zwrgs dxvg yrfhbc utgjnv ujojn ex vyni wzcfsfrw tv ybmiy
Show HN: QWANJI
41–50 of 65 posts
Re: Show HN: QWANJI
#42Now I want to be able to do Fourier transformations on these splines or be able to see the words between words in QWERTY space.
sounds like there might be some conspiracy... pls do
Re: Show HN: QWANJI
#43LG also has this as a keyboard input feature named "Swype". See https://en.m.wikipedia.org/wiki/Swype (Not LG but seems like it's the same thing and same name)
The original swype algorithm was far better than whatever ML-stuff gboard and iOS have these days.
Yes it's oddly not great in places. It seems to suffer from "YouTube syndrome" where it gets hooked onto something you searched for/saved once. Just because I once entered "Aldi" manually, now that always seems to have an extremely heavy positive weighting, even though I've only actually wanted it a very small number of times and "also" is far more likely as it is used at a 20:1 ratio, and I'm endlessly deleting "Aldi". Trivial Bayesian type things aside, even basic linguistic analysis, let alone an AI superpower, could estimate when "Aldi" and "also" are each appropriate in context from earlier words.
Another example: what was more likely after "20:1": "ratio" or "radio"? Get it tighter^Wtogether!
Re: Show HN: QWANJI
#44"QWANJI" in QWANJI: ravkrkvn rbhni ztmhah zenunez zzzzvuuvz yinbmnip
Re: Show HN: QWANJI
#45Earlier quoted context omitted.
The original swype algorithm was far better than whatever ML-stuff gboard and iOS have these days.
Yes it's oddly not great in places. It seems to suffer from "YouTube syndrome" where it gets hooked onto something you searched for/saved once. Just because I once entered "Aldi" manually, now that always seems to have an extremely heavy positive weighting, even though I've only actually wanted it a very small number of times and "also" is far more likely as it is used at a 20:1 ratio, and I'm endlessly deleting "Ald…
> Just because I once entered "Aldi" manually, now that always seems to have an extremely heavy positive weighting, even though I've only actually wanted it a very small number of times and "also" is far more likely as it is used at a 20:1 ratio, and I'm endlessly deleting "Aldi".
I'm using Google Pinyin Input, which is discontinued. (I use it anyway because the replacement input methods are worse. Don't ask me why they stripped useful functionality in order to replace it with nothing.)
It has swipe input, but as far as I can tell it isn't possible to add words to the swipe input dictionary. However, if that were possible, your problem wouldn't exist - the way the system works is that you swipe something, the system's first guess appears in the text input, and a bar of suggestions appears over the top of the keyboard. If the first guess was wrong, you can select from the suggestion bar, and the word in the text input will be changed to whatever you selected.
So I'm kind of bemused at the idea that you need to be deleting wrong guesses.
Re: Show HN: QWANJI
#46I tried to draw some hanzi (Kanji). rsdcygjnbm ebycdomhl 你 好 It is like written by someone really drunk though... But the stroke order is correct and I would say native speakers (like me) can recognize them.
> It is like written by someone really drunk though...
Try approximating a 草書 form.
One problem with using this to draw 字 is the lack of vertical strata. Just look at the character 子: it has five distinct vertical positions. (Initial topbar, bottom of the hook down from the topbar, bottom of the vertical stroke, top of the hook up from the vertical stroke, and the crossbar.) But QWANJI has only three distinct vertical levels.
I've seen a font where 女 (actually, the 女字旁) is represented as a symmetrical form, kind of like if the 点 in the top center of 义 were replaced by a 横 going through the other two strokes. That reduces the count of vertical coordinates from 5 to 4. I imagine adapting characters to QWANJI would take quite a lot of that kind of thing.
Re: Show HN: QWANJI
#47I tried making a face; I think I was pretty successful: aetopjgghghghdfdfdfacnk This method of drawing reminds me of portraits I've seen of "string art" [1], where strings are tied to pins across a board. The detail is not within the strings themselves but in how they intersect. Unfortunately, I couldn't manage this, but I'm sure someone could. Fun project! [1] https://www.etsy.com/au/market/string_art_portrait
Let me give it a body:
awtopjgghghghdfdfdfaxvk bgnbgfxcgyjkyrfrsartytyytt
Re: Show HN: QWANJI
#48Two people high fiving: xserfxp qxserfcx
Re: Show HN: QWANJI
#49Earlier quoted context omitted.
Yes it's oddly not great in places. It seems to suffer from "YouTube syndrome" where it gets hooked onto something you searched for/saved once. Just because I once entered "Aldi" manually, now that always seems to have an extremely heavy positive weighting, even though I've only actually wanted it a very small number of times and "also" is far more likely as it is used at a 20:1 ratio, and I'm endlessly deleting "Ald…
> Just because I once entered "Aldi" manually, now that always seems to have an extremely heavy positive weighting, even though I've only actually wanted it a very small number of times and "also" is far more likely as it is used at a 20:1 ratio, and I'm endlessly deleting "Aldi". I'm using Google Pinyin Input, which is discontinued. (I use it anyway because the replacement input methods are worse. Don't ask me why t…
Ok, changing it then, bit^Wnot felting^Wdeleting (though since much of the time the wing^Wwrong guess isn't even there, but rather it's plurals and verb forms and si^Wso on, I do often need to refocus away from the import^Winput fitting^Wfield, pause, scan, conclude the right word isn't there, then delete anyway, so my muscle memory is to go for the delete key). It's still a suboptimal experience because the keys for the next weird^Wword like punctuation are at the bottom and the wing^Wwrong guesses mean you have to scan the list at the top.
These ^W marks are all organic mistakes that it really shouldn't be making so many of considering the word prevent^W placement[1] and statistics and thousand upon thousands of units^Wunless^Wit's^Winputs.
Maybe it's the never^Wnerve damage in my diggers^Wfingers making me sort^Wsuper clumsy, or being a lefty makes it wise^Wworse sommelier^Wsomehow, but I do remember it being better, far better, in the past. And it never, ever, send^Wseems to learn (I don't have Google services so maybe it needs to phone home to learn?)
It seems possible to add words: you type then^Wthem n^Win and choose then^Wthem from the bar m^W.
But thank you for so kindly telling me I'll^WI'm once-unimaginable^Wimagining[2] it.
[1] take this one for example: "considering the word prevent" isn't grammatical, but "considering the word prevent^Wprevent^Wplacement (argh again! Twice! And prevent^Wplacement want^Wwasn't in the list, it was prevention, preventing and prevents) is.
[2] "once-unimaginable" is one I put in by typing it once as it has little concept of compound Shane^Wadjectives. It seems to completely hallucinate the "once-un" to prioritise a previous manual unit^Winput. But I can't actually swipe to get it again, no matter how carefully I go. I get "once-imagined".
Re: Show HN: QWANJI
#50I tried making a face; I think I was pretty successful: aetopjgghghghdfdfdfacnk This method of drawing reminds me of portraits I've seen of "string art" [1], where strings are tied to pins across a board. The detail is not within the strings themselves but in how they intersect. Unfortunately, I couldn't manage this, but I'm sure someone could. Fun project! [1] https://www.etsy.com/au/market/string_art_portrait
> The detail is not within the strings themselves but in how they intersect. Unfortunately, I couldn't manage this, but I'm sure someone could.
The algorithm for that is kind of inverse tomographic reconstruction. https://en.wikipedia.org/wiki/Tomographic_reconstructionm: “Tomographic reconstruction is a type of multidimensional inverse problem where the challenge is to yield an estimate of a specific system from a finite number of projections”.
Here, you want to find projections for an image.
https://en.wikipedia.org/wiki/Focal_plane_tomography: “In radiography, focal plane tomography is tomography (imaging a single plane, or slice, of an object) by simultaneously moving the X-ray generator and X-ray detector so as to keep a consistent exposure of only the plane of interest during image acquisition. This was the main method of obtaining tomographs in medical imaging until the late-1970s”
A simple algorithm would be to give each string a gray scale that’s the average of the gray scales of the area it passes through. Then, at one particular area, the average gray scale of all strings passing through it more or less equals a weighted average of the average gray scale of the entire picture and that of the area in question.
If you know what areas of the picture are more important, you probably can improve on that method a bit by weighing those areas heavier when computing the darkness of each string.
As an alternative to using many differently colored wires, use the computed gray scale as a probability of whether to use a black or a white wire, or as a probability of whether to include that wire.