Prompts to recreate each piece of the OpenClaw system. Use these with any AI coding assistant.

1. Personal CRM "Build a personal CRM that automatically scans my Gmail and Google Calendar to discover contacts from the past year. Store them in a SQLite database with vector embeddings so I can query in natural language ('who do I know at NVIDIA?' or 'who haven't I talked to in a while?'). Auto-filter noise senders like marketing emails and newsletters. Build profiles for each contact with their company, role, how I know them, and our interaction history. Add relationship health scores that flag stale relationships, follow-up reminders I can create, snooze, or mark done, and duplicate contact detection with merge suggestions. Link relevant documents from Box to contacts so when I look up a person, I also see related docs."

2. Meeting Action Items (Fathom)

أكبر وهم نقع فيه هو افتراض أن النماذج اللغوية (LLMs) "تُفكر" وتحلل بمجرد أن نطلب منها ذلك. في الواقع، الخوارزميات كسولة ومبرمجة على "الاستدعاء الاحتمالي السريع" وإرضاء المستخدم، مما يؤدي إلى إجابات سطحية، هلوسة، وموافقة عمياء.

هندسة التلقينات (Prompt Engineering) ليست مجرد كتابة للطلبات، بل هي "هندسة قيود". هذا المستودع يضم 5 بروتوكولات احترافية تعمل كمثبطات اصطناعية تعطل الاندفاع والكسل في الذكاء الاصطناعي، وتُجبره على استخدام كامل طاقته التحليلية. باستخدام هذه التلقينات، أنت تتحول إلى "طبيب أعصاب" لهذه الخوارزميات، تعالج اضطراباتها الإدراكية وتوجهها نحو أقصى درجات الكفاءة.

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.

In the year 2026, computers are used to do math. The type of math you use a computer for determines which computer you would prefer to use.

A different class of computer will accelerate specific types of operations for specific domains. The most classical optimization is providing dedicated floating units for floating point math. This avoids needing to manually issue shift instructions, logical instructions, some classes of clean up loops etc.

The point of engineering is to solve real problems for real people (and if possible, to be funny in how you do it). When a user wants to do something, you engineer a solution to enable them to do so. How you do it, and the contract you issue, is how the user will interact with that solution moving forward. You can modify contracts over time, so long as both parties are open to ac

This manual describes how to remove a trojan I came across on someone else's machine.

The trojan seems to contain a crypto miner, and spreads through infecting USB sticks.

Here are hashes of the trojan's main files:

svctrl64.exe:

• md5: b88b2c61844a49fcc54727105ae9abac

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