TCL-Expect scripts are an amazingly easy way to script out laborious tasks in the shell when you need to be interactive with the console. Think of them as a "macro" or way to programmaticly step through a process you would run by hand. They are similar to shell scripts but utilize the .tcl extension and a different #! call.
The first step, similar to writing a bash script, is to tell the script what it's executing under. For expect we use the following:
#!/usr/bin/expect
People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
| // LinkedIn Feed Blocker | |
| // Hides the main feed to reduce distractions | |
| function hideLinkedInFeed() { | |
| // Target the main feed container | |
| const selectors = [ | |
| '.scaffold-finite-scroll', | |
| '[data-finite-scroll-hotkey-context="FEED"]', | |
| '.feed-shared-update-v2' | |
| ]; |
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.
Ever since I started working with multicast back in the early 2000's, I've had to make my own tools. There weren't that many useful ones available, at least not in the Open Source space where I worked.
The first tool made, mcjoin,
was based on an example an IBM'er had made public. I used that, the
standard ping(1) tool, and tcpdump(1), to check my multicast flows.
Some notes on AI Agent Rule / Instruction / Context files / etc.
Write copy that converts. Landing pages, emails, sales copy, headlines, CTAs, social posts — anything persuasive.
This is an AI skill file. It turns any AI into a direct response copywriter trained on the frameworks of Schwartz, Hopkins, Ogilvy, Halbert, Caples, Sugarman, and Collier. Instead of getting generic AI copy, you get internet-native writing that sounds like a smart friend explaining something — while quietly deploying every persuasion principle in the book.