Previously called "bitcoin-strings-with-txids" since text was the initial focus, but Ciro Santilli decided to go for the more general name once images became more and more important to the project.
Set of scripts b Ciro Santilli, primarily created while researching Cool data embedded in the Bitcoin blockchain.
TODO who owns it? Are they reliable?
- transaction hex data: blockchain.info/tx/930a2114cdaa86e1fac46d15c74e81c09eee1d4150ff9d48e76cb0697d8e1d72?format=hex
- disassembled transaction as JSON: blockchain.info/tx/930a2114cdaa86e1fac46d15c74e81c09eee1d4150ff9d48e76cb0697d8e1d72?format=json
- block by height:
This helper dumps a transaction JSON to a binary:
bitcoin-tx-out-scripts() (
# Dump data contained in out scripts. Remove first 3 last 2 bytes of
# standard transaction boilerplate.
h="$1"
echo curl "https://blockchain.info/tx/${h}?format=json" |
jq '.out[].script' tmp.json |
sed 's/"76a914//;s/88ac"//' |
xxd -r -p > "${h}.bin"
)TODO: it would be cool to have something like bitcoinstrings.com but including the actual transactions:
Local methods:
- Bitcoin Inscription Indexer
- bitcoin.stackexchange.com/questions/30295/how-can-i-search-for-transaction-text-on-the-blockchain
- bitcoin.stackexchange.com/questions/22500/is-there-a-lightweight-blockchain-parser-library-server/101472#101472
- github.com/alecalve/python-bitcoin-blockchain-parser
- bitcoin.stackexchange.com/questions/84266/wondering-how-to-use-bitcoin-parser
- github.com/bitcoinprivacy/Bitcoin-Graph-Explorer stores the blockchain in a database, and should allow more intelligent querying.
Further bibliography:
- bitcoin.stackexchange.com/questions/799/can-i-download-the-whole-block-chain-from-somewhere
- bitcoin.stackexchange.com/questions/68925/how-can-data-be-accessed-searched-for-in-a-blockchain
- bitcoin.stackexchange.com/questions/55188/download-single-and-specific-block-for-study-purposes
- www.fiverr.com/usefulshine/embed-your-logo-or-brand-art-on-blockchain user usefulshine from India embeds ASCII art for you into the blockchain starting at 260 dollars! XD
There are apparently two methods:
- in the script, e.g. as in the Genesis block message
- in output addresses
Specific implementations:
- eternitywall.it/ Eternity WallLaunched 2015 www.newsbtc.com/news/bitcoin/eternity-wall-records-1150-documents-blockchain-first-year/Shutdown sometime after 2019, working archive: web.archive.org/web/20190417074034/https://eternitywall.it/ says "Sorry, the service is not properly working at the moment..." and last working message timestamped "April 16, 2019 8:02 PM GMT".
is a hyperparameter, and are common choices when doing dataset exploration, as they can be easily visualized on a planar plot.
The mapping is done by projecting all points to a dimensional hyperplane. PCA is an algorithm for choosing this hyperplane and the coordinate system within this hyperplane.
The hyperplane choice is done as follows:
- the hyperplane will have origin at the mean point
- the first axis is picked along the direction of greatest variance, i.e. where points are the most spread out.Intuitively, if we pick an axis of small variation, that would be bad, because all the points are very close to one another on that axis, so it doesn't contain as much information that helps us differentiate the points.
- then we pick a second axis, orthogonal to the first one, and on the direction of second largest variance
- and so on until orthogonal axes are taken
www.sartorius.com/en/knowledge/science-snippets/what-is-principal-component-analysis-pca-and-how-it-is-used-507186 provides an OK-ish example with a concrete context. In there, each point is a country, and the input data is the consumption of different kinds of foods per year, e.g.:so in this example, we would have input points in 4D.
- flour
- dry codfish
- olive oil
- sausage
Suppose that every country consumes the same amount of flour every year. Then, that number doesn't tell us much about which country each point represents (has the least variance), and the first PCA axes would basically never point anywhere near that direction.
Another cool thing is that PCA seems to automatically account for linear dependencies in the data, so it skips selecting highly correlated axes multiple times. For example, suppose that dry codfish and olive oil consumption are very high in Portugal and Spain, but very low in Germany and Poland. Therefore, the variation is very high in those two parameters, and contains a lot of information.
However, suppose that dry codfish consumption is also directly proportional to olive oil consumption. Because of this, it would be kind of wasteful if we selected:since the information about codfish already tells us the olive oil. PCA apparently recognizes this, and instead picks the first axis at a 45 degree angle to both dry codfish and olive oil, and then moves on to something else for the second axis.
Officially supported installation method on Ubuntu 23.10.
These are commands that e.g. the Bitcoin CLI client can make to the server.
The commands can be listed with:and full help with:
bitcoin-core.cli helpbitcoin-core.cli help getrawtransactionFor example. to run the Bitcoin and then on another shell:
getrawtransaction command, first in one shell we start bitcoind:bitcoin-core.daemonbitcoin-core.cli getrawtransaction 75b431e0a8c4617ca8adefe593ba66aa30907742b6dc8772761bfe7edabd74b4 trueYou can then interact with it via the Bitcoin CLI client.
Pinned article: Introduction to the OurBigBook Project
Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
Intro to OurBigBook
. Source. We have two killer features:
- topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculusArticles of different users are sorted by upvote within each article page. This feature is a bit like:
- a Wikipedia where each user can have their own version of each article
- a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
Figure 1. Screenshot of the "Derivative" topic page. View it live at: ourbigbook.com/go/topic/derivativeVideo 2. OurBigBook Web topics demo. Source. - local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
- to OurBigBook.com to get awesome multi-user features like topics and likes
- as HTML files to a static website, which you can host yourself for free on many external providers like GitHub Pages, and remain in full control
Figure 3. Visual Studio Code extension installation.
Figure 4. Visual Studio Code extension tree navigation.
Figure 5. Web editor. You can also edit articles on the Web editor without installing anything locally.Video 3. Edit locally and publish demo. Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.Video 4. OurBigBook Visual Studio Code extension editing and navigation demo. Source. 
- Infinitely deep tables of contents:
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact