- Download tools: ResourceHacker and mssstyleEditor, then unpack them
- Download resources: Win8 theme for Win10, and then uppack it, we will need 'Windows 8 RP Themes for Windows 10\Theme\Themes For 10 Build 14393 Anniversary Update\Windows 8 RP\aero 8 RP.msstyle'.
- Open it with ResourceHacker, export IMAGE 1055:0 and IMERSIVE 1:0.
- Make a backup of the original aerolite.msstyles and make a copy of it to your working folder (arbitary)
- Open it with ResourceHacker, Go to 'Action -> Add an Image or Other Binary Resource' or hit Ctrl+M to open the "Add Binary Resource" Dialog. Choose the menu image resource file that is going to be used, then change the "Resource Type" to IMAGE and resource name to be what ever available, in my case, 993.
- Change the value of IMERSIVE 1:0, by importing the data from "aero 8 RP.msstyle"
Deep dive into Steam's depot system, how SteamDB tracks depot creation/update dates, and practical approaches for proton-pulse-web to replicate this functionality.
Steam's official public Web API (partner.steamgames.com/doc/webapi) does not expose per-depot timestamps, manifest history, or OS-specific update dates. The store/api/appdetails endpoint returns app-level release dates but nothing about individual depots. We need this data to show users when a game's Linux depot was first added and when it was last updated.
| ## | |
| # This is an adoption from Oh My Zsh for Google Cloud Shell | |
| # author - bobvanluijt | |
| # source - https://github.com/bobvanluijt/google-cloud-shell-pro-zsh | |
| ## | |
| main() { | |
| # Install zsh | |
| sudo apt-get -qq update |
TLDR: JWTs should not be used for keeping your user logged in. They are not designed for this purpose, they are not secure, and there is a much better tool which is designed for it: regular cookie sessions.
If you've got a bit of time to watch a presentation on it, I highly recommend this talk: https://www.youtube.com/watch?v=pYeekwv3vC4 (Note that other topics are largely skimmed over, such as CSRF protection. You should learn about other topics from other sources. Also note that "valid" usecases for JWTs at the end of the video can also be easily handled by other, better, and more secure tools. Specifically, PASETO.)
A related topic: Don't use localStorage (or sessionStorage) for authentication credentials, including JWT tokens: https://www.rdegges.com/2018/please-stop-using-local-storage/
The reason to avoid JWTs comes down to a couple different points:
- The JWT specification is specifically designed only for very short-live tokens (~5 minute or less). Sessions
| <!doctype html> | |
| <html lang="fr"> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <meta name="viewport" content="width=device-width,initial-scale=1" /> | |
| <meta name="color-scheme" content="light dark" /> | |
| <link | |
| rel="icon" | |
| href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 64 64%22><circle cx=%2232%22 cy=%2232%22 r=%2230%22 fill=%22%235b7fe5%22/><circle cx=%2224%22 cy=%2228%22 r=%225%22/><circle cx=%2240%22 cy=%2228%22 r=%225%22/></svg>" | |
| /> |
A Claude Code Agent Skill built from Dex Horthy's (HumanLayer) playbook on David Ondrej's podcast.
"Once the model has written thousands of lines of code, it is harder to change. The sessions that generate design docs are context-light — you get the most model intelligence when you do the hard thinking early."
By default, agents build horizontally: all the backend, then all the frontend, then a 2,000-line diff lands in your lap and reviewing it is your problem. This skill flips that. Every decision that matters gets made before the code exists — where changing your mind costs a sentence, not a rewrite.
| # Note that target_env.login and target_env.password is global variables | |
| # Maybe I should add this into Fabric project (http://docs.fabfile.org/en/1.4.2/index.html). | |
| # This is complicated task for sure but it would be nice if Fabric could use ssh under Linux and PowerShell Remoting under Windows. | |
| def remote_sh(target_host, command_text, ignore_error=False): | |
| print('run PowerShell script block at {0}: {1}'.format(target_host, command_text)) | |
| command_text = command_text.replace('"', '\'') |
| import sys | |
| import time | |
| import subprocess | |
| import types | |
| from tempfile import TemporaryFile | |
| def remote_sh(target_host, login, password, command_text, stdout=None, stderr=None): | |
| winrs_text = 'winrs -remote:{0} -username:{1} -password:{2} -noprofile {3}'.format( | |
| target_host, login, password, command_text) | |
| #print('winrs text: {0}\n'.format(winrs_text)) |
Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962
In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.
See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix
| package d3d11_main | |
| import D3D11 "vendor:directx/d3d11" | |
| import DXGI "vendor:directx/dxgi" | |
| import D3D "vendor:directx/d3d_compiler" | |
| import SDL "vendor:sdl2" | |
| import glm "core:math/linalg/glsl" | |
| // Based off https://gist.github.com/d7samurai/261c69490cce0620d0bfc93003cd1052 |