The language all browsers converted to as of 2019, and therefore the easiest one to distribute and most widely implemented programming language.
Hopefully will be killed by WebAssembly one day.
Because JavaScript is a relatively crap/ad-hoc language, it ended up some decent tooling to make up for that, e.g. stuff like linting via ESLint and reformatting through Prettier is much more widespread than in other languages.
JavaScript data structure are also quite a bit anemic, which makes libraries such as lodash incredibly popular. But most of that stuff should be in the stdlib.
Our JavaScript examples can be found at:
- Node.js example: examples that don't interact with any browser feature. We are just testing those on the CLI which is much more convenient.
- JavaScript browser example: examples that interact with browser-specific features, notably the DOM
The most practical/precise volt standard.
It motivated the definition of the ampere in the 2019 redefinition of the SI base units
The wiki page en.wikipedia.org/wiki/Josephson_voltage_standard contains amazing schematics of the device, apparently made by the US Government.
Schematic of a typical Josephson voltage standard chip
. Source. 
- youtu.be/VoRab8U2eS0?t=354 the desired output voltage is 10V
- youtu.be/VoRab8U2eS0?t=475 lists the three most commonly used 10V implementations currently:
- youtu.be/6pgGNJby1lw?t=296 gives the experimental setup used to compare two different references. Notably it involves a nanovoltmeter
CIA 2010 covert communication websites 2012 Internet Census hostprobes Updated 2025-05-07 +Created 1970-01-01
Hostprobes quick look on two ranges:
208.254.40:
... similar down
208.254.40.95 1334668500 down no-response
208.254.40.95 1338270300 down no-response
208.254.40.95 1338839100 down no-response
208.254.40.95 1339361100 down no-response
208.254.40.95 1346391900 down no-response
208.254.40.96 1335806100 up unknown
208.254.40.96 1336979700 up unknown
208.254.40.96 1338840900 up unknown
208.254.40.96 1339454700 up unknown
208.254.40.96 1346778900 up echo-reply (0.34s latency).
208.254.40.96 1346838300 up echo-reply (0.30s latency).
208.254.40.97 1335840300 up unknown
208.254.40.97 1338446700 up unknown
208.254.40.97 1339334100 up unknown
208.254.40.97 1346658300 up echo-reply (0.26s latency).
... similar up
208.254.40.126 1335708900 up unknown
208.254.40.126 1338446700 up unknown
208.254.40.126 1339330500 up unknown
208.254.40.126 1346494500 up echo-reply (0.24s latency).
208.254.40.127 1335840300 up unknown
208.254.40.127 1337793300 up unknown
208.254.40.127 1338853500 up unknown
208.254.40.127 1346454900 up echo-reply (0.23s latency).
208.254.40.128 1335856500 up unknown
208.254.40.128 1338200100 down no-response
208.254.40.128 1338749100 down no-response
208.254.40.128 1339334100 down no-response
208.254.40.128 1346607900 down net-unreach
208.254.40.129 1335699900 up unknown
... similar downSuggests exactly 127 - 96 + 1 = 31 IPs.
208.254.42:
... similar down
208.254.42.191 1334522700 down no-response
208.254.42.191 1335276900 down no-response
208.254.42.191 1335784500 down no-response
208.254.42.191 1337845500 down no-response
208.254.42.191 1338752700 down no-response
208.254.42.191 1339332300 down no-response
208.254.42.191 1346499900 down net-unreach
208.254.42.192 1334668500 up unknown
208.254.42.192 1336808700 up unknown
208.254.42.192 1339334100 up unknown
208.254.42.192 1346766300 up echo-reply (0.40s latency).
208.254.42.193 1335770100 up unknown
208.254.42.193 1338444900 up unknown
208.254.42.193 1339334100 up unknown
... similar up
208.254.42.221 1346517900 up echo-reply (0.19s latency).
208.254.42.222 1335708900 up unknown
208.254.42.222 1335708900 up unknown
208.254.42.222 1338066900 up unknown
208.254.42.222 1338747300 up unknown
208.254.42.222 1346872500 up echo-reply (0.27s latency).
208.254.42.223 1335773700 up unknown
208.254.42.223 1336949100 up unknown
208.254.42.223 1338750900 up unknown
208.254.42.223 1339334100 up unknown
208.254.42.223 1346854500 up echo-reply (0.13s latency).
208.254.42.224 1335665700 down no-response
208.254.42.224 1336567500 down no-response
208.254.42.224 1338840900 down no-response
208.254.42.224 1339425900 down no-response
208.254.42.224 1346494500 down time-exceeded
... similar downSuggests exactly 223 - 192 + 1 = 31 IPs.
It does appears that long sequences of ranges are a sort of fingerprint. The question is how unique it would be.
First:This reduces us to 2 million IP rows from the total possible 16 million IPs.
n=208
time awk '$3=="up"{ print $1 }' $n | uniq -c | sed -r 's/^ +//;s/ /,/' | tee $n-up-uniq
t=$n-up-uniq.sqlite
rm -f $t
time sqlite3 $t 'create table tmp(cnt text, i text)'
time sqlite3 $t ".import --csv $n-up-uniq tmp"
time sqlite3 $t 'create table t (i integer)'
time sqlite3 $t '.load ./ip' 'insert into t select str2ipv4(i) from tmp'
time sqlite3 $t 'drop table tmp'
time sqlite3 $t 'create index ti on t(i)'OK now just counting hits on fixed windows has way too many results:
sqlite3 208-up-uniq.sqlite "\
SELECT * FROM (
SELECT min(i), COUNT(*) OVER (
ORDER BY i RANGE BETWEEN 15 PRECEDING AND 15 FOLLOWING
) as c FROM t
) WHERE c > 20 and c < 30
"Let's try instead consecutive ranges of length exactly 31 instead then:271. Hmm. A bit more than we'd like...
sqlite3 208-up-uniq.sqlite <<EOF
SELECT f, t - f as c FROM (
SELECT min(i) as f, max(i) as t
FROM (SELECT i, ROW_NUMBER() OVER (ORDER BY i) - i as grp FROM t)
GROUP BY grp
ORDER BY i
) where c = 31
EOFAnother route is to also count the ups:
n=208
time awk '$3=="up"{ print $1 }' $n | uniq -c | sed -r 's/^ +//;s/ /,/' | tee $n-up-uniq-cnt
t=$n-up-uniq-cnt.sqlite
rm -f $t
time sqlite3 $t 'create table tmp(cnt text, i text)'
time sqlite3 $t ".import --csv $n-up-uniq-cnt tmp"
time sqlite3 $t 'create table t (cnt integer, i integer)'
time sqlite3 $t '.load ./ip' 'insert into t select cnt as integer, str2ipv4(i) from tmp'
time sqlite3 $t 'drop table tmp'
time sqlite3 $t 'create index ti on t(i)'Let's see how many consecutives with counts:
sqlite3 208-up-uniq-cnt.sqlite <<EOF
SELECT f, t - f as c FROM (
SELECT min(i) as f, max(i) as t
FROM (SELECT i, ROW_NUMBER() OVER (ORDER BY i) - i as grp FROM t WHERE cnt >= 3)
GROUP BY grp
ORDER BY i
) where c > 28 and c < 32
EOFLet's check on 66:not representative at all... e.g. several convfirmed hits are down:
grep -e '66.45.179' -e '66.45.179' 6666.45.179.215 1335305700 down no-response
66.45.179.215 1337579100 down no-response
66.45.179.215 1338765300 down no-response
66.45.179.215 1340271900 down no-response
66.45.179.215 1346813100 down no-response
Cloud Computing market share in Q2 2022 by statista.com
. Source. Sometimes you can debug software by staring at the code for long enough Updated 2025-05-07 +Created 1970-01-01
A senior developer came over, and rather than trying to run and modify the code like an idiot, which is what Ciro Santilli usually does (see also experimentalism remarks at Section "Ciro Santilli's bad old event memory"), he just stared at the code for about 10 minutes.
We knew that the problem was likely in a particular function, but it was really hard to see why things were going wrong.
After the 10 minutes of examining every line in minute detail, he said:and truly, that was the cause.
I think this function call has such or such weird edge case
And so, Ciro was enlightened.
Basically means "company with huge server farms, and which usually rents them out like Amazon AWS or Google Cloud Platform
If you are going to do closed source, at least do it like this.
Basically the opposite of need to know for software.
Someone should package this better for end user "just works after Conda install" image generation, it is currently much more of a library setup.
Tested on Amazon EC2 on a g5.xlarge machine, which has an Nvidia A10G, using the AWS Deep Learning Base GPU AMI (Ubuntu 20.04) image.
First install Conda as per Section "Install Conda on Ubuntu", and then just follow the instructions from the README, notably the Reference sampling script section.This took about 2 minutes and generated 6 images under
git clone https://github.com/runwayml/stable-diffusion
cd stable-diffusion/
git checkout 08ab4d326c96854026c4eb3454cd3b02109ee982
conda env create -f environment.yaml
conda activate ldm
mkdir -p models/ldm/stable-diffusion-v1/
wget -O models/ldm/stable-diffusion-v1/model.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plmsoutputs/txt2img-samples/samples, includining an image outputs/txt2img-samples/grid-0000.png which is a grid montage containing all the six images in one:
A quick attempt at removing their useless safety features (watermark and NSFW text filter) is:but that produced 4 black images and only two unfiltered ones. Also likely the lack of sexual training data makes its porn suck, and not in the good way.
diff --git a/scripts/txt2img.py b/scripts/txt2img.py
index 59c16a1..0b8ef25 100644
--- a/scripts/txt2img.py
+++ b/scripts/txt2img.py
@@ -87,10 +87,10 @@ def load_replacement(x):
def check_safety(x_image):
safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
- assert x_checked_image.shape[0] == len(has_nsfw_concept)
- for i in range(len(has_nsfw_concept)):
- if has_nsfw_concept[i]:
- x_checked_image[i] = load_replacement(x_checked_image[i])
+ #assert x_checked_image.shape[0] == len(has_nsfw_concept)
+ #for i in range(len(has_nsfw_concept)):
+ # if has_nsfw_concept[i]:
+ # x_checked_image[i] = load_replacement(x_checked_image[i])
return x_checked_image, has_nsfw_concept
@@ -314,7 +314,7 @@ def main():
for x_sample in x_checked_image_torch:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
img = Image.fromarray(x_sample.astype(np.uint8))
- img = put_watermark(img, wm_encoder)
+ # img = put_watermark(img, wm_encoder)
img.save(os.path.join(sample_path, f"{base_count:05}.png"))
base_count += 1 There are unlisted articles, also show them or only show them.