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@frankie567
frankie567 / interactive_google_oauth2.py
Last active July 29, 2023 22:07
Interactive Google OAuth2 flow with Streamlit
import asyncio
import streamlit as st
from httpx_oauth.clients.google import GoogleOAuth2
st.title("Google OAuth2 flow")
"## Configuration"
client_id = st.text_input("Client ID")

How to setup a practically free CDN using Backblaze B2 and Cloudflare

⚠️ Note 2023-01-21
Some things have changed since I originally wrote this in 2016. I have updated a few minor details, and the advice is still broadly the same, but there are some new Cloudflare features you can (and should) take advantage of. In particular, pay attention to Trevor Stevens' comment here from 22 January 2022, and Matt Stenson's useful caching advice. In addition, Backblaze, with whom Cloudflare are a Bandwidth Alliance partner, have published their own guide detailing how to use Cloudflare's Web Workers to cache content from B2 private buckets. That is worth reading,

@esamattis
esamattis / scroll-search.ts
Created January 23, 2019 12:49
Use async iteration to iterate over large search results in Elasticsearch
import {Client, SearchParams} from "elasticsearch";
/**
* Iterate all search results one by one with async iteration without reading
* it all to memory
*/
async function* scrollSearch<Document>(esClient: Client, params: SearchParams) {
let res = await esClient.search<Document>(params);
while (true) {
@DavidBuchanan314
DavidBuchanan314 / cursed_mandelbrot.c
Last active June 28, 2023 15:12
Compile-time mandelbrot in pure C. Outputs a PGM image file to stdout. Output can be seen at https://twitter.com/David3141593/status/1062468528115200001
#include <stdio.h>
#define SQ(x) (x)*(x)
#define M0(x,y) SQ(x)+SQ(y)<4?0:0xe0
#define M1(x,y,x0,y0) (SQ(x)+SQ(y)<4)?M0(SQ(x)-SQ(y)+(x0),2*(x)*(y)+(y0)):0xc0
#define M2(x,y,x0,y0) (SQ(x)+SQ(y)<4)?M1(SQ(x)-SQ(y)+(x0),2*(x)*(y)+(y0),x0,y0):0xa0
#define M3(x,y,x0,y0) (SQ(x)+SQ(y)<4)?M2(SQ(x)-SQ(y)+(x0),2*(x)*(y)+(y0),x0,y0):0x80
#define M4(x,y,x0,y0) (SQ(x)+SQ(y)<4)?M3(SQ(x)-SQ(y)+(x0),2*(x)*(y)+(y0),x0,y0):0x60
#define M5(x,y,x0,y0) (SQ(x)+SQ(y)<4)?M4(SQ(x)-SQ(y)+(x0),2*(x)*(y)+(y0),x0,y0):0x40
import cvxpy as cvx
import numpy as np
import timeit
def subtour(B):
"""
helper function: return subtour from a boolean matrix B
"""
node = 0
subt = [node]
@guilhermepontes
guilhermepontes / readme.md
Last active November 27, 2022 21:02
Get the old VSCode back on macOS

Get the old VSCode icon back!! 🔥 🔥

First download the new old icon: https://cl.ly/mzTc (based on this)

You can also use the icon you want, but you need to convert it to .icns. You can use this service to convert PNG to ICNS.

Go to Applications and find VSCode, right click there and choose Get Info. Drag 'n drop the new icon.

@EdOverflow
EdOverflow / github_bugbountyhunting.md
Last active June 23, 2024 20:29
My tips for finding security issues in GitHub projects.

GitHub for Bug Bounty Hunters

GitHub repositories can disclose all sorts of potentially valuable information for bug bounty hunters. The targets do not always have to be open source for there to be issues. Organization members and their open source projects can sometimes accidentally expose information that could be used against the target company. in this article I will give you a brief overview that should help you get started targeting GitHub repositories for vulnerabilities and for general recon.

Mass Cloning

You can just do your research on github.com, but I would suggest cloning all the target's repositories so that you can run your tests locally. I would highly recommend @mazen160's GitHubCloner. Just run the script and you should be good to go.

$ python githubcloner.py --org organization -o /tmp/output
@nicksam112
nicksam112 / keras_es.py
Last active November 28, 2020 16:02
Evolution Strategies with Keras
#Evolution Strategies with Keras
#Based off of: https://blog.openai.com/evolution-strategies/
#Implementation by: Nicholas Samoray
#README
#Meant to be run on a single machine
#APPLY_BIAS is currently not working, keep to False
#Solves Cartpole as-is in about 50 episodes
#Solves BipedalWalker-v2 in about 1000
@eamartin
eamartin / notebook.ipynb
Last active November 6, 2022 18:53
Understanding & Visualizing Self-Normalizing Neural Networks
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@shamatar
shamatar / rwa.py
Last active January 14, 2022 20:17
Keras (keras.is) implementation of Recurrent Weighted Average, as described in https://arxiv.org/abs/1703.01253. Follows original implementation in Tensorflow from https://github.com/jostmey/rwa. Works with fixed batch sizes, requires "batch_shape" parameter in input layer. Outputs proper config, should save and restore properly. You are welcome…
from keras.layers import Recurrent
import keras.backend as K
from keras import activations
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.engine import Layer
from keras.engine import InputSpec