optimization software CPLEX which is freeware, for students, not sure what it is used for exactly, from the comment in the requirements.txt the dependency is only partial.

This project makes Ciro Santilli think of the E. Coli as an optimization problem. Given such external nutrient/temperature condition, which DNA sequence makes the cell grow the fastest? Balancing metabolites feels like designing a Factorio speedrun.

There is one major thing missing thing in the current model: promoters/transcription factor interactions are not modelled due to lack/low quality of experimental data: github.com/CovertLab/WholeCellEcoliRelease/issues/21. They just have a magic direct "transcription factor to gene" relationship, encoded at reconstruction/ecoli/flat/foldChanges.tsv in terms of type "if this is present, such protein is expressed 10x more". Transcription units are not implemented at all it appears.

Everything in this section refers to version 7e4cc9e57de76752df0f4e32eca95fb653ea64e4, the code drop from November 2020, and was tested on Ubuntu 21.04 with a docker install of docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full with image id 502c3e604265, unless otherwise noted.

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E. Coli Whole Cell Model by Covert Lab / Other run variants Updated 2025-07-16

Besides time series run variants, conditions can also be selected directly without a time series as in:

python runscripts/manual/runSim.py --variant condition 1 1

which select row indices from reconstruction/ecoli/flat/condition/condition_defs.tsv. The above 1 1 would mean the second line of that file which starts with:

"condition" "nutrients" "genotype perturbations" "doubling time (units.min)" "active TFs"
"basal" "minimal" {} 44.0 []
"no_oxygen" "minimal_minus_oxygen" {} 100.0 []
"with_aa" "minimal_plus_amino_acids" {} 25.0 ["CPLX-125", "MONOMER0-162", "CPLX0-7671", "CPLX0-228", "MONOMER0-155"]

so 1 means no_oxygen.

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E. Coli Whole Cell Model by Covert Lab / Output overview Updated 2025-07-16

Run output is placed under out/:

Some of the output data is stored as .cpickle files. To observe those files, you need the original Python classes, and therefore you have to be inside Docker, from the host it won't work.

We can list all the plots that have been produced under out/ with

find -name '*.png'

Plots are also available in SVG and PDF formats, e.g.:

PNG: ./out/manual/plotOut/low_res_plots/massFractionSummary.png
SVG: ./out/manual/plotOut/svg_plots/massFractionSummary.svg The SVGs write text as polygons, see also: SVG fonts.
PDF: ./out/manual/plotOut/massFractionSummary.pdf

The output directory has a hierarchical structure of type:

./out/manual/wildtype_000000/000000/generation_000000/000000/

where:

wildtype_000000: variant conditions. wildtype is a human readable label, and 000000 is an index amongst the possible wildtype conditions. For example, we can have different simulations with different nutrients, or different DNA sequences. An example of this is shown at run variants.
000000: initial random seed for the initial cell, likely fed to NumPy's np.random.seed
genereation_000000: this will increase with generations if we simulate multiple cells, which is supported by the model
000000: this will presumably contain the cell index within a generation

We also understand that some of the top level directories contain summaries over all cells, e.g. the massFractionSummary.pdf plot exists at several levels of the hierarchy:

./out/manual/plotOut/massFractionSummary.pdf
./out/manual/wildtype_000000/plotOut/massFractionSummary.pdf
./out/manual/wildtype_000000/000000/plotOut/massFractionSummary.pdf
./out/manual/wildtype_000000/000000/generation_000000/000000/plotOut/massFractionSummary.pdf

Each of thoes four levels of plotOut is generated by a different one of the analysis scripts:

./out/manual/plotOut: generated by python runscripts/manual/analysisVariant.py. Contains comparisons of different variant conditions. We confirm this by looking at the results of run variants.
./out/manual/wildtype_000000/plotOut: generated by python runscripts/manual/analysisCohort.py --variant_index 0. TODO not sure how to differentiate between two different labels e.g. wildtype_000000 and somethingElse_000000. If -v is not given, a it just picks the first one alphabetically. TODO not sure how to automatically generate all of those plots without inspecting the directories.
./out/manual/wildtype_000000/000000/plotOut: generated by python runscripts/manual/analysisMultigen.py --variant_index 0 --seed 0
./out/manual/wildtype_000000/000000/generation_000000/000000/plotOut: generated by python runscripts/manual/analysisSingle.py --variant_index 0 --seed 0 --generation 0 --daughter 0. Contains information about a single specific cell.

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E. Coli Whole Cell Model by Covert Lab / Publications Updated 2025-07-16

Unfortunately, due to lack of one page to rule them all, the on-Git tree publication list is meager, some of the most relevant ones seems to be:

2021 open access review paper: journals.asm.org/doi/full/10.1128/ecosalplus.ESP-0001-2020 "The E. coli Whole-Cell Modeling Project". They should just past that stuff in a README :-) The article mentions that it is a follow up to the previous M. genitalium whole cell model by Covert lab. Only 43% of known genes modelled at this point however, a shame.
2020 under Science paywall: www.science.org/doi/10.1126/science.aav3751 "Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation"

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E. Coli Whole Cell Model by Covert Lab / Source code overview Updated 2025-07-16

The key model database is located in the source code at reconstruction/ecoli/flat.

Let's try to understand some interesting looking, with a special focus on our understanding of the tiny E. Coli K-12 MG1655 operon thrLABC part of the metabolism, which we have well understood at Section "E. Coli K-12 MG1655 operon thrLABC".

We'll realize that a lot of data and IDs come from/match BioCyc quite closely.

reconstruction/ecoli/flat/compartments.tsv contains cellular compartment information:
```
"abbrev" "id"
"n" "CCO-BAC-NUCLEOID"
"j" "CCO-CELL-PROJECTION"
"w" "CCO-CW-BAC-NEG"
"c" "CCO-CYTOSOL"
"e" "CCO-EXTRACELLULAR"
"m" "CCO-MEMBRANE"
"o" "CCO-OUTER-MEM"
"p" "CCO-PERI-BAC"
"l" "CCO-PILUS"
"i" "CCO-PM-BAC-NEG"
```
- CCO: "Celular COmpartment"
- BAC-NUCLEOID: nucleoid
- CELL-PROJECTION: cell projection
- CW-BAC-NEG: TODO confirm: cell wall (of a Gram-negative bacteria)
- CYTOSOL: cytosol
- EXTRACELLULAR: outside the cell
- MEMBRANE: cell membrane
- OUTER-MEM: bacterial outer membrane
- PERI-BAC: periplasm
- PILUS: pilus
- PM-BAC-NEG: TODO: plasma membrane, but that is the same as cell membrane no?
reconstruction/ecoli/flat/promoters.tsv contains promoter information. Simple file, sample lines:
```
"position" "direction" "id" "name"
148 "+" "PM00249" "thrLp"
```
corresponds to E. Coli K-12 MG1655 promoter thrLp, which starts as position 148.
reconstruction/ecoli/flat/proteins.tsv contains protein information. Sample line corresponding to e. Coli K-12 MG1655 gene thrA:
```
"aaCount" "name" "seq" "comments" "codingRnaSeq" "mw" "location" "rnaId" "id" "geneId"
[91, 46, 38, 44, 12, 53, 30, 63, 14, 46, 89, 34, 23, 30, 29, 51, 34, 4, 20, 0, 69] "ThrA" "MRVL..." "Location information from Ecocyc dump." "AUGCGAGUGUUG..." [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 89103.51099999998, 0.0, 0.0, 0.0, 0.0] ["c"] "EG10998_RNA" "ASPKINIHOMOSERDEHYDROGI-MONOMER" "EG10998"
```
so we understand that:
- aaCount: amino acid count, how many of each of the 20 proteinogenic amino acid are there
- seq: full sequence, using the single letter abbreviation of the proteinogenic amino acids
- mw; molecular weight? The 11 components appear to be given at reconstruction/ecoli/flat/scripts/unifyBulkFiles.py:
  molecular_weight_keys = [ '23srRNA', '16srRNA', '5srRNA', 'tRNA', 'mRNA', 'miscRNA', 'protein', 'metabolite', 'water', 'DNA', 'RNA' # nonspecific RNA ]
  so they simply classify the weight? Presumably this exists for complexes that have multiple classes?
  - 23srRNA, 16srRNA, 5srRNA are the three structural RNAs present in the ribosome: 23S ribosomal RNA, 16S ribosomal RNA, 5S ribosomal RNA, all others are obvious:
  - tRNA
  - mRNA
  - protein. This is the seventh class, and this enzyme only contains mass in this class as expected.
  - metabolite
  - water
  - DNA
  - RNA: TODO rna vs miscRNA
- location: cell compartment where the protein is present, c defined at reconstruction/ecoli/flat/compartments.tsv as cytoplasm, as expected for something that will make an amino acid
reconstruction/ecoli/flat/rnas.tsv: TODO vs transcriptionUnits.tsv. Sample lines:
```
"halfLife" "name" "seq" "type" "modifiedForms" "monomerId" "comments" "mw" "location" "ntCount" "id" "geneId" "microarray expression"
174.0 "ThrA [RNA]" "AUGCGAGUGUUG..." "mRNA" [] "ASPKINIHOMOSERDEHYDROGI-MONOMER" "" [0.0, 0.0, 0.0, 0.0, 790935.00399999996, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] ["c"] [553, 615, 692, 603] "EG10998_RNA" "EG10998" 0.0005264904
```
- halfLife: half-life
- mw: molecular weight, same as in reconstruction/ecoli/flat/proteins.tsv. This molecule only have weight in the mRNA class, as expected, as it just codes for a protein
- location: same as in reconstruction/ecoli/flat/proteins.tsv
- ntCount: nucleotide count for each of the ATGC
- microarray expression: presumably refers to DNA microarray for gene expression profiling, but what measure exactly?

reconstruction/ecoli/flat/sequence.fasta: FASTA DNA sequence, first two lines:

>E. coli K-12 MG1655 U00096.2 (1 to 4639675 = 4639675 bp)
AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGTGTGGATTAAAAAAAGAGTGTCTGATAGCAGCTTCTG

reconstruction/ecoli/flat/transcriptionUnits.tsv: transcription units. We can observe for example the two different transcription units of the E. Coli K-12 MG1655 operon thrLABC in the lines:
```
"expression_rate" "direction" "right" "terminator_id"  "name"    "promoter_id" "degradation_rate" "id"       "gene_id"                                   "left"
0.0               "f"         310     ["TERM0-1059"]   "thrL"    "PM00249"     0.198905992329492 "TU0-42486" ["EG11277"]                                  148
657.057317358791  "f"         5022    ["TERM_WC-2174"] "thrLABC" "PM00249"     0.231049060186648 "TU00178"   ["EG10998", "EG10999", "EG11000", "EG11277"] 148
```
- promoter_id: matches promoter id in reconstruction/ecoli/flat/promoters.tsv
- gene_id: matches id in reconstruction/ecoli/flat/genes.tsv
- id: matches exactly those used in BioCyc, which is quite nice, might be more or less standardized:
  - biocyc.org/ECOLI/NEW-IMAGE?object=TU0-42486
  - biocyc.org/ECOLI/NEW-IMAGE?type=OPERON&object=TU00178

reconstruction/ecoli/flat/genes.tsv

"length" "name"                      "seq"             "rnaId"      "coordinate" "direction" "symbol" "type" "id"      "monomerId"
66       "thr operon leader peptide" "ATGAAACGCATT..." "EG11277_RNA" 189         "+"         "thrL"   "mRNA" "EG11277" "EG11277-MONOMER"
2463     "ThrA"                      "ATGCGAGTGTTG"    "EG10998_RNA" 336         "+"         "thrA"   "mRNA" "EG10998" "ASPKINIHOMOSERDEHYDROGI-MONOMER"

reconstruction/ecoli/flat/metabolites.tsv contains metabolite information. Sample lines:
```
"id"                       "mw7.2" "location"
"HOMO-SER"                 119.12  ["n", "j", "w", "c", "e", "m", "o", "p", "l", "i"]
"L-ASPARTATE-SEMIALDEHYDE" 117.104 ["n", "j", "w", "c", "e", "m", "o", "p", "l", "i"]
```
In the case of the enzyme thrA, one of the two reactions it catalyzes is "L-aspartate 4-semialdehyde" into "Homoserine".
Starting from the enzyme page: biocyc.org/gene?orgid=ECOLI&id=EG10998 we reach the reaction page: biocyc.org/ECOLI/NEW-IMAGE?type=REACTION&object=HOMOSERDEHYDROG-RXN which has reaction ID HOMOSERDEHYDROG-RXN, and that page which clarifies the IDs:
- biocyc.org/compound?orgid=ECOLI&id=L-ASPARTATE-SEMIALDEHYDE: "L-aspartate 4-semialdehyde" has ID L-ASPARTATE-SEMIALDEHYDE
- biocyc.org/compound?orgid=ECOLI&id=HOMO-SER: "Homoserine" has ID HOMO-SER
so these are the compounds that we care about.

reconstruction/ecoli/flat/reactions.tsv contains chemical reaction information. Sample lines:

"reaction id" "stoichiometry" "is reversible" "catalyzed by"

"HOMOSERDEHYDROG-RXN-HOMO-SER/NAD//L-ASPARTATE-SEMIALDEHYDE/NADH/PROTON.51."
  {"NADH[c]": -1, "PROTON[c]": -1, "HOMO-SER[c]": 1, "L-ASPARTATE-SEMIALDEHYDE[c]": -1, "NAD[c]": 1}
  false
  ["ASPKINIIHOMOSERDEHYDROGII-CPLX", "ASPKINIHOMOSERDEHYDROGI-CPLX"]

"HOMOSERDEHYDROG-RXN-HOMO-SER/NADP//L-ASPARTATE-SEMIALDEHYDE/NADPH/PROTON.53."
  {"NADPH[c]": -1, "NADP[c]": 1, "PROTON[c]": -1, "L-ASPARTATE-SEMIALDEHYDE[c]": -1, "HOMO-SER[c]": 1
  false
  ["ASPKINIIHOMOSERDEHYDROGII-CPLX", "ASPKINIHOMOSERDEHYDROGI-CPLX"]

catalized by: here we see ASPKINIHOMOSERDEHYDROGI-CPLX, which we can guess is a protein complex made out of ASPKINIHOMOSERDEHYDROGI-MONOMER, which is the ID for the thrA we care about! This is confirmed in complexationReactions.tsv.

reconstruction/ecoli/flat/complexationReactions.tsv contains information about chemical reactions that produce protein complexes:
```
"process" "stoichiometry" "id" "dir"
"complexation"
  [
    {
      "molecule": "ASPKINIHOMOSERDEHYDROGI-CPLX",
      "coeff": 1,
      "type": "proteincomplex",
      "location": "c",
      "form": "mature"
    },
    {
      "molecule": "ASPKINIHOMOSERDEHYDROGI-MONOMER",
      "coeff": -4,
      "type": "proteinmonomer",
      "location": "c",
      "form": "mature"
    }
  ]
"ASPKINIHOMOSERDEHYDROGI-CPLX_RXN"
1
```
The coeff is how many monomers need to get together for form the final complex. This can be seen from the Summary section of ecocyc.org/gene?orgid=ECOLI&id=ASPKINIHOMOSERDEHYDROGI-MONOMER:
Aspartate kinase I / homoserine dehydrogenase I comprises a dimer of ThrA dimers. Although the dimeric form is catalytically active, the binding equilibrium dramatically favors the tetrameric form. The aspartate kinase and homoserine dehydrogenase activities of each ThrA monomer are catalyzed by independent domains connected by a linker region.
Fantastic literature summary! Can't find that in database form there however.

reconstruction/ecoli/flat/proteinComplexes.tsv contains protein complex information:

"name" "comments" "mw" "location" "reactionId" "id"
"aspartate kinase / homoserine dehydrogenase"
""
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 356414.04399999994, 0.0, 0.0, 0.0, 0.0]
["c"]
"ASPKINIHOMOSERDEHYDROGI-CPLX_RXN"
"ASPKINIHOMOSERDEHYDROGI-CPLX"

reconstruction/ecoli/flat/protein_half_lives.tsv contains the half-life of proteins. Very few proteins are listed however for some reason.

reconstruction/ecoli/flat/tfIds.csv: transcription factors information:

"TF"   "geneId"  "oneComponentId"  "twoComponentId" "nonMetaboliteBindingId" "activeId" "notes"
"arcA" "EG10061" "PHOSPHO-ARCA"    "PHOSPHO-ARCA"
"fnr"  "EG10325" "FNR-4FE-4S-CPLX" "FNR-4FE-4S-CPLX"
"dksA" "EG10230"

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Education Updated 2025-07-16

One of the causes Ciro Santilli care the most about: motivation.

Ciro Santilli's view of the ideal teaching method: how to teach.

A list of complaints against education: Section "Education is broken".

How to improve education? Simple:

tax the fuck out of the rich people and companies: wealth tax and invest it in education
invest intelligently as mentioned at what poor countries have to do to get richer:
- focus on fewer higher excellence schools that select the most promising poor students, rather than giving crappy average to everyone
- use OurBigBook.com

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Educational charitable organization Updated 2025-07-16

In this section we list charitable organizations that support education or research:

elifesciences.org/labs by eLife
www.digital-science.com/investment/catalyst-grant/ by Shuttleworth Foundation.
en.wikipedia.org/wiki/PLOS
www.chanzuckerberg.com/ Zuck has already invested in education previously:
- www.forbes.com/sites/kathleenchaykowski/2018/02/14/zuckerberg-invests-in-on-demand-education-site-varsity-tutors-in-50-million-fundraise/#1c9b6b385dab
- uk.businessinsider.com/mark-zuckerberg-schools-education-newark-mayor-ras-baraka-cory-booker-2018-5
openuk.uk/
bccampus.ca/ CC BY, lower undergrad stuff, but better than nothing.
Hmm, some/all of it is a copy of LibreTexts but with URLs shorter than a million characters, e.g.:
- opentextbc.ca/universityphysicsv3openstax/chapter/de-broglies-matter-waves/
- phys.libretexts.org/Bookshelves/University_Physics/Book%3A_University_Physics_(OpenStax)/Book%3A_University_Physics_III_-_Optics_and_Modern_Physics_(OpenStax)/06%3A_Photons_and_Matter_Waves/6.06%3A_De_Broglies_Matter_Waves
Sir Peter Lampl, Education Endowment Foundation and Sutton Trust
Jacobs Foundation, by German-Swiss coffee mogul Klaus Johann Jacobs
joint-research-centre.ec.europa.eu/what-open-education_en
www.non-trivial.org/ "Launch Your Own Impactful Project CHOOSE YOUR CAUSE. GET EXPERT GUIDANCE. FIND A SOLUTION AND MAKE IT HAPPEN."
by United States Government:
- FY 2024 Education Innovation and Research:

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ELF Hello World Tutorial / Program header table Updated 2025-07-16

Only appears in the executable.

Contains information of how the executable should be put into the process virtual memory.

The executable is generated from object files by the linker. The main jobs that the linker does are:

determine which sections of the object files will go into which segments of the executable.
In Binutils, this comes down to parsing a linker script, and dealing with a bunch of defaults.
You can get the linker script used with ld --verbose, and set a custom one with ld -T.
do relocation according to the .rela.text section. This depends on how the multiple sections are put into memory.

readelf -l hello_world.out gives:

Elf file type is EXEC (Executable file)
Entry point 0x4000b0
There are 2 program headers, starting at offset 64

Program Headers:
  Type           Offset             VirtAddr           PhysAddr
                 FileSiz            MemSiz              Flags  Align
  LOAD           0x0000000000000000 0x0000000000400000 0x0000000000400000
                 0x00000000000000d7 0x00000000000000d7  R E    200000
  LOAD           0x00000000000000d8 0x00000000006000d8 0x00000000006000d8
                 0x000000000000000d 0x000000000000000d  RW     200000

 Section to Segment mapping:
  Segment Sections...
   00     .text
   01     .data

On the ELF header, e_phoff, e_phnum and e_phentsize told us that there are 2 program headers, which start at 0x40 and are 0x38 bytes long each, so they are:

00000040  01 00 00 00 05 00 00 00  00 00 00 00 00 00 00 00  |................|
00000050  00 00 40 00 00 00 00 00  00 00 40 00 00 00 00 00  |..@.......@.....|
00000060  d7 00 00 00 00 00 00 00  d7 00 00 00 00 00 00 00  |................|
00000070  00 00 20 00 00 00 00 00                           |.. .....        |

and:

00000070                           01 00 00 00 06 00 00 00  |        ........|
00000080  d8 00 00 00 00 00 00 00  d8 00 60 00 00 00 00 00  |..........`.....|
00000090  d8 00 60 00 00 00 00 00  0d 00 00 00 00 00 00 00  |..`.............|
000000a0  0d 00 00 00 00 00 00 00  00 00 20 00 00 00 00 00  |.......... .....|

Structure represented www.sco.com/developers/gabi/2003-12-17/ch5.pheader.html:

typedef struct {
    Elf64_Word  p_type;
    Elf64_Word  p_flags;
    Elf64_Off   p_offset;
    Elf64_Addr  p_vaddr;
    Elf64_Addr  p_paddr;
    Elf64_Xword p_filesz;
    Elf64_Xword p_memsz;
    Elf64_Xword p_align;
} Elf64_Phdr;

Breakdown of the first one:

40 0: p_type = 01 00 00 00 = PT_LOAD: this is a regular segment that will get loaded in memory.
40 4: p_flags = 05 00 00 00 = execute and read permissions. No write: we cannot modify the text segment. A classic way to do this in C is with string literals: stackoverflow.com/a/30662565/895245 This allows kernels to do certain optimizations, like sharing the segment amongst processes.
40 8: p_offset = 8x 00 TODO: what is this? Standard says:
This member gives the offset from the beginning of the file at which the first byte of the segment resides.
But it looks like offsets from the beginning of segments, not file?
50 0: p_vaddr = 00 00 40 00 00 00 00 00: initial virtual memory address to load this segment to
50 8: p_paddr = 00 00 40 00 00 00 00 00: unspecified effect. Intended for systems in which physical addressing matters. TODO example?
60 0: p_filesz = d7 00 00 00 00 00 00 00: size that the segment occupies in memory. If smaller than p_memsz, the OS fills it with zeroes to fit when loading the program. This is how BSS data is implemented to save space on executable files. i368 ABI says on PT_LOAD:
The bytes from the file are mapped to the beginning of the memory segment. If the segment’s memory size (p_memsz) is larger than the file size (p_filesz), the ‘‘extra’’ bytes are defined to hold the value 0 and to follow the segment’s initialized area. The file size may not be larger than the memory size.

60 8: p_memsz = d7 00 00 00 00 00 00 00: size that the segment occupies in memory
70 0: p_align = 00 00 20 00 00 00 00 00: 0 or 1 mean no alignment required. TODO why is this required? Why not just use p_addr directly, and get that right? Docs also say:
p_vaddr should equal p_offset, modulo p_align

The second segment (.data) is analogous. TODO: why use offset 0x0000d8 and address 0x00000000006000d8? Why not just use 0 and 0x00000000006000d8?

Then the:

 Section to Segment mapping:

section of the readelf tells us that:

0 is the .text segment. Aha, so this is why it is executable, and not writable
1 is the .data segment.

TODO where does this information come from? stackoverflow.com/questions/23018496/section-to-segment-mapping-in-elf-files

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Education as a system of indoctrination Updated 2025-07-16

Whenever Ciro Santilli walks in front of a school and sees the tall gates it makes him sad. Maybe 8 year olds need gates. But do we need to protect 15 year olds like that? Students should be going out to see the world, both good and evil not hiding from it! We should instead be guiding them to the world. But instead, we are locking them up in brainwashing centers.

Video "The Purpose of Education by Noam Chomsky (2012)" puts it well, education can be either be:

a brainwashing to make people comply with The Establishment
a way to get people genuinely interested and help them to reach their life goals

He has spoken about that infinitely, e.g. from when he was thin: www.youtube.com/watch?v=JVqMAlgAnlo

Bibliography:

www.youtube.com/watch?v=ts7CEFQM2bE How Education Became Indoctrination: Dr Stephen Hicks (2021) Interview by www.youtube.com/c/KnowlandKnows Interesting channel. "Are you sick of woke-washing in education? Free speech distinguishes education from indoctrination" and "I taught at Eton College before I was fired because 'The Patriarchy Paradox' caused offence.".

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Edward Snowden Updated 2025-07-16

See also: cirosantilli.com/china-dictatorship/snowden

Figure 1.
Edward Snowden in 2013
. Source. From the film Prism, during interview with reporter Glenn Greenwald.

Edward Snowden original interview cut by The Guardian (2013)

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Edward Witten Updated 2025-07-16

This dude is generally viewed as a God. His incredibly understated demeanor and tone certainly help.

Unintentional ASMR | Sleepiest Interview Ever | Edward Witten

. Source. The title of this reupload is just epic. Edward telling his biography.

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