Missing a double entendre is when, in social intercourse, there is another meaning upon the lips, that one fails to fully wrap their head around or penetrate the whole of the available depth, a sort of premature interpretation.

Within this oral intercourse,

There is a seeming, source

Of a premature interpretation.

Flip it with a second destination.

On those lips, left wanting,

of a deeper thrust,

a pair of content's taunting,

their points securely trussed.

Beyond the naked point's, another.

A spectrum of awaited breadth,

It's hard to grasp

the whole, of the penetrable depth.

When in oral intercourse there is pair of points upon the lips, yet a failure to grasp the whole of the penetrable depth.

Double Entendre: n. pair of material and both points are grasped, and the whole of available depth is penetrated.

Double Entendre: when there is considerable pair, and both points are grasped

--- My hope here was to make a good and quippy definition for what a double entendre is, but I dunno, these seem a bit lackluster.

## Tuesday, October 3, 2017

## Friday, September 22, 2017

### Adding to my list of oddities in computer science.

CS folks are enamored of dividing 2D space in 2D space chunks? It makes all of the math needlessly harder, even if it makes the math-sense and understandability easier. You divide 2D space into two instances of 1D space. The algorithms don't require trees, you can still easily access the needed data in log(n) time, update them as easy as a sorted list can be updated, and with a bit of style you can easily prove points don't need to be checked. It's weird because no algorithms do this, but you can do an amazing amount of very simple math to solve all the traditional problems in computational geometry with simpler algorithms.

But the really really pressing bits of this is that categorically these algorithms are tiny. Like they don't take much work at all. They are exceptionally short. And the datastructure is what modern memory systems are optimized for so you won't be cache missing all the time. And it's so common it's implemented in like four lines of code. So why the hell are all games doing collision detection of divided up 2D space in 2D chunks or 3D chunks in 3D space? Also, these algorithms obviously scale really easily with the degree of dimensionality. They don't violate theoretical points though. If you do a Nearest Neighbor Search in 3D it's basically identical code with the steps as the 2D code but since we're dealing with 3D, it can easily end up checking most all the points anyway simply because you can't rule out the remaining checks as quickly.

I needed a nearest neighbor search for my android app to find the nearest point to a point and do this many many times in real time. But, when a point is moved it required a lot to fix the pre-processed datastructure since they were organized into trees so the nodes needed to be fiddled with etc. This allowed for nodes to be updated en masse, and the lists to simply be very quickly resorted in a trivial amount of time and perfectly ready for the next tick.

I hit upon this algorithm largely because I had also done considerable work with filling monotone polygons using a scanline, and used a sort of step-raytrace with line segments which is another brilliant algorithm (it's very similar to the good solution to the skyline problem), it's largely just a single list of all the points sorted along one axis. Then you scan through the list, keeping a working list of active line segments. This solves the perpendicular rays and finds all intersections of that perpendicular ray (it's literally the intersections of the active list of line segments) in fast enough time that a little android phone can do the ray traced lines in real time.

To speed up my attract tool (requires many nearest neighbor searches), I previously made an interesting nearest neighbor search with a point quadtree which equally used the same general idea that you could prove the invalidity of other parts of the tree by showing that the distance in just 1 dimension is greater than the shortest distance already found. I thought this was awesome as nobody seemed to have any nearest neighbor algorithms based on that rather rare datastructure.

But, if you could do this with a tree it would be much easier to do this with 2 different 1D sorted lists. Just binary search into the list, and go to each point in the +X, +Y, -X, -Y directions until the distance in just dX is greater than your best candidate so far. You can also conclude things like if +X and -X are exhausted then there can be no closer point, as you proved all points exhausted (the points found in the other dimensions are going to be duplicates of the ones you ruled out). But, the point is implementing all of that is trivial compared to the other ways. And all the tricks in computational geometry are simply proving you don't have to check those other points, which is can be done with the same trick over and over, can this point further along this axis possibly be closer or included? For the K-nearest neighbor algorithm, you store the candidates in a heap sorted by distance and only compare the worst of them to the new potential candidates until just that one direction along is enough to be worse in which case no more points along that axis can possibly be better.

For Axis Aligned Bounding Boxes (AABB) you can rather simply add the start points and end points of the shapes. Then when you cross a threshold by traveling along the axis (only going to the next relevant point always as that's how the traversal works), you can trivially check if the relevant point is within that bounding box. So you can rather easily linearly iterate into the list and you'll know exactly which bounding boxes that point is within tracking them basically exactly as one would the skyline problem. Then moving that point within the space equally quite quick and just means updating any active elements as you cross thresholds (and if you don't cross any relevant boundaries, changing nothing at all). Equally it solves a number of common problems in collision detections like not updating something that moved in time to see that it struck something. As the object moves it needs to find it's place in the lists, which means you must process each element in order, which means you know if you hit anything, regardless whether you would now be completely on the other side of that object now.

I totally understand that it's easier to think in 3d space in 3d and 2d space in 2d so when you're partitioning space you are wildly more likely to divide the space in ways that does not screw up the dimensionality. It's easy to think of dividing a square into two half-squares but it's a bit weird to divide it into two equally lengthed lines. But the math is easier if you divide it into two lines for obvious reasons, even if it's harder to conceptualize.

- You can build the space in d*n*log(n) time (sorting lists).
- With sorted lists of points you can insert in as easy as inserting into a list.
- You can remove as easily as removing from a list.
- You can find the nearest neighbor to a point in a short algorithm in log(n) time.
- You can find k nearest neighbors to a point in a similar short algorithm in ~k*log(k)*log(n) time.
- You can do basically all the AABB math quite easily too. You can easily find in n time the bounding boxes relevant to a point. And update that point very easily.
- You can find if a point is inside a polygon.
- You can find all points within an axis aligned rectangle in trivial time.

But the really really pressing bits of this is that categorically these algorithms are tiny. Like they don't take much work at all. They are exceptionally short. And the datastructure is what modern memory systems are optimized for so you won't be cache missing all the time. And it's so common it's implemented in like four lines of code. So why the hell are all games doing collision detection of divided up 2D space in 2D chunks or 3D chunks in 3D space? Also, these algorithms obviously scale really easily with the degree of dimensionality. They don't violate theoretical points though. If you do a Nearest Neighbor Search in 3D it's basically identical code with the steps as the 2D code but since we're dealing with 3D, it can easily end up checking most all the points anyway simply because you can't rule out the remaining checks as quickly.

I needed a nearest neighbor search for my android app to find the nearest point to a point and do this many many times in real time. But, when a point is moved it required a lot to fix the pre-processed datastructure since they were organized into trees so the nodes needed to be fiddled with etc. This allowed for nodes to be updated en masse, and the lists to simply be very quickly resorted in a trivial amount of time and perfectly ready for the next tick.

I hit upon this algorithm largely because I had also done considerable work with filling monotone polygons using a scanline, and used a sort of step-raytrace with line segments which is another brilliant algorithm (it's very similar to the good solution to the skyline problem), it's largely just a single list of all the points sorted along one axis. Then you scan through the list, keeping a working list of active line segments. This solves the perpendicular rays and finds all intersections of that perpendicular ray (it's literally the intersections of the active list of line segments) in fast enough time that a little android phone can do the ray traced lines in real time.

To speed up my attract tool (requires many nearest neighbor searches), I previously made an interesting nearest neighbor search with a point quadtree which equally used the same general idea that you could prove the invalidity of other parts of the tree by showing that the distance in just 1 dimension is greater than the shortest distance already found. I thought this was awesome as nobody seemed to have any nearest neighbor algorithms based on that rather rare datastructure.

But, if you could do this with a tree it would be much easier to do this with 2 different 1D sorted lists. Just binary search into the list, and go to each point in the +X, +Y, -X, -Y directions until the distance in just dX is greater than your best candidate so far. You can also conclude things like if +X and -X are exhausted then there can be no closer point, as you proved all points exhausted (the points found in the other dimensions are going to be duplicates of the ones you ruled out). But, the point is implementing all of that is trivial compared to the other ways. And all the tricks in computational geometry are simply proving you don't have to check those other points, which is can be done with the same trick over and over, can this point further along this axis possibly be closer or included? For the K-nearest neighbor algorithm, you store the candidates in a heap sorted by distance and only compare the worst of them to the new potential candidates until just that one direction along is enough to be worse in which case no more points along that axis can possibly be better.

For Axis Aligned Bounding Boxes (AABB) you can rather simply add the start points and end points of the shapes. Then when you cross a threshold by traveling along the axis (only going to the next relevant point always as that's how the traversal works), you can trivially check if the relevant point is within that bounding box. So you can rather easily linearly iterate into the list and you'll know exactly which bounding boxes that point is within tracking them basically exactly as one would the skyline problem. Then moving that point within the space equally quite quick and just means updating any active elements as you cross thresholds (and if you don't cross any relevant boundaries, changing nothing at all). Equally it solves a number of common problems in collision detections like not updating something that moved in time to see that it struck something. As the object moves it needs to find it's place in the lists, which means you must process each element in order, which means you know if you hit anything, regardless whether you would now be completely on the other side of that object now.

I totally understand that it's easier to think in 3d space in 3d and 2d space in 2d so when you're partitioning space you are wildly more likely to divide the space in ways that does not screw up the dimensionality. It's easy to think of dividing a square into two half-squares but it's a bit weird to divide it into two equally lengthed lines. But the math is easier if you divide it into two lines for obvious reasons, even if it's harder to conceptualize.

## Monday, September 4, 2017

### On Intelligence being evolution.

Wrote this elsewhere as personal correspondence, but it makes some points I'd rather not forget for future reference.
I'm a computer scientist and one of my notable skills is understanding algorithms that underlie things. In 2007, I watched a pretty great TED talk by Jeff Hawkins ( Jeff Hawkins on how brain science will change computing. ) which had some really critical ideas about understanding intelligence and it made me realize that if brains predict the future, and that seems right, then the algorithm for intelligence is necessarily: evolution. Now, I don't mean this to say intelligence evolved, I mean this to say that a child from infancy to adulthood how they work in the brain is fundamentally an implementation of evolution. And I don't mean that lightly. There's a few people who note that artificial neural nets have a somewhat evolutionary strengthening, or folks who argue that brains work kind of like the adaptive immune system. I mean this strongly like algorithmically evolution and intelligence are identical.
It seems a bit weird at first, but the more you let it infect your mind the more you realize it explains things. Creationists always look at trees and insist they were intelligently designed. Because they look at trees and the products of intelligence and say; the causes of these things are the same. We turn around and say we know the tree formed through evolution and therefore that statement is not true; but I think this is wrong. I think this rather shockingly strong *illusion* of intelligence is no fiction at all. Sometimes the opposite of a profound truth is another profound truth. The error we make is in assuming we know what the fuck intelligence is and are therefore correct in dismissing Darwinian evolution as a form of it; I think it is a form of it because at the core the two are the same thing. The argument from design says that the two things are the same. We say that argument is wrong because the tree is the result of evolution by natural selection. But, there's a missing premise there required to deny the argument: intelligence is not evolution. And I think that premise is false; It is evolution.
This also gives us a few interesting requirements about what is required between senses and minds; namely there's no requirement for a connection at all. It isn't as if the environment programs DNA of various species. There's no direct connection there at all. Rather the things that work effectively are strengthened and those that don't do not. Likewise if brains are making predictions of things fairly randomly and are only being confirmed by your senses, then we necessarily do not need our senses to direct our actions at all. Without senses we'd start hallucinating wildly, with them we hallucinate coherently. Our senses only need to confirm things after the fact. So some weird items like how we can swing a bat in less time than we could have decided to, just sort of evaporate. And seemingly profound thought experiments like Searle's Chinese Box become rather trite. Undoubtedly you can think of dozens more examples that quickly get explained away.
So the posted article contains things like "Your mind filters, sorts, selects, contains, and bestows meaning to an element in the endless stream of external data flooding your senses." -- This is completely pointless and wrong. Your mind doesn't need to do any of that because our senses don't do any of that. We invent a world inside our own heads with various theories and ideas and understandings, and then our senses tell us whether that stuff is right or not and we react accordingly. This is also largely why in sensory deprivation chambers or our sleep we kind of go a little crazy, because nothing ends up registering as being more true than anything else so we start hallucinating. We also get basically every form of hallucination for free, from excess details that we can't really see to the blind spot in our eyes, and why we make up intermediate steps between events even if they didn't exist. Under my theory, we not only get these for free; they are required to be true.
It also broadly explains why "there is a difference between what you experience, and what exists out there: The virtual can never be real. The essence of your understanding is, in effect, rooted in this difference. Where does difference arise?" -- The difference arises because the stuff we perceive is completely made up. Everything about our understanding of the world what we think the world actually is, is a confabulation that was made up inside our head, and whittled down by our senses. Just as every nucleotide in our very amazing DNA was a random mutation. It sounds like this should completely unmoor our understanding of reality from reality itself and it does, but our senses narrow that range of possibilities and those elements, just as the process of selection culls random mutations into "endless forms most beautiful".
If you take this idea seriously, you will quickly realize that it starts knocking down barriers by its mere existence in your head. It not only explains the veil but why it must exist. Our very nature as intelligent beings requires that this is how we work. Most of the objections to George Berkeley's sort of idealism ((1) We perceive ordinary objects (houses, mountains, etc.). (2) We perceive only ideas. Therefore, (3) Ordinary objects are ideas.) are generally that reality is real and it is, but if you understand what intelligence is you can thread the needle aptly enough to take the correct points from idealism and from materialism. It's entirely true that reality comes only from us internally and that reality is not somehow the result of our senses grabbing reality and pulling it into our head. Rather the reality in our head is influenced by our senses and our ability to predict what we well sense next requires us to come up with an internal view of reality that need not be anything like actual reality (in fact we know a lot of aspects like color are just ad hoc inventions and do not exist outside our heads) it's filled in with a bunch of hallucinations and randomness because the internal view of reality need not have anything to do with actual reality, except that we have consistent ideas about reality that have evolved and they are remarkably resilient. Just as evolved forms need not be efficient hunters or energy gatherers but because of the nature of evolution that's how they end up. We need not have a good grasp of reality and make correct predictions and correct understandings, but because of the nature of what evolution does, the more efficient forms are preferred. In other words, under this understanding intelligence will end up being as robust as the web of life itself.
Update, based on a reply discussing evolutionary epistemology:
No. I am not at all saying evolutionary epistemology. Fucking obviously evolution formed our senses, but that's not even remotely the point. I'm thinking I missed the point if you couldn't get your head around it enough to stop you from jumpping down one of the many different lines of similar but all equally inexact thoughts. I mean what I say more profoundly than that, like expelling the hobgoblins there and replacing the whole shebang. I wrote it well and tried to stress the points to help get your head around it, but apparently it still missed the mark. Imagine if you would that there is no such thing as intelligence. That that which we call intelligence is entirely just evolution taking place in neurons. Not that senses are evolved or brains are evolved or that brains use vaguely reinforcing neurons that are akin to evolutionary processes; no. Everything about intelligence is just evolution taking place in the medium of brains. That the mind is running an evolutionary algorithm. That's how it entirely works.

## Wednesday, June 28, 2017

### A conclusion shopping around for a theory.

You sir are a donkey fucker. Okay, maybe you're not a donkey fucker, since there's no evidence of that. You sir, are a chicken fucker. Wait, no evidence there either. You sir are a squirrel fucker. -- Look, I have my conclusion, you fuck some kind of animal and when that type of animal is shot-down, I'm going to shop around for another type of animal. Because that's how this shit works apparently. --- Make some shit up, when that gets demolished, make different similar shit up, with generally the same conclusion.

You catch the analogy there goat-fucker?

## Tuesday, June 27, 2017

### Approximating an arc with cubic bezier curves, yet another metric.

C = 0.552

Really,

C = 0.55201372171

The idea is that rather than minimize the extrema of the error we can instead minimize the total error. Graph out formula for the error and calculate what would be under the curve as a whole. So rather than positive and negative error equal it would seek to minimize the error overall. It's not too different from the other metrics. But, rather than trying to keep the first pixel from being wrong, it tries to keep the pixels as a whole from being wrong for as long as possible. It's different than the number for the extrema because oddly the graph is symmetric at 0.5. It's not symmetric with regard to the y axis, and has different amounts areas of positive and negative and the error shifts as the value of c shifts.

Mortensen's value. Even extrema point.

0.55191502449 at 0.0000001 increments

Total error:1180.57375326880434046054832219597498652621901536765190

Samples: 10000001

My calculated value, brute force.

0.55201372171 at 0.0000001 increments

Total error:1159.83397426356807198675826572857280613256846239728729

Samples: 10000001

Naive geometric value, with purely positive error ((4/3)*(sqrt(2) - 1).

0.552284749 at 0.0000001 increments

Total error:1401.62730758375303300973494715872257045283590056376636

Samples: 10000001

As we can see the total error over a million samples gives us an improvement of 20. Compared to the naive value for all error being positive we gain 242 which is better than Mortensen's gain of 222.

We're talking literally fractions of percents here, but this number has another advantage. You can call it .552 which is a much shorter fraction. Besides the more slices I do the more I narrow in on the value, but it's still a bit off. I'm pretty sure on most of those digits but without actually doing the calculus I can't get much better than that.

0.552 at 0.0000001 increments

Total error:1160.26180861006145200701189677079769078925976995284068

Samples 10000001

You'll notice my value is only 0.4279 different than that in total error over a million samples. And since that truncation lowers it, it will only make it a bit closer to the even extrema point, which is a fine metric.

P_0 = (0,1), P_1 = (c,1), P_2 = (1,c), P_3 = (1,0)

P_0 = (1,0), P_1 = (1,-c), P_2 = (c,-1), P_3 = (0,-1)

P_0 = (0,-1), P_1 = (-c,-1), P_2 = (-1,-c), P_3 = (-1,0)

P_0 = (-1,0), P_1 = (-1,c), P_2 = (-c,1), P_3 = (0,1)

with c = 0.552

Under this metric the cubic value something like: 0.92103099 which might be more important because really flattening out the error might matter in that case a lot more than in the cubic case. I'll call it 0.921

Really,

C = 0.55201372171

The idea is that rather than minimize the extrema of the error we can instead minimize the total error. Graph out formula for the error and calculate what would be under the curve as a whole. So rather than positive and negative error equal it would seek to minimize the error overall. It's not too different from the other metrics. But, rather than trying to keep the first pixel from being wrong, it tries to keep the pixels as a whole from being wrong for as long as possible. It's different than the number for the extrema because oddly the graph is symmetric at 0.5. It's not symmetric with regard to the y axis, and has different amounts areas of positive and negative and the error shifts as the value of c shifts.

Mortensen's value. Even extrema point.

0.55191502449 at 0.0000001 increments

Total error:1180.57375326880434046054832219597498652621901536765190

Samples: 10000001

My calculated value, brute force.

0.55201372171 at 0.0000001 increments

Total error:1159.83397426356807198675826572857280613256846239728729

Samples: 10000001

Naive geometric value, with purely positive error ((4/3)*(sqrt(2) - 1).

0.552284749 at 0.0000001 increments

Total error:1401.62730758375303300973494715872257045283590056376636

Samples: 10000001

As we can see the total error over a million samples gives us an improvement of 20. Compared to the naive value for all error being positive we gain 242 which is better than Mortensen's gain of 222.

We're talking literally fractions of percents here, but this number has another advantage. You can call it .552 which is a much shorter fraction. Besides the more slices I do the more I narrow in on the value, but it's still a bit off. I'm pretty sure on most of those digits but without actually doing the calculus I can't get much better than that.

0.552 at 0.0000001 increments

Total error:1160.26180861006145200701189677079769078925976995284068

Samples 10000001

You'll notice my value is only 0.4279 different than that in total error over a million samples. And since that truncation lowers it, it will only make it a bit closer to the even extrema point, which is a fine metric.

P_0 = (0,1), P_1 = (c,1), P_2 = (1,c), P_3 = (1,0)

P_0 = (1,0), P_1 = (1,-c), P_2 = (c,-1), P_3 = (0,-1)

P_0 = (0,-1), P_1 = (-c,-1), P_2 = (-1,-c), P_3 = (-1,0)

P_0 = (-1,0), P_1 = (-1,c), P_2 = (-c,1), P_3 = (0,1)

with c = 0.552

Under this metric the cubic value something like: 0.92103099 which might be more important because really flattening out the error might matter in that case a lot more than in the cubic case. I'll call it 0.921

## Friday, June 16, 2017

### Using a quad to emulate an arc, solving for C.

Basic geometry allows one to solve for the best solution to use a value of C to make a bezier curve close to an arc. The naive solution using Geometry is

This is the solution to the question of we have anchor points on the curve and the curve touches the circle at the center, what value of C for a control point on the quadrant curve (0,1),(C,C),(1,0). Gives us the best fit to the curve.

However Mortenson points out that we are better if we minimize the error rather than allow all the error to be on one side of the circle.

This issue allows a few more percent with the Cubic Bezier form. But, it's more important if we're using one fewer control points. The same is also true at one fewer yet. If we have a square, what square looks most similar to a circle.

The if the naive geometry solution is the same we get:

. Where the corners touch the circle but do not exceed it. The other way you could do it is to have them only touch at the corners and just make the curve larger than the circle.

And finally we have Mortensen's solution to it, by making the metric for closest be the average error across the entire graph.

Well, solving this for 1 control point means doing the same thing he did. Which isn't super-trivial because it means calculating min and max error and adjusting various things. So I wrote a program to do it.

Your C points in Quad Bezier curves are:

C= 0.92519820883625651516056680057654772234129017577799879993432335355559372311727101122467939843041708056568481020881580812152533756252840773920708961655740969447362217856208142868847332990974178763987890

Having the program I refigured it for Cubic:

0.55191502449351057074356272279256664233618039472430889733698053746758709885277817592685338345358001614300815257463095148547403466508799941938848910944812198495136713728128014911200347760723733292314480

Mortensen gave this as,

0.551915024494

Since we might well be using doubles I'd give it as:

0.5519150244935106

M0,1

C0.5519150244935106,1 1,0.5519150244935106 1,0

C1,-0.5519150244935106 0.5519150244935106,-1 0,-1

C-0.5519150244935106,-1 -1,-0.5519150244935106 -1,0

C-1,0.5519150244935106 -0.5519150244935106,1 0,1

And the c value for the quad bezier curve as:

0.9251982088362565

M0,1

Q0.9251982088362565 0.9251982088362565 1,0

Q0.9251982088362565 -0.9251982088362565 0,-1

Q-0.9251982088362565 -0.9251982088362565 -1,0

Q-0.9251982088362565 0.9251982088362565 0,1

This is much better than the more naive value: 0.91421356237 which is effectively unusable. While Mortensen's use for the cubic is great, it changes the quad naive to almost usable, generally not, but *almost* usable. The Mortensen-optimized value for quads is off by max 0.007767318 whereas the naive value is off by 0.010781424258 which is 28% better. Hm. That's the same value Mortensen got for the cubic.

Naive Quad: For comparison.

However Mortenson points out that we are better if we minimize the error rather than allow all the error to be on one side of the circle.

The if the naive geometry solution is the same we get:

And finally we have Mortensen's solution to it, by making the metric for closest be the average error across the entire graph.

Well, solving this for 1 control point means doing the same thing he did. Which isn't super-trivial because it means calculating min and max error and adjusting various things. So I wrote a program to do it.

Your C points in Quad Bezier curves are:

C= 0.92519820883625651516056680057654772234129017577799879993432335355559372311727101122467939843041708056568481020881580812152533756252840773920708961655740969447362217856208142868847332990974178763987890

Having the program I refigured it for Cubic:

0.55191502449351057074356272279256664233618039472430889733698053746758709885277817592685338345358001614300815257463095148547403466508799941938848910944812198495136713728128014911200347760723733292314480

Having a deviation in both directions of: ±0.00019607646987687817401874512914923 ....

Mortensen gave this as,

0.551915024494

Since we might well be using doubles I'd give it as:

0.5519150244935106

M0,1

C0.5519150244935106,1 1,0.5519150244935106 1,0

C1,-0.5519150244935106 0.5519150244935106,-1 0,-1

C-0.5519150244935106,-1 -1,-0.5519150244935106 -1,0

C-1,0.5519150244935106 -0.5519150244935106,1 0,1

And the c value for the quad bezier curve as:

0.9251982088362565

M0,1

Q0.9251982088362565 0.9251982088362565 1,0

Q0.9251982088362565 -0.9251982088362565 0,-1

Q-0.9251982088362565 -0.9251982088362565 -1,0

Q-0.9251982088362565 0.9251982088362565 0,1

This is much better than the more naive value: 0.91421356237 which is effectively unusable. While Mortensen's use for the cubic is great, it changes the quad naive to almost usable, generally not, but *almost* usable. The Mortensen-optimized value for quads is off by max 0.007767318 whereas the naive value is off by 0.010781424258 which is 28% better. Hm. That's the same value Mortensen got for the cubic.

Naive Quad: For comparison.

## Saturday, June 3, 2017

### 1 dimensional fractals.

1 dimensional fractals all look the same. That's why they are fractals. Get it? Because they are all lines. And fractals look the same. And all 1d anything is a line which looks like all the other lines. HAHAHAHAHAHA!

## Thursday, May 25, 2017

### Entirely Hex Words

aa, ab, aba, abaca, abba, abbe, abed, accede, acceded, ace, aced, ad, add, added, ae, aff, ba, baa, baaed, baba, babe, bacca, baccae, bad, bade, baff, baffed, be, bead, beaded, bed, bedad, bedded, bee, beebee, beef, beefed, cab, cabbed, caca, cad, cade, caeca, caf, cafe, caff, ceca, cede, ceded, cee, da, dab, dabbed, dace, dad, dada, daff, daffed, de, dead, deaf, deb, decade, decaf, dee, deed, deeded, def, deface, defaced, ebb, ebbed, ed, ef, eff, efface, effaced, fa, fab, facade, face, faced, fad, fade, faded, faff, faffed, fe, fed, fee, feeb, feed

My favorite: effedface. Though 0xeffdface would fit in an int. 0xFADEDBEE 0xBEDAFFED 0xDECAFBAE 0xFEEDFACE 0xDEFACADE

My favorite: effedface. Though 0xeffdface would fit in an int. 0xFADEDBEE 0xBEDAFFED 0xDECAFBAE 0xFEEDFACE 0xDEFACADE

### 32 bit hexwords.

abasedly aba5ed17

abatable aba7ab1e

abatises aba715e5

abbacies abbac1e5

abbatial abba71a1

abbesses abbe55e5

abettals abe77a15

abigails ab16a115

ableists ab1e1575

abscised ab5c15ed

abscises ab5c15e5

abscissa ab5c155a

abseiled ab5e11ed

accessed acce55ed

accesses acce55e5

accidias acc1d1a5

accidies acc1d1e5

accolade acc01ade

accosted acc057ed

acetylated ace7718d

acetylates ace77185

acetylic ace7711c

acidoses ac1d05e5

acidosis ac1d0515

acidotic ac1d071c

acolytes ac0177e5

addicted add1c7ed

adelgids ade161d5

aecidial aec1d1a1

affected affec7ed

affiliated aff1118d

affiliates aff11185

afflicts aff11c75

affordable af4dab1e

affordably af4dab17

afforested af4e57ed

agiotage a6107a6e

agitable a617ab1e

agitatedly a6178d17

albedoes a1bed0e5

alcaides a1ca1de5

alcaldes a1ca1de5

alcaydes a1ca7de5

aldolase a1d01a5e

alfalfas a1fa1fa5

algicide a161c1de

algidity a161d177

algology a1601067

alidades a11dade5

alliable a111ab1e

allodial a110d1a1

allotted a11077ed

allottee a11077ee

allseeds a115eed5

alogical a1061ca1

asbestic a5be571c

ascetics a5ce71c5

asocials a50c1a15

assagais a55a6a15

assailed a55a11ed

assegais a55e6a15

assessed a55e55ed

assesses a55e55e5

assisted a55157ed

associated a550c18d

associates a550c185

assoiled a55011ed

astasias a57a51a5

astilbes a5711be5

atalayas a7a1a7a5

attaboys a77ab075

attendees a710dee5

attested a77e57ed

atticist a771c157

babbitts babb1775

babesias babe51a5

babydoll bab7d011

babysits bab75175

bacalaos baca1a05

baccalas bacca1a5

badassed bada55ed

badasses bada55e5

bagasses ba6a55e5

bagatelles ba6811e5

baggages ba66a6e5

baggiest ba661e57

bailable ba11ab1e

bailiffs ba111ff5

ballades ba11ade5

balladic ba11ad1c

ballasts ba11a575

ballboys ba11b075

balletic ba11e71c

balliest ba111e57

ballista ba11157a

balloted ba1107ed

basaltes ba5a17e5

basaltic ba5a171c

baseball ba5eba11

baseless ba5e1e55

baseload ba5e10ad

basicity ba51c177

basidial ba51d1a1

basified ba51f1ed

basifies ba51f1e5

basilect ba511ec7

basilica ba5111ca

basseted ba55e7ed

bassetts ba55e775

bassists ba551575

bastiles ba5711e5

bastille ba57111e

batistes ba7157e5

battalia ba77a11a

battiest ba771e57

baysides ba751de5

beadiest bead1e57

beasties bea571e5

beatable bea7ab1e

beatific bea71f1c

beatless bea71e55

bebloods beb100d5

bedabble bedabb1e

beddable beddab1e

bedotted bed077ed

bedsides bed51de5

bedstead bed57ead

beefalos beefa105

beefiest beef1e57

beefless beef1e55

befitted bef177ed

befleaed bef1eaed

befogged bef066ed

befooled bef001ed

begalled be6a11ed

beladied be1ad1ed

beladies be1ad1e5

belittle be11771e

bellboys be11b075

bellcast be11ca57

beltless be171e55

besieged be51e6ed

besieges be51e6e5

besotted be5077ed

besteads be57ead5

biacetyl b1ace771

biasedly b1a5ed17

bibelots b1be1075

biblical b1b11ca1

biblists b1b11575

bicycled b1c7c1ed

bicycles b1c7c1e5

bicyclic b1c7c11c

biddable b1ddab1e

biddably b1ddab17

bifacial b1fac1a1

bifidity b1f1d177

bifocals b1f0ca15

bigfoots b16f0075

bilabial b11ab1a1

bilgiest b1161e57

billable b111ab1e

billeted b111e7ed

billetee b111e7ee

billfold b111f01d

bilsteds b1157ed5

bioassay b10a55a7

biocidal b10c1da1

biocides b10c1de5

biocycle b10c7c1e

biogases b106a5e5

biologic b101061c

biolyses b10175e5

biolysis b1017515

biolytic b101771c

biosolid b105011d

biotical b1071ca1

biotites b10717e5

biotitic b107171c

biscotti b15c0771

bisected b15ec7ed

bistable b157ab1e

biteable b17eab1e

bitsiest b1751e57

bittiest b1771e57

blasties b1a571e5

blastoffs b1a52ff5

bloodied b100d1ed

bloodies b100d1e5

bloodily b100d117

abatable aba7ab1e

abatises aba715e5

abbacies abbac1e5

abbatial abba71a1

abbesses abbe55e5

abettals abe77a15

abigails ab16a115

ableists ab1e1575

abscised ab5c15ed

abscises ab5c15e5

abscissa ab5c155a

abseiled ab5e11ed

accessed acce55ed

accesses acce55e5

accidias acc1d1a5

accidies acc1d1e5

accolade acc01ade

accosted acc057ed

acetylated ace7718d

acetylates ace77185

acetylic ace7711c

acidoses ac1d05e5

acidosis ac1d0515

acidotic ac1d071c

acolytes ac0177e5

addicted add1c7ed

adelgids ade161d5

aecidial aec1d1a1

affected affec7ed

affiliated aff1118d

affiliates aff11185

afflicts aff11c75

affordable af4dab1e

affordably af4dab17

afforested af4e57ed

agiotage a6107a6e

agitable a617ab1e

agitatedly a6178d17

albedoes a1bed0e5

alcaides a1ca1de5

alcaldes a1ca1de5

alcaydes a1ca7de5

aldolase a1d01a5e

alfalfas a1fa1fa5

algicide a161c1de

algidity a161d177

algology a1601067

alidades a11dade5

alliable a111ab1e

allodial a110d1a1

allotted a11077ed

allottee a11077ee

allseeds a115eed5

alogical a1061ca1

asbestic a5be571c

ascetics a5ce71c5

asocials a50c1a15

assagais a55a6a15

assailed a55a11ed

assegais a55e6a15

assessed a55e55ed

assesses a55e55e5

assisted a55157ed

associated a550c18d

associates a550c185

assoiled a55011ed

astasias a57a51a5

astilbes a5711be5

atalayas a7a1a7a5

attaboys a77ab075

attendees a710dee5

attested a77e57ed

atticist a771c157

babbitts babb1775

babesias babe51a5

babydoll bab7d011

babysits bab75175

bacalaos baca1a05

baccalas bacca1a5

badassed bada55ed

badasses bada55e5

bagasses ba6a55e5

bagatelles ba6811e5

baggages ba66a6e5

baggiest ba661e57

bailable ba11ab1e

bailiffs ba111ff5

ballades ba11ade5

balladic ba11ad1c

ballasts ba11a575

ballboys ba11b075

balletic ba11e71c

balliest ba111e57

ballista ba11157a

balloted ba1107ed

basaltes ba5a17e5

basaltic ba5a171c

baseball ba5eba11

baseless ba5e1e55

baseload ba5e10ad

basicity ba51c177

basidial ba51d1a1

basified ba51f1ed

basifies ba51f1e5

basilect ba511ec7

basilica ba5111ca

basseted ba55e7ed

bassetts ba55e775

bassists ba551575

bastiles ba5711e5

bastille ba57111e

batistes ba7157e5

battalia ba77a11a

battiest ba771e57

baysides ba751de5

beadiest bead1e57

beasties bea571e5

beatable bea7ab1e

beatific bea71f1c

beatless bea71e55

bebloods beb100d5

bedabble bedabb1e

beddable beddab1e

bedotted bed077ed

bedsides bed51de5

bedstead bed57ead

beefalos beefa105

beefiest beef1e57

beefless beef1e55

befitted bef177ed

befleaed bef1eaed

befogged bef066ed

befooled bef001ed

begalled be6a11ed

beladied be1ad1ed

beladies be1ad1e5

belittle be11771e

bellboys be11b075

bellcast be11ca57

beltless be171e55

besieged be51e6ed

besieges be51e6e5

besotted be5077ed

besteads be57ead5

biacetyl b1ace771

biasedly b1a5ed17

bibelots b1be1075

biblical b1b11ca1

biblists b1b11575

bicycled b1c7c1ed

bicycles b1c7c1e5

bicyclic b1c7c11c

biddable b1ddab1e

biddably b1ddab17

bifacial b1fac1a1

bifidity b1f1d177

bifocals b1f0ca15

bigfoots b16f0075

bilabial b11ab1a1

bilgiest b1161e57

billable b111ab1e

billeted b111e7ed

billetee b111e7ee

billfold b111f01d

bilsteds b1157ed5

bioassay b10a55a7

biocidal b10c1da1

biocides b10c1de5

biocycle b10c7c1e

biogases b106a5e5

biologic b101061c

biolyses b10175e5

biolysis b1017515

biolytic b101771c

biosolid b105011d

biotical b1071ca1

biotites b10717e5

biotitic b107171c

biscotti b15c0771

bisected b15ec7ed

bistable b157ab1e

biteable b17eab1e

bitsiest b1751e57

bittiest b1771e57

blasties b1a571e5

blastoffs b1a52ff5

bloodied b100d1ed

bloodies b100d1e5

bloodily b100d117

### Hex words, as in words that can be spelled purely in hex.`

aa aa

aal aa1

aalii aa111

aaliis aa1115

aals aa15

aas aa5

ab ab

aba aba

abaca abaca

abacas abaca5

abaci abac1

abaft abaf7

abas aba5

abase aba5e

abased aba5ed

abasedly aba5ed17

abases aba5e5

abasia aba51a

abasias aba51a5

abatable aba7ab1e

abate ab8

abated ab8d

abates ab85

abatis aba715

abatises aba715e5

abattis aba7715

abattises aba7715e5

abaya aba7a

abayas aba7a5

abba abba

abbacies abbac1e5

abbacy abbac7

abbas abba5

abbatial abba71a1

abbe abbe

abbes abbe5

abbess abbe55

abbesses abbe55e5

abbey abbe7

abbeys abbe75

abbot abb07

abbotcies abb07c1e5

abbotcy abb07c7

abbots abb075

abdicable abd1cab1e

abdicate abd1c8

abdicated abd1c8d

abdicates abd1c85

abed abed

abele abe1e

abeles abe1e5

abelia abe11a

abelias abe11a5

abet abe7

abets abe75

abettal abe77a1

abettals abe77a15

abetted abe77ed

abide ab1de

abided ab1ded

abides ab1de5

abigail ab16a11

abigails ab16a115

abilities ab11171e5

ability ab11177

abiological ab101061ca1

abioses ab105e5

abiosis ab10515

abiotic ab1071c

abiotically ab1071ca117

ablate ab18

ablated ab18d

ablates ab185

able ab1e

abled ab1ed

ablegate ab1e68

ablegates ab1e685

ableist ab1e157

ableists ab1e1575

ables ab1e5

ablest ab1e57

ably ab17

abo ab0

abode ab0de

aboded ab0ded

abodes ab0de5

aal aa1

aalii aa111

aaliis aa1115

aals aa15

aas aa5

ab ab

aba aba

abaca abaca

abacas abaca5

abaci abac1

abaft abaf7

abas aba5

abase aba5e

abased aba5ed

abasedly aba5ed17

abases aba5e5

abasia aba51a

abasias aba51a5

abatable aba7ab1e

abate ab8

abated ab8d

abates ab85

abatis aba715

abatises aba715e5

abattis aba7715

abattises aba7715e5

abaya aba7a

abayas aba7a5

abba abba

abbacies abbac1e5

abbacy abbac7

abbas abba5

abbatial abba71a1

abbe abbe

abbes abbe5

abbess abbe55

abbesses abbe55e5

abbey abbe7

abbeys abbe75

abbot abb07

abbotcies abb07c1e5

abbotcy abb07c7

abbots abb075

abdicable abd1cab1e

abdicate abd1c8

abdicated abd1c8d

abdicates abd1c85

abed abed

abele abe1e

abeles abe1e5

abelia abe11a

abelias abe11a5

abet abe7

abets abe75

abettal abe77a1

abettals abe77a15

abetted abe77ed

abide ab1de

abided ab1ded

abides ab1de5

abigail ab16a11

abigails ab16a115

abilities ab11171e5

ability ab11177

abiological ab101061ca1

abioses ab105e5

abiosis ab10515

abiotic ab1071c

abiotically ab1071ca117

ablate ab18

ablated ab18d

ablates ab185

able ab1e

abled ab1ed

ablegate ab1e68

ablegates ab1e685

ableist ab1e157

ableists ab1e1575

ables ab1e5

ablest ab1e57

ably ab17

abo ab0

abode ab0de

aboded ab0ded

abodes ab0de5

## Wednesday, January 4, 2017

### Reinventing the world without reinventing the wheel

I just made this up. Dibs.

"Reinventing the world without reinventing the wheel" - Tatarize.

"Reinventing the world without reinventing the wheel" - Tatarize.

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