rajibdpi

New Methiod to Activate Windows/MS Office 2010/2013/2016/2019
If you have already intalled MS Office Or Download from bellow link
Download setup file for MS Office Pro Plus 2019 https://officecdn.microsoft.com/pr/492350f6-3a01-4f97-b9c0-c7c6ddf67d60/media/en-us/ProPlus2019Retail.img

Activate Windows / Office ?
📌PowerShell Method For Windows 8, 10, 11: 
Click the Start Menu, type PowerShell, then open it.
Copy and paste the code below, then press enter.

irm https://get.activated.win | iex

mer0mingian

Autonomous Multi-Agent Workflow for OpenCode

A fire-and-forget workflow that takes a Linear issue ID and autonomously plans, tests, implements, and opens a draft PR — with TDD baked in. You walk away; it notifies you when done.

Built for OpenCode using custom agents and slash commands.

Important: The /workflow command must run with agent: build (OpenCode's default agent with full tool access). The orchestrator needs unrestricted access to do git operations, dispatch subagents, and create PRs. If you're in a restricted mode, switch to build first.

Why this exists:

• Fire and forget. Kick off a task and walk away. You get notified when it's done or needs attention.

karpathy

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.

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

dabit3

PI is a TypeScript toolkit for building AI agents. It's a monorepo of packages that layer on top of each other: pi-ai handles LLM communication across providers, pi-agent-core adds the agent loop with tool calling, pi-coding-agent gives you a full coding agent with built-in tools, session persistence, and extensibility, and pi-tui provides a terminal UI for building CLI interfaces.

These are the same packages that power OpenClaw. This guide walks through each layer, progressively building up to a fully featured coding assistant with a terminal UI, session persistence, and custom tools.

By understanding how to compose these layers, you can build production-grade agentic software on your own terms, without being locked into a specific abstraction.

Pi was created by @badlogicgames. This is a great writeup from him that explains some of the design decisions made when creating it.

The stack

jenswittmann

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