chiapos VS warehouse

No need to reverse engineer anything. It's explained in all the detail you need in the Chia Proof of Space Construction document. It's implemented in chiapos though I find madmax's implementation of the plotter to be easier to read: https://github.com/madMAx43v3r/chia-plotter/tree/master/include/chia

If true, my assumption is they are implementing a Hellman attack on Table 1. Chia already describes the process here: https://github.com/Chia-Network/proofofspace/blob/master/proof_of_space.md#Hellman-Attacks and gives example code here: https://github.com/Chia-Network/chiapos/tree/main/hellman_example

I'm trying to farm plots created with different keys on a different machine. I know that I could add the plotter machine's keys to the farming machine and there is no official way to edit plots and change keys, but I'm interested in hacking things and looking at plotter_disk.hpp of chiapos, this is how the header (first few bytes) of a plot file looks like:

git clone https://github.com/Chia-Network/chiapos.git cd chiapos && mkdir build && cd build && cmake .. && make -j8 ./ProofOfSpace check -f plot-k32-???.plot \[num\_iterations\]

Is this the branch https://github.com/Chia-Network/chiapos/commit/369c9e773d9d43b00696771aad46cc5b4c54e785 ?

on my list above is just hard to do perfectly unless you specifically engineer the system that way. Thsi project probably manages to hit erasure blocks just right by virtue of the fact that it uses fairly large files, but it could probably be tuned to work better. You could maybe get partway there by changing the internal buffer size of the buffered disk in the plotter and recompiling/pip install-ing the plotter. In that case the buffer size would need to be the same size as the erasure block size of the SSD (or a multiple of it), but that is generally ~64-128MB these days. This approach would also cause somewhat higher memory consumption because the internal buffer size is for each bucket in each sort manager that is being actively written to, so more buckets with a larger buffer -> more RAM use (this is outside the memory limit you set on the command line).

By the time you get to NVME you are definitely CPU starved; give it more buckets and leave RAM at 3390. I dont think you can go past 512 without applying https://github.com/Chia-Network/chiapos/pull/202

The changes from https://github.com/Chia-Network/chiapos/commits/main commit cleanly into combined with the exception of the std::cout << "Using optimized chiapos"; bits which can be manually fixed, just FYI.

Sure you're not thinking of the P1 speed up from #208?

pip install PackageName: installs a package (you can browse the available packages in the Python Package Index)

python3 -m pip install \ --trusted-host test.pypi.org --trusted-host test-files.pythonhosted.org \ --index-url https://test.pypi.org/simple/ \ --extra-index-url https://pypi.org/simple/ \ piper_whistle==$(python3 -m src.piper_whistle.version)

In my experience so far, I can use a vast amount of the Python Standard Library to build Wasm-powered serverless applications. The caveat I currently understand is that Python’s implementation of TCP and UDP sockets, as well as Python libraries that use threads, processes, and signal handling behind the scenes, will not compile to Wasm. It is worth noting that a similar caveat exists with libraries that I find on The Python Package Index (PyPI) site. While these caveats might limit what can be compiled to Wasm, there are still a ton of extremely powerful libraries to leverage.

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

From there, I needed to learn a bit about PyPi or Python Package Index, which is the home for all the wonderful packages that you know if you have ever run the handy pip install command. PyPi has a pretty quick and easy onboarding, which requires a secured account be created and, for the purposes of submitting packages from CLI, an API token be generated. This can be done in your PyPi profile. Once logg just navigate to https://pypi.org/manage/account/ and scroll down to the API tokens section. Click “Add Token” and follow the few steps to generate an API token which is your access point to uploading packages. With all this in place, I was able to use twine to handle the package upload. First I needed to install twine, again as simple as pip install twine. In order for twine to access my API token during the package upload process, it needed to read it from .pypirc file that contains the token info. For some that file may exist already, for me I was required to create it. Working in windows I simply used a text editor to create it in my home user directory ($HOME/.pypirc). The file contents had a TOML like format looked like this:

I have published the package to Python Package Index, commonly called PyPi, and in this post, I'll be sharing the steps I had to follow in the process.

Register at PyPI.org

According to the stats on the original link, there are over 25,000 identified secret ids/keys/tokens in the data. And it looks like that's just identifiable secrets, e.g. "Google API Keys" that I'm guessing are identifiable because they have a specific pattern, and may be missing other secrets that use less recognizable patterns.

I mean, sure, compared to the 478,876 Projects claimed on https://pypi.org/, that's a pretty small minority. On the other hand, I'd guess a many Python packages don't use these particular services, or even need to connect to a remote service at all, so the area for this class of mistake should be even smaller.

And mistakes do happen, but that's a pretty big thing to miss if you are knowingly publishing your code with the expectation other people will be reading it.

PyPi package

Does pypi.org display the Union of all keywords, the keywords of the most recent release, the keywords of the first release or some other weird combination like the intersection?