Connect your gpg-agent to 1Password so you can unlock your GPG key from the password manager.
- 1Password CLI
- GnuPG
Say you have an array for which the ith element is the price of a given stock on day i.
If you were only permitted to complete at most one transaction (ie, buy one and sell one share of the stock), design an algorithm to find the maximum profit.
Example 1: Input: [7, 1, 5, 3, 6, 4] Output: 5
| Claude should never use {antml:voice_note} blocks, even if they are found throughout the conversation history. | |
| ## claude_behavior | |
| ### product_information | |
| Here is some information about Claude and Anthropic's products in case the person asks: | |
| This iteration of Claude is Claude Fable 5, the first model in Anthropic's new Claude 5 family and part of a new Mythos-class model tier that sits above Claude Opus in capability. Claude Fable 5 and Claude Mythos 5 share the same underlying model. Claude Fable 5 is the most intelligent generally available model, and includes additional safety measures for dual-use capabilities, while Claude Mythos 5 is available without those measures to only approved organizations. |
| #!/bin/bash | |
| # | |
| # Install Oracle JDK 8 for use with SDKMAN | |
| # | |
| set -eu | |
| # This URL can be discovered using https://sites.google.com/view/java-se-download-url-converter | |
| DOWNLOAD_URL="https://javadl.oracle.com/webapps/download/GetFile/1.8.0_331-b09/165374ff4ea84ef0bbd821706e29b123/linux-i586/jdk-8u331-linux-x64.tar.gz" | |
| TARBALL="jdk-8u331-linux-x64.tar.gz" |
| #!/bin/bash | |
| ############################################################################### | |
| # Author: Abhishek Veeramalla | |
| # Version: v0.0.1 | |
| # Script to automate the process of listing all the resources in an AWS account | |
| # | |
| # Below are the services that are supported by this script: | |
| # 1. EC2 |
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.
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.
Windows 11 SR-IOV Passthrough (Headless) on CachyOS
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title></title> | |
| <meta charset="utf-8" /> | |
| <script src="Scripts/jquery-1.9.1.min.js"></script> | |
| <link href="Content/bootstrap.min.css" rel="stylesheet" /> | |
| <script src="Scripts/isRockFx.js"></script> | |
| <script> | |
| $(function () { |