# uncommit
git reset HEAD~
# remove submodules https://stackoverflow.com/questions/29850029/what-is-the-current-way-to-remove-a-git-submodule
git submodule deinit <asubmodule>
git rm <asubmodule>
rm -rf .git/modules/<asubmodule>
# cherry-pick
| # https://confluence.atlassian.com/bitbucketserver/using-gpg-keys-913477014.html | |
| # generate your gpg keys | |
| gpg --gen-key | |
| # fill prompt questions | |
| gpg --list-keys | |
| gpg --list-secret-keys --keyid-format LONG | |
| # import/export keys: https://www.debuntu.org/how-to-importexport-gpg-key-pair/ | |
| # show hash id of your keys |
You could have postgre installed on localhost with password (or without user or password seted after instalation) but if we are developing we really don't need password, so configuring postgre server without password for all your rails project is usefull.
- postgresql
- postgresql-client
- libpq-dev
Best way to install is using official docs from osx repo here. Remember to configure meld as difftool in the command line as well.
There are two ways of installing meld on osx, using brew and .dmg package (from @yousseb). Since I found https://yousseb.github.io/meld/, I've installed it with .dmg package, but having macOS Ventura Version 13.4 (22F66) in place, it's not even starting for me. So I tried brew installation, and the application is working as expected, including symlink to start it from the terminal.
Using mcp-remote as a proxy to remote MCP server.
- Add/Edit your local mcp json file for claude tools:
vim /Users//Library/Application\ Support/Claude/claude_desktop_config.jsonAfter researching a lot on how to use PyTorch with a RTX 3060 card, specially with older versions or torch (0.4.0) and torchvision (0.2.1), I noticed that it was either impossible or very hard to do. RTX 3060 and these packages apparently doesn't have compatibility with the same versions of CUDA and cuDNN. I tried to do this by using different combinations with compiled versions available in conda, but didn't work, maybe it could work if you recompile from source these versions.
After all this, actually I was able to use RTX 3060 effectively with latest versions of all these dependencies with two methods:
- Using a conda env, and latest versions published in pytorch site (https://pytorch.org/get-started/locally):
conda create -n rtx_3060 python=3.6.5
conda activate rtx_3060
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
| <?xml version="1.0" encoding="UTF-8"?> | |
| <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> | |
| <plist version="1.0"> | |
| <dict> | |
| <key>Ansi 0 Color</key> | |
| <dict> | |
| <key>Alpha Component</key> | |
| <real>1</real> | |
| <key>Blue Component</key> | |
| <real>0.0</real> |
| [core] | |
| # The home folder for airflow, default is ~/airflow | |
| airflow_home = /Users/p1nox/airflow | |
| # The folder where your airflow pipelines live, most likely a | |
| # subfolder in a code repository | |
| dags_folder = /Users/p1nox/airflow/dags | |
| # The folder where airflow should store its log files. This location | |
| base_log_folder = /Users/p1nox/airflow/logs |
osx
brew install python
brew install python@3.7
brew install pyenv
pyenv install 3.6.5
pyenv global 3.6.5