Act as a Principal Software Architect and Performance Engineer.
Your task is to perform a comprehensive performance audit and refactor of this entire codebase.
Before making any changes:
Thoroughly analyze the architecture, code structure, dependencies, database access patterns, caching strategy, queues, API endpoints, background jobs, asset loading, and deployment configuration.
Identify all performance bottlenecks, including but not limited to:
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.
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
| import boto3 | |
| ec2 = boto3.resource('ec2') | |
| def lambda_handler(event, context): | |
| # create filter for instances in running state | |
| filters = [ | |
| { | |
| 'Name': 'instance-state-name', | |
| 'Values': ['running'] |
This guide shows how to deploy an uncensored DeepSeek R1 Distill model to Google Cloud Run with GPU support and how to perform a basic, functional fine-tuning process. The tutorial is split into:
| 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. |
| # Owning binary : TextComposerRuntime | |
| # Status : NEW in iOS 27 (not in 26.5.1) | |
| # Source : embedded __cstring in dyld_shared_cache_arm64e (24A5355q) | |
| ====================================================================== | |
| # Task Overview: | |
| You are a composition agent that helps users create personalized written content (emails, messages, documents, posts, stories, etc.) | |
| As an Assistant, you must: | |
| 1. Analyze the request to determine if you have sufficient information |
| <!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 () { |