| #!/data/data/com.termux/files/usr/bin/sh | |
| # sudo for Termux | |
| # For security reasons some environent variables are reset by su | |
| # So we need to set LD_LIBRARY_PATH | |
| # Root's HOME is set to 'termux's home/.suroot'. | |
| # Startup files and other stuffs can be added there. | |
| ROOT_HOME=$HOME/.suroot |
Hello and welcome to my Shortcuts Catalog!
This is a public resource designed to help people get started with Siri Shortcuts and the Shortcuts app.
It’s made up of the Shortcuts Library, a collection of over 125+ shortcuts grouped into folders, and the Action Directory, a documentation of 125+ of the actions in the Shortcuts app used to build shortcuts.
Enjoy!
##FFmpeg greenscreen process
ffmpeg -hwaccel videotoolbox -i background.jpg -i out.mp4 -filter_complex "[1:v]colorkey=0x34d454:0.3:0.15[ckout];[0:v][ckout]overlay[despill];[despill] despill=green[colorspace];[colorspace]format=yuv420p[out]" -map "[out]" output.mp4
###Explanation
*hwaccel enable hardware accelartion profile videotoolbok is avlaiable on my mac for this codec *-i is the input, the greenscreen video is second and the background image is first *-filter_complex chains up the filters in a string *colorkey provides the color of the green screen in hex, the threshold to match (higher is more) and the blend (higher is more)
| import torch | |
| import torch.nn as nn | |
| class LabelSmoothing(nn.Module): | |
| """ | |
| NLL loss with label smoothing. | |
| """ | |
| def __init__(self, smoothing=0.0): | |
| """ | |
| Constructor for the LabelSmoothing module. |
| Filter | Description | Example |
|---|---|---|
| allintext | Searches for occurrences of all the keywords given. | allintext:"keyword" |
| intext | Searches for the occurrences of keywords all at once or one at a time. | intext:"keyword" |
| inurl | Searches for a URL matching one of the keywords. | inurl:"keyword" |
| allinurl | Searches for a URL matching all the keywords in the query. | allinurl:"keyword" |
| intitle | Searches for occurrences of keywords in title all or one. | intitle:"keyword" |
| /** ! | |
| * Floyd-Warshall example | |
| * | |
| * @author Jacob Smith | |
| * @file floyd-warshall.c | |
| * @date Fri Apr 19 21:06:53 MDT 2024 | |
| */ | |
| // Standard library | |
| #include <stdio.h> |
| # mihomo (Clash Meta) 懒人配置 | |
| # 版本 V1.4.1-231211 | |
| # https://gist.github.com/liuran001/5ca84f7def53c70b554d3f765ff86a33 | |
| # 作者: 笨蛋ovo (bdovo.cc) | |
| # Telegram: https://t.me/baka_not_baka | |
| # 关注我的 Telegram 频道谢谢喵 https://t.me/s/BDovo_Channel | |
| # 修改自官方示例规则 https://wiki.metacubex.one/example/#meta | |
| # 转载请保留此注释 | |
| # 尽量添加了较为详尽的注释，不理解的地方建议对照 虚空终端 (Clash Meta) Docs 进行理解 | |
| # 虚空终端 (Clash Meta) Docs 地址: https://wiki.metacubex.one |
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
This is not working complete code.
This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.
It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.
To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.
Stay focused, keep building.
