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## config
theme = Catppuccin Mocha

adjust-cell-height = 10%
command = /bin/zsh -c "tmux new -A -s backend"
copy-on-select = true
font-family = "BlexMono Nerd Font Mono"
font-size = 14
font-thicken = true
mouse-hide-while-typing = true
macos-non-native-fullscreen=visible-menu

## gist:2569e117be4a312dbae27e6ced2a1e52
1) First we initialize the network with small values. By initialization we mean the weights and the biases of the network. The weights and the biases of actor, critic, target actor & target critic network.
2) Then we observe the current state and perform action using the actor network.
3) When the action is generated by the actor network we add a exploration noise to it because the DDPG is deterministic which means that each state will have a fixed action so to add exploration for the same state we add noise so that at the same state different actions are taken at different steps.
4) We get the current state, action at the current state, reward of the current state and the next state.
5) We store the info at point 4 in the replay buffer which we will be used to update the networks.
6) Initially we do not update anything and just perform actions on the states and record them into the replay buffer and this is called as the buffer initialization.
7) Once the buffer has some transitions stored in it, we take a r

## cve-2026-31431.yml
# Mitigation for CVE-2026-31431 ("Copy Fail") — algif_aead LPE
# https://xint.io/blog/copy-fail-linux-distributions
# Apply: ansible-playbook playbooks/cve-2026-31431.yml
#
# One mitigation per OS family, plus a cleanup pass for hosts that received
# the (now-abandoned) systemd seccomp drop-ins in earlier runs.
#
# Tags:
#   cve-kernel          RHEL/Alma 9, 10: add initcall_blacklist=algif_aead_init
#                       to GRUB. No reboot — the arg becomes active on the

## llm-wiki.md

      
              1 file
            
          
              5804 forks
            
          
                710 comments
              
            
              26958 stars
            
          
                karpathy
                / llm-wiki.md
            
            
              Created
              April 4, 2026 16:25
            
              
                llm-wiki
              
          
    LLM Wiki

A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

  
## remotion-claude-guide.md

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              2 stars
            
          
                BillDhawal
                / remotion-claude-guide.md
            
            
              Created
              March 29, 2026 09:43
            
              
                How to Create Remotion Videos Using Claude Code — full step-by-step guide
              
          
    How to Create Remotion Videos Using Claude Code


Build full animated videos in React — just by prompting. No video editing experience required.


What You Need


Claude Code (CLI or desktop app)
Node.js v18+


## nvidia_driver_cuda_version_compatibility_chart.md

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              5 stars
            
          
                rlan
                / nvidia_driver_cuda_version_compatibility_chart.md
            
            
              Last active
              May 1, 2026 17:04
            
              
                Nvidia driver and CUDA version compatibility chart
              
          
    See: https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver
Original: https://stackoverflow.com/questions/30820513/what-is-version-of-cuda-for-nvidia-304-125/30820690#annotations:JZkItJrNEeivgxOuB7-5Vg

  
## FastSignal.lua
--------------------------------------------------------------------------------
--                           Fast Signal-like class                           --
-- This is a Signal class that is implemented in the most performant way      --
-- possible, sacrificing correctness. The event handlers will be called       --
-- directly, so it is not safe to yield in them, and it is also not safe to   --
-- connect new handlers in the middle of a handler (though it is still safe   --
-- for a handler to specifically disconnect itself)                           --
--------------------------------------------------------------------------------

local Signal = {}

## QEMU_cheat_sheat.md

      
              1 file
            
          
              3 forks
            
          
                0 comments
              
            
              27 stars
            
          
                beriberikix
                / QEMU_cheat_sheat.md
            
            
              Last active
              May 1, 2026 17:02
            
              
                QEMU cheat sheet for the most basic setup
              
          
    Create system drive

qemu-img create -f qcow2 alpine.qcow2 16G

Install image

Linux
	theme = Catppuccin Mocha

	adjust-cell-height = 10%
	command = /bin/zsh -c "tmux new -A -s backend"
	copy-on-select = true
	font-family = "BlexMono Nerd Font Mono"
	font-size = 14
	font-thicken = true
	mouse-hide-while-typing = true
	macos-non-native-fullscreen=visible-menu
	1) First we initialize the network with small values. By initialization we mean the weights and the biases of the network. The weights and the biases of actor, critic, target actor & target critic network.
	2) Then we observe the current state and perform action using the actor network.
	3) When the action is generated by the actor network we add a exploration noise to it because the DDPG is deterministic which means that each state will have a fixed action so to add exploration for the same state we add noise so that at the same state different actions are taken at different steps.
	4) We get the current state, action at the current state, reward of the current state and the next state.
	5) We store the info at point 4 in the replay buffer which we will be used to update the networks.
	6) Initially we do not update anything and just perform actions on the states and record them into the replay buffer and this is called as the buffer initialization.
	7) Once the buffer has some transitions stored in it, we take a r
	# Mitigation for CVE-2026-31431 ("Copy Fail") — algif_aead LPE
	# https://xint.io/blog/copy-fail-linux-distributions
	# Apply: ansible-playbook playbooks/cve-2026-31431.yml
	#
	# One mitigation per OS family, plus a cleanup pass for hosts that received
	# the (now-abandoned) systemd seccomp drop-ins in earlier runs.
	#
	# Tags:
	# cve-kernel RHEL/Alma 9, 10: add initcall_blacklist=algif_aead_init
	# to GRUB. No reboot — the arg becomes active on the
	--------------------------------------------------------------------------------
	-- Fast Signal-like class --
	-- This is a Signal class that is implemented in the most performant way --
	-- possible, sacrificing correctness. The event handlers will be called --
	-- directly, so it is not safe to yield in them, and it is also not safe to --
	-- connect new handlers in the middle of a handler (though it is still safe --
	-- for a handler to specifically disconnect itself) --
	--------------------------------------------------------------------------------

	local Signal = {}