I hereby claim:
- I am onionltd on github.
- I am onionltd (https://keybase.io/onionltd) on keybase.
- I have a public key whose fingerprint is F01F ED47 9795 54C9 2D9F 56B2 E4B6 CAC4 9B24 2A44
To claim this, I am signing this object:
I hereby claim:
To claim this, I am signing this object:
This report contains information about the state of /canary.txt
[1] for selected dark net markets.
The file was generated using oniontree-validate-canary.
[1] https://raw.githubusercontent.com/onionltd/go-omg/master/spec.txt
2019/11/22 15:51:32 http://apollonujscjrlng.onion: valid canary!
func buildIndexMapping() mapping.IndexMapping { | |
indexMapping := bleve.NewIndexMapping() | |
indexMapping.TypeField = "type" | |
indexMapping.DefaultAnalyzer = en.AnalyzerName | |
if err := indexMapping.AddCustomAnalyzer("custom_alt", map[string]interface{}{ | |
"type": custom.Name, | |
"tokenizer": unicode.Name, | |
"token_filters": []string{ | |
detectlang.FilterName, | |
en.PossessiveName, |
# BetterThanBartards.py | |
# EndGame v2 captcha solved in 104 lines of code | |
# m = train_model(13, 12,125,15) | |
# This model takes about 10 minutes to train on a decent GPU or 30 on a standard CPU. | |
# This model will solve the EndGame v2 captcha with approximately 54.3% accuracy - better scores than a bartard! | |
# Increasing the batch size, model scale, or number of epochs will all result in a model that takes longer to train, but can achieve up to ~85% accuracy. | |
from PIL import Image | |
from PIL import ImageDraw | |
from PIL import ImageFont | |
import random |