Critical Coins

(Illustration from:, copyright the artist.)

To make the process of art reviews and criticism more transparent and quantifiable, we can use cryptographic asset tokens to represent critical opinion and valences.

See here for how:

Easier Dogecode

I’ve added a Dogecode runner that uses‘s API rather than requiring a local dogepartyd instance to be running. You can get the runner as part of the Dogecode source code here: And you use it like this:

dcrunw DFibwNZvuJaHM9bD6x1WA63urkHiE4tWzF

which will fetch the program encoded as Dogeparty tokens at the specified address (DFib…) and run it locally. Here’s some addresses to try:






dcrun -u rpcuser -w rpcpassword DCvDS9g9VUZ94MSLbWi4zWRtxHrXeEctZ3
Hello World!

Cryptographic asset tokens can represent all kinds of things.

Including computer programs. Introducing…:


(There are several other projects called Dogecode. This isn’t them).

Dogecode takes computer programs in the Brainfuck programming language (chosen for simplicity of encoding):


and translates them into a csv file of token amounts using dcc:


which are then sent to a Dogeparty address (slowly) as a series of token transfer transactions using dcsend:

Sending lots of tokens. Make sure you really want to do this.
Waiting for token state to synch
Row 1: INCB,8
Waiting for token state to synch...........
Row 2: JFOR,1
Waiting for token state to synch.............
Row 3: INCP,1
Waiting for token state to synch........
Row 4: INCB,4
Waiting for token state to synch..................
Row 5: JFOR,1
Waiting for token state to synch.......................
Row 6: INCP,1
Waiting for token state to synch.......................
Row 7: INCB,2
Waiting for token state to synch......
Row 8: INCP,1
Waiting for token state to synch......
Row 9: INCB,3
Waiting for token state to synch......
Row 10: INCP,1
Waiting for token state to synch.....................
Row 11: INCB,3
Waiting for token state to synch..............
Row 12: INCP,1
Waiting for token state to synch.....

The transactions encode the program on the address, which can then be fetched and run as seen at the top of this post using dcrun.

For more details see the whitepaper.

Update: There’s an easier to use runner and more examples here.

Blockchain Aesthetics 2

Visualizing Bitcoin blockchain transactions – click on each image to run in your browser.

Each hash as instructions for a turtle graphics pen:


Life games with each hash as the starting board state:


A Chernoff Face of each hash:


Cellular automata with each hash as the initial row:


Quadratic curves with the bytes of each hash as the control point co-ordinates:


Essay Corpse – Accelerate Aesthetics

(This essay wouldn’t gel and I abandoned it. “XXXX…” means “do more here in the next writing or edit pass.”
Do get “Speculative Aesthetics” and “Class Wargames”, they are both wonderful books.)

Urbanomic’s “Speculative Aesthetics” is a freshly mined block of well-contextualised ideas, including some insightful discussion of Accelerationism’s relationship to aesthetics. That relationship is one I’ve been thinking and writing about in ignorant parallel.


I do not understand (I mean that literally: I’ve tried to parse the arguments and failed) the idea of overdetermined adherence to a teleological ideology as “freedom”. Whether the eschaton is religious, political/economic or technological, I don’t regard submission to its inevitability as freedom so much as a kind of Dice-Mannish false blamelessness. The fruit fly that buzzes in an endless repeating pattern through a featureless space is neither free nor showing free will. But then nor is the liberal consumer or subject of history in any absolute sense, however right Popper was about totalitarianism.


Abstraction is a word that describes many different phenomena, XXXXXXXX


At the same time as reading (or “reading“) “Speculative Aesthetics” I’ve been reading Richard Barbrook’s excellent”Class Wargames” book. Barbrook’s history of Guy Debord’s “Game of War” casts the game convincingly as a pedagogical tool for inoculating the political Left against the temptations of vanguard politics. Debord’s game represents the field and forces of battle at the time of the Napoleonic wars at a high level of abstraction compared to the average SPI/GDW-style wargame , making them a general representation of armed conflict. In order to win, one must recapitulate the tactics of Napoleon or Trotsky, with their attendant sacrifice and bloodshed. Having played at being Napoleon, players can both defeat them and will understand why they wouldn’t want to become or follow such a leader in real life.

This indicates the value of Acceleration’s strategy of abstraction, from specifics to generalities and back. Failing cloning, there will not literally be another Napoleon. But there will be scenarios in which another clever general might need to be defeated, either on the battlefield or in politics. There’s nothing Accelerationist about a board game, but there is about its use to create a distributed clever general. And it’s in distribution that Accelerationism can avoid vanguardism.


The legacy of CCRU can easily be painted as sitting uncomfortably with Accelerationism’s emphasis on rationality. But the irrational, imaginative, mystical, fictional nature of much CCRU/Orphan Drift/Nick Land output is rational – it can be rational to use irrationality when there is no rational means of achieving a rational end. This is instrumental irrationality, and it’s a standard part of creativity. Creativity theory tells us why – if thinking sensibly is leaving you stuck in a rut then thinking irrationally may get you out of it. Whether surrealist games, Edward de Bono’s “lateral thinking” or chemically assisted imaginings, XXXXXXXXX.


This is a very different kind of abstraction from the Real Abstraction of Marxism, or the representational simplifications and distillations of Data Visualization. Accelerationist aesthetics must achieve a new kind of abstraction via either a CCRUian instrumental irrationality as a mythological attractor and search space exploder, a Debordian detournement and redemption (rather than gentrification) of data visualization , or a remix of both.



I have placed my soul on the blockchain, representing it as a cryptographic asset token.

The MYSOUL token is on the Dogecoin blockchain as a Dogeparty asset:

I’ve divided it up into 100 units. This is more efficient that having a single token to represent the soul and transferring it to a single owner, as competition within the market will both reduce costs and allocate this resource more efficiently than a monopoly could.

To make ownership of my soul more accessible, I’ve also created a MYSOUL asset on the Bitcoin blockchain with Counterparty:

This is also divided up into 100 units. Counterparty is more expensive for transactions than Dogeparty, but is more widespread, so it’s good to have both options.

Blockchain Aesthetics


These images are examples of real-time generative patterns visualising Bitcoin transactions. I wrote them in html5 using’s WebSockets API to get notifications of the hash value of each new transaction.

You can click on each image to open a new window actually running that visualization.

Above, each row is a transaction with each byte of 32-byte hash rendered as a square of colour from a 256-colour palette.


Above, each sentence is a transaction rendered in a standard list of words.


Above, each bitmap is the 32-byte hash for each transaction hash rendered as a 16×16 1-bit bitmap (original Macintosh-style, 1 is black).


Above, each row is a transaction with each byte of 32-byte hash rendered as a spot of colour from a 256-colour palette.


Above, each transaction is rendered as a drawing of lines connecting x,y co-ordinate pairs taken from the low and high 4 bits in each 8-bit byte in the 32-byte transaction hash. Each transaction is joined to the next as part of the same continuous drawing.


Above, each bitmap is rendered as before and then blurred. A face recognition algorithm is used to find any collections of pixels that accidentally resemble faces, and these are outlined in red. This is machine pareidolia.

As well as clicking on the images to run each visualisation, you can view a list of them here (including both the block and transaction-based visualisations – the former run much slower):

You can get the code here:


We can use NLTK’s support for WordNet to help generate and classify text.

from nltk.corpus import wordnet as wn
from nltk.corpus import sentiwordnet as swn

def make_synset(word, category='n', number='01'):
    """Conveniently make a synset"""
    number = int(number)
    return wn.synset('%s.%s.%02i' % (word, category, number))

>>> dog = make_synset('dog')
>>> dog.definition
'a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds'

A synset is WordNet’s representation of a word/concept. Looking at the definition confirms that we have the synset for canis familiaris rather than persecution or undesirability.

>>> dog.hypernyms()
[Synset('domestic_animal.n.01'), Synset('canine.n.02')]

Hypernyms are more general concepts. ‘dog’ has two of them, which shows that WordNet is not arranged in a simple tree of concepts. This makes checking for common ancestors slightly more complex but represents concepts more realistically.

>>> dog.hyponyms()
[Synset('puppy.n.01'), Synset('great_pyrenees.n.01'), Synset('basenji.n.01'), Synset('newfoundland.n.01'), Synset('lapdog.n.01'), Synset('poodle.n.01'), Synset('leonberg.n.01'), Synset('toy_dog.n.01'), Synset('spitz.n.01'), Synset('pooch.n.01'), Synset('cur.n.01'), Synset('mexican_hairless.n.01'), Synset('hunting_dog.n.01'), Synset('working_dog.n.01'), Synset('dalmatian.n.02'), Synset('pug.n.01'), Synset('corgi.n.01'), Synset('griffon.n.02')]

Hyponyms are more specific concepts. ‘dog’ has several. These may have hypernyms other than ‘dog’, and may have several hyponyms themselves.

def _recurse_all_hypernyms(synset, all_hypernyms):
    synset_hypernyms = synset.hypernyms()
    if synset_hypernyms:
        all_hypernyms += synset_hypernyms
        for hypernym in synset_hypernyms:
            _recurse_all_hypernyms(hypernym, all_hypernyms)

def all_hypernyms(synset):
    """Get the set of hypernyms of the hypernym of the synset etc.
       Nouns can have multiple hypernyms, so we can't just create a depth-sorted
    hypernyms = []
    _recurse_all_hypernyms(synset, hypernyms)
    return set(hypernyms)

>>> all_hypernyms(dog)
>>> set([Synset('chordate.n.01'), Synset('living_thing.n.01'), Synset('physical_entity.n.01'), Synset('animal.n.01'), Synset('mammal.n.01'), Synset('object.n.01'), Synset('vertebrate.n.01'), Synset('entity.n.01'), Synset('carnivore.n.01'), Synset('domestic_animal.n.01'), Synset('canine.n.02'), Synset('placental.n.01'), Synset('organism.n.01'), Synset('whole.n.02')])

We can recursively fetch the hypernyms of a synset. since ‘dog’ has two hypernyms this isn’t a single list of hypernyms.
We can use this to find how similar different words are by searching for common ancestors.
The Python WordNet library can find common hypernyms for us though.

>>> cat = make_synset('cat')
>>> cat.common_hypernyms(dog)
[Synset('chordate.n.01'), Synset('living_thing.n.01'), Synset('physical_entity.n.01'), Synset('animal.n.01'), Synset('mammal.n.01'), Synset('vertebrate.n.01'), Synset('entity.n.01'), Synset('carnivore.n.01'), Synset('object.n.01'), Synset('placental.n.01'), Synset('organism.n.01'), Synset('whole.n.02')]
>>> steel = make_synset('steel')
>>> steel.common_hypernyms(dog)
[Synset('physical_entity.n.01'), Synset('entity.n.01')]
>>> sunset = make_synset('sunset')
>>> sunset.common_hypernyms(dog)

As might be expected, cats and dogs are more similar than steel or sunsets.
We can recursively fetch the hyponyms of a synset. This gives us the set of objects or concepts with a kind-of relationship to the word.

def _recurse_all_hyponyms(synset, all_hyponyms):
    synset_hyponyms = synset.hyponyms()
    if synset_hyponyms:
        all_hyponyms += synset_hyponyms
        for hyponym in synset_hyponyms:
            _recurse_all_hyponyms(hyponym, all_hyponyms)

def all_hyponyms(synset):
    """Get the set of the tree of hyponyms under the synset"""
    hyponyms = []
    _recurse_all_hyponyms(synset, hyponyms)
    return set(hyponyms)

>>> all_hyponyms(dog)
set([Synset('harrier.n.02'), Synset('water_spaniel.n.01'), Synset('standard_poodle.n.01'), Synset('dandie_dinmont.n.01'), Synset('wirehair.n.01'), Synset('toy_manchester.n.01'), Synset('puppy.n.01'), Synset('briard.n.01'), Synset('beagle.n.01'), Synset('siberian_husky.n.01'), Synset('manchester_terrier.n.01'), Synset('bloodhound.n.01'), ...

WordNet has some support for synonyms and antonyms via lemmas.

def synset_synonyms(synset):
    """Get the synonyms for the synset"""
    return set([lemma.synset for lemma in synset.lemmas])

def synset_antonyms(synset):
    """Get the antonyms for [the first lemma of] the synset"""
    return set([lemma.synset for lemma in synset.lemmas[0].antonyms()])

>>> synset_synonyms(sunset)
>>> synset_antonyms(sunset)

And we can find related concepts by getting all the hyponyms of a word’s hypernynms.

def all_peers(synset):
    """Get the set of all peers of the synset (including the synset).
       If the synset has multiple hypernyms then the peers will be hyponyms of
       multiple synsets."""
    hypernyms = synset.hypernyms()
    peers = []
    for hypernym in hypernyms:
        peers += hypernym.hyponyms()
    return set(peers)

>>> all_peers(sunset)
set([Synset('zero_hour.n.01'), Synset('rush_hour.n.01'), Synset('early-morning_hour.n.01'), Synset('none.n.01'), Synset('midnight.n.01'), Synset('happy_hour.n.01'), Synset('dawn.n.01'), Synset('bedtime.n.01'), Synset('late-night_hour.n.01'), Synset('small_hours.n.01'), Synset('noon.n.01'), Synset('sunset.n.01'), Synset('twilight.n.01'), Synset('mealtime.n.01'), Synset('canonical_hour.n.01'), Synset('closing_time.n.01')])

We use sets here so that common ancestors and children appear only once, and to allow for boolean set operations on concepts.
It’s trivial to get the the word (or words) for a synset.

def synsets_words(synsets):
    """Get the set of strings for the words represented by the synsets"""
    return set([synset_word(synset) for synset in synsets])

>>> synsets_words(all_hyponyms(dog))
set(['rottweiler', 'bull mastiff', 'belgian sheepdog', 'courser', 'brabancon griffon', 'toy terrier', 'fox terrier', 'sennenhunde', 'standard poodle', 'saluki', 'pointer', 'toy spaniel', 'setter', 'giant schnauzer', 'housedog', 'papillon', 'american foxhound', 'weimaraner', 'cocker spaniel', 'basenji', 'beagle', ...

WordNet has part/whole, group and substance relationships.

>>> body = make_synset('body')
>>> body.part_meronyms()
[Synset('arm.n.01'), Synset('articulatory_system.n.01'), Synset('body_substance.n.01'), Synset('cavity.n.04'), Synset('circulatory_system.n.01'), Synset('crotch.n.02'), Synset('digestive_system.n.01'), Synset('endocrine_system.n.01'), Synset('head.n.01'), Synset('leg.n.01'), Synset('lymphatic_system.n.01'), Synset('musculoskeletal_system.n.01'), Synset('neck.n.01'), Synset('nervous_system.n.01'), Synset('pressure_point.n.01'), Synset('respiratory_system.n.01'), Synset('sensory_system.n.02'), Synset('torso.n.01'), Synset('vascular_system.n.01')]

>>> dog.member_holonyms()
[Synset('canis.n.01'), Synset('pack.n.06')]

>>> wood = make_synset('wood')
>>> wood.substance_holonyms()
[Synset('beam.n.02'), Synset('chopping_block.n.01'), Synset('lumber.n.01'), Synset('spindle.n.02')]
>>> wood.substance_meronyms()

We can use hypernyms to classify words into domains using WordNet, but there’s an existing domain classification system in the form of WordNet Domains. It can be downloaded here. Code for using this can be found on Stack Overflow. But it doesn’t seem to work with nltk 3.0 (the synset numbers don’t match).

And there’s a sentiment score system for WordNet in the form of SentiWordNet. There’s an interface for it in WordNet 3.0.

def make_senti_synset(word, category='n', number='01'):
    """Conveniently make a senti_synset"""
    number = int(number)
    return swn.senti_synset('%s.%s.%02i' % (word, category, number))

def synsets_sentiments(synsets):
    """Return the objs, pos, neg and pos - neg score sums for the synsets"""
    pos = 0.0
    obj = 0.0
    neg = 0.0
    for synset in synsets:
            pos += synset.pos_score()
            obj += synset.obj_score()
            neg += synset.neg_score()
        except AttributeError, e:
    return obj, pos, neg, pos - neg

>>> happy = make_senti_synset('happy', 'a')
>>> happy.pos_score()
>>> happy.neg_score()
>>> happy.obj_score()

synsets_sentiments([make_senti_synset(word, 'a') for word in 'happy sad angry heavy light depressing'.split()])
(2.5, 1.5, 2.0, -0.5)

Not every word has a sentiment score, hence the try/except block in synsets_sentiments.

WordNet is sensitive to senses and it’s hard to automatically resolve senses when processing arbitrary text. When generating text and using WordNet to find words, it’s important (and easier) to set the correct sense for the synset.

>>> colour = make_synset('colour', 'n', 6)
>>> all_hyponyms(colour)
set([Synset('chrome_red.n.01'), Synset('primary_color.n.01'), Synset('light_brown.n.01'), Synset('sallowness.n.01'), Synset('hazel.n.04'), Synset('iron-grey.n.01'), Synset('olive_green.n.01'), Synset('tan.n.02'), Synset('pastel.n.01'), Synset('coal_black.n.01'), Synset('pinkness.n.01'), Synset('vandyke_brown.n.01'), Synset('beige.n.01'), Synset('blue.n.01'), Synset('shade.n.02'), Synset('achromatic_color.n.01'), Synset('whiteness.n.03'), Synset('coral.n.01'), Synset('chromatism.n.02'), Synset('apatetic_coloration.n.01'), ...

This gives concepts on different levels. Maybe if we try the peers of a colour.

>>> all_peers(make_synset('red'))
set([Synset('red.n.01'), Synset('pastel.n.01'), Synset('purple.n.01'), Synset('green.n.01'), Synset('olive.n.05'), Synset('complementary_color.n.01'), Synset('brown.n.01'), Synset('blue.n.01'), Synset('blond.n.02'), Synset('yellow.n.01'), Synset('orange.n.02'), Synset('pink.n.01'), Synset('salmon.n.04')])

OK maybe if we try the children of a concept.

>>> all_hyponyms(make_synset('chromatic_color'))
set([Synset('chrome_red.n.01'), Synset('light_brown.n.01'), Synset('hazel.n.04'), Synset('olive_green.n.01'), Synset('tan.n.02'), Synset('pastel.n.01'), Synset('pinkness.n.01')

Perhaps the leaf nodes.

def _recurse_leaf_hyponyms(synset, leaf_hyponyms):
    synset_hyponyms = synset.hyponyms()
    if synset_hyponyms:
        for hyponym in synset_hyponyms:
            _recurse_all_hyponyms(hyponym, leaf_hyponyms)
        leaf_hyponyms += synset

def leaf_hyponyms(synset):
    """Get the set of leaf nodes from the tree of hyponyms under the synset"""
    hyponyms = []
    _recurse_leaf_hyponyms(synset, hyponyms)
    return set(hyponyms)

>>> leaf_hyponyms(make_synset('chromatic_color'))
set([Synset('taupe.n.01'), Synset('snuff-color.n.01'), Synset('chrome_red.n.01'), Synset('light_brown.n.01'), Synset('hazel.n.04'), Synset('olive_drab.n.01'), Synset('old_gold.n.01'), Synset('chocolate.n.03'), Synset('yellowish_pink.n.01'), Synset('yellowish_brown.n.01'), Synset('tyrian_purple.n.02'), ...

That looks good. All colours, no intermediate concepts.

We can use this set of words to choose colours, or to categorize words as colours.

I hope this demonstrates that WordNet can be a very useful resource for Generative Art and Digital Humanities projects.