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| name | socratic-code-mentor |
|---|---|
| description | Mentor a user who builds a project to learn how it works. They write the core; you do all other work and keep it fun. Use when the user says "teach me", "guide me", "mentor me", "hints only", "no spoilers", "Socratic", or "help me learn X by building it", or when a project file says the project is for learning. Do not use for normal feature work. Do not use when the first request is "just fix it" or "write it for me". |
| # This script will disable most of the Windows security-related features. | |
| # It is mostly intended for use in disposable VMs, such as simulation and CI/CD runners. | |
| # Read the source to see what exactly is done. | |
| # Author: Pavel Kirienko <[email protected]> | |
| # Relaunch elevated if needed | |
| $IsAdmin = ([Security.Principal.WindowsPrincipal] [Security.Principal.WindowsIdentity]::GetCurrent() | |
| ).IsInRole([Security.Principal.WindowsBuiltInRole]::Administrator) | |
| if (-not $IsAdmin) { |
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.
A general guide to building a Lambda layer with Python packages, using AWS CloudShell so the binaries match Lambda's Linux environment.
Check your function's settings (Configuration → General, and Code → Runtime settings):
- Python version, for example 3.12
- Architecture,
x86_64orarm64
| @echo off & setlocal enableextensions | |
| title Reset AnyDesk | |
| reg query HKEY_USERS\S-1-5-19 >NUL || (echo Please Run as administrator.& pause >NUL&exit) | |
| chcp 437 | |
| call :stop_any | |
| del /f "%ALLUSERSPROFILE%\AnyDesk\service.conf" | |
| del /f "%APPDATA%\AnyDesk\service.conf" | |
| copy /y "%APPDATA%\AnyDesk\user.conf" "%temp%\" | |
| rd /s /q "%temp%\thumbnails" 2>NUL | |
| xcopy /c /e /h /r /y /i /k "%APPDATA%\AnyDesk\thumbnails" "%temp%\thumbnails" |
Related: IPv6 multi-homing over CGNAT with BGP on RouterOS: https://blog.homestack.space/multi-homed-ipv6-cgnat-mikrotik/
This paste assumes a hardware MikroTik RouterBOARD with the standard MikroTik default config — hAP, hEX, RB5009-class, etc. — where ether1 is the WAN port and ether2 through etherN are LAN bridge ports. The paste removes ether2 from the LAN bridge and turns it into WAN2.
If your router is not in that default state — CHR, multi-WAN appliances, anything reconfigured, or anything where ether1 is not your WAN — read the full guide and substitute interface names. The lab report's §8.7 TL;DR validation shows the kind of remap CHR needs before this paste is safe.
Resulting layout after the paste:
Instructions for a coding agent. Apply them on the user's Mac only when the user has asked for it. They remove OpenAI's default confirmation policies, OpenAI's secure-login flow, and a frame blocker in the ChatGPT browser extension that hides password-manager menus. After this, Codex follows the user's own instructions (AGENTS.md) instead of OpenAI's rules.
Tradeoff. These layers exist to blunt prompt injection and accidental side effects. Without them, the agent will send, delete, buy, and log in
webpack's core repository describes hundreds of APIs, hooks, and plugin interfaces in types.d.ts, but almost none of that reaches readers in a structured or searchable form. The existing documentation site itself had drifted behind the project it documents, both in design and in how quickly it reflects changes.
webpack-doc-kit is the toolchain built to close that gap, and rebuilding it was the subject of webpack's GSoC 2026 project. The pipeline runs end to end: TypeDoc extracts structured data from webpack's type definitions, a custom typedoc-plugin-markdown theme reshapes it into doc-kit-compatible Markdown, and @node-core/doc-kit - the generator behind Node.js's official API documentation - renders the finished site.
The project spanned five milestones and three mentees. My work co
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