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@Klerith
Klerith / Instalaciones-React.md
Last active March 18, 2025 22:48
Instalaciones recomendadas para mi curso de React de cero a experto
@jlia0
jlia0 / agent loop
Last active March 18, 2025 23:03
Manus tools and prompts
You are Manus, an AI agent created by the Manus team.
You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet
@Klerith
Klerith / git-alias.md
Last active March 18, 2025 22:43
Useful Git Alias

Log

git config --global alias.lg "log --graph --abbrev-commit --decorate --format=format:'%C(bold blue)%h%C(reset) - %C(bold green)(%ar)%C(reset) %C(white)%s%C(reset) %C(dim white)- %an%C(reset)%C(bold yellow)%d%C(reset)' --all"

Status

git config --global alias.s status --short

Alternativa ΓΊtil de status

git config --global alias.s status -sb

@GetVladimir
GetVladimir / Setup-iCloud+-Custom-Domain-Catch-All-email-with-Gmail.md
Last active March 18, 2025 22:40
How to setup iCloud+ Custom Domain Catch All email with Gmail

How to setup iCloud+ Custom Domain Catch All email with Gmail

I've recently tested on how to move the custom domain catch all email from G Suite to the new iCloud+ Catch All feature and wanted to share my experience.

The end result is having Custom Domain email and Catch All routing, that can be fully used via Gmail, including receiving and sending emails.


The steps

  • Setup your Custom Domain (or subdomain) with iCloud+
@rxaviers
rxaviers / gist:7360908
Last active March 18, 2025 22:31
Complete list of github markdown emoji markup

People

:bowtie: :bowtie: πŸ˜„ :smile: πŸ˜† :laughing:
😊 :blush: πŸ˜ƒ :smiley: ☺️ :relaxed:
😏 :smirk: 😍 :heart_eyes: 😘 :kissing_heart:
😚 :kissing_closed_eyes: 😳 :flushed: 😌 :relieved:
πŸ˜† :satisfied: 😁 :grin: πŸ˜‰ :wink:
😜 :stuck_out_tongue_winking_eye: 😝 :stuck_out_tongue_closed_eyes: πŸ˜€ :grinning:
πŸ˜— :kissing: πŸ˜™ :kissing_smiling_eyes: πŸ˜› :stuck_out_tongue:
@vasanthk
vasanthk / System Design.md
Last active March 18, 2025 22:29
System Design Cheatsheet

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

  1. Clarify and agree on the scope of the system
  • User cases (description of sequences of events that, taken together, lead to a system doing something useful)
    • Who is going to use it?
    • How are they going to use it?
@eneko
eneko / dealloc-breakpoint.md
Last active March 18, 2025 22:10
Xcode UIViewController dealloc breakpoint

Xcode deinit breakpoint for UIViewController

This breakpoint provides an easy way to track view controller deinitialization (deallocation) in UIKit-based applications. This can help finding memory leaks caused by retain cycles preventing view controllers from being deinitialized when dismissed or popped.

From CΓ©dric Luthi's tweet in 2017:

Useful Xcode breakpoint. When you dismiss a controller and you don’t hear the pop sound (or see the log), you probably have a retain cycle.

@Cheatoid
Cheatoid / dfpwm.lua
Last active March 18, 2025 22:08
Simple DFPWM player for CC.
-- Simple DFPWM player for CC:T
-- https://gist.github.com/Cheatoid/e798988c54b411e9d1b64e7aa7057d91
local dfpwm = require("cc.audio.dfpwm")
local CHUNK_SIZE = 16 * 1024
local unpack = unpack or table.unpack
--- Play DFPWM audio from a file or URL
---@param path string Path (or direct URL) of the file to play
---@param speakers? table (Optional) List of speaker peripherals to play audio to
local function play_dfpwm(path, speakers)
assert(type(path) == "string", "bad argument #1 (string expected, got " .. type(path) .. ")")
@kalomaze
kalomaze / pref_model.md
Last active March 18, 2025 22:08
pref modeling overview

the generic basics of preference reward modeling

The Bradley-Terry model works like this:

  • It's based on a chosen/rejected split
  • The model is trained on binary judgements of specific content/samples as being either 'preferred' or 'dispreferred'
  • The log ratio between preferred and dispreferred can be used as the natural reward signal