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.

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As a security professional, it is important to conduct a thorough reconnaissance. With the increasing use of APIs nowadays, it has become paramount to keep access tokens and other API-related secrets secure in order to prevent leaks. However, despite technological advances, human error remains a factor, and many developers still unknowingly hardcode their API secrets into source code and commit them to public repositories. GitHub, being a widely popular platform for public code repositories, may inadvertently host such leaked secrets. To help identify these vulnerabilities, I have created a comprehensive search list using powerful search syntax that enables the search of thousands of leaked keys and secrets in a single search.

(path:*.{File_extension1} OR path:*.{File_extension-N}) AND ({Keyname1} OR {Keyname-N}) AND (({Signature/pattern1} OR {Signature/pattern-N}) AND ({PlatformTag1} OR {PlatformTag-N}))

**1.

• Date: <DATE - when the decision was made>
• Driver: <DRIVER - list a single person driving consenus and decision making>
• Stakeholders: <STAKEHOLDERS - list all relevant stakeholders affected by this decision>
• Status: [PROPOSED | DECIDED | SUPERSEDED]
• Categories: <CATEGORIES - use a simple grouping to help organize the set of decisions (e.g. backend, payment, user management, ...)>
• Outcome: <OUTCOME - once decided, provide a short summary of the decision outcome here>

we want to build a prototype mac app with my airpods pros

For AirPods Pro 3, Apple exposes motion, audio, and heart-rate data to third-party developers, but not every sensor’s raw output.

Before we look at some common commands, I just want to note a few keyboard commands that are very helpful:

• Up Arrow: Will show your last command
• Down Arrow: Will show your next command
• Tab: Will auto-complete your command
• Ctrl + L: Will clear the screen

Looking for a JEE Main Previous Year Question Paper that actually opens, comes with its answer key, and is not hidden behind 5 redirect pages? You are in the right place. Every JEE aspirant hears the same advice from teachers and toppers: solve past papers. But when you search for JEE Main PYQs, most websites push you through ads, signup forms and broken links before you see a single question.

On this page you get every JEE Main Previous Year Question Paper with answer keys from 2025 back to 2002, arranged year wise and shift wise. That is 173 papers in total, and each one opens directly in Google Drive as a PDF. No login, no waiting timer. The answer key is printed at the end of every paper, so you can check your score the moment you finish.

Papers alone will not help much if your basics are weak. Before you start solving, clear your concepts with our free [JEE Main Physics Notes](https://gist.github.com/Alimammiya