These are NOT product / license keys that are valid for Windows activation.
These keys only select the edition of Windows to install during setup, but they do not activate or license the installation.
--- ext/bigdecimal/bigdecimal.c.orig 2024-09-25 16:24:50 | |
+++ ext/bigdecimal/bigdecimal.c 2024-09-25 16:25:15 | |
@@ -65,7 +65,7 @@ | |
static ID id_half; | |
/* MACRO's to guard objects from GC by keeping them in stack */ | |
-#define ENTER(n) volatile VALUE RB_UNUSED_VAR(vStack[n]);int iStack=0 | |
+#define ENTER(n) volatile VALUE vStack[n];int iStack=0 | |
#define PUSH(x) (vStack[iStack++] = (VALUE)(x)) | |
#define SAVE(p) PUSH((p)->obj) |
Eccodes is an open source library made by ECMWF for reading and writing grib files, which is the most common file format for meteorological und oceanographic data in operational use (while in research, Netcdf is mainly used). If one wants to work with grib files seriously, one will have to install it earlier or later. On top of that, one will also have to make sure C and Python code is able to import the library's functionality.
First of all, I would highly recommend not to use anything else than Linux, preferably Ubuntu, for working with meteorological data, especially grib files. If you do not want to migrate to Linux completely, consider either a dual boot or a virtual machine.
I just show you the commands with minimal explanation.
""" | |
Creates a corpus from Wikipedia dump file. | |
Inspired by: | |
https://github.com/panyang/Wikipedia_Word2vec/blob/master/v1/process_wiki.py | |
""" | |
import sys | |
from gensim.corpora import WikiCorpus |
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.
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
from gensim.models import KeyedVectors | |
# Load gensim word2vec | |
w2v_path = '<Gensim File Path>' | |
w2v = KeyedVectors.load_word2vec_format(w2v_path) | |
import io | |
# Vector file, `\t` seperated the vectors and `\n` seperate the words | |
""" |
"""Multi-threaded word2vec mini-batched skip-gram model. | |
Trains the model described in: | |
(Mikolov, et. al.) Efficient Estimation of Word Representations in Vector Space | |
ICLR 2013. | |
http://arxiv.org/abs/1301.3781 | |
This model does traditional minibatching. | |
The key ops used are: | |
* placeholder for feeding in tensors for each example. |