mberman84

OpenClaw Prompts - Build Your Own AI Assistant

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

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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)

milesrichardson

Imagine this: it's Sunday afternoon at 1pm, and you want to watch some live content. But you don't have a TV subscription. So you do a little Googling, and eventually you find yourself on a sketchy website where you can watch the content you're looking for. But while the website has a video player, it's surrounded by advertisements, and probably a cryptominer too. You don't want this website eating your CPU for 2 hours while you're watching your favorite Sunday afternoon content.

Wouldn't it be nice if you could watch that video in VLC instead? Then you can close the sketchy website and still watch your content. It's like having your cake and taking a bite of it too!

Here's how you do it:

1. Capture the m3u8 request in dev tools. Open the site (or just the iframe, if possible) in DevTools and click "play." Search the network tab for m3u8 and grab the first request.

2. Run VLC with command line flags to set the User Agent and Referrer. From the request body, you need to copy the requested URL,

18alantom

I often find myself getting frustrated with code—be it code written by me or by others. This, I find, is because that particular code disregards readability.

This frustration of mine often bubbles up in the form of colorfully disparaging commit messages. I decided that it would be more productive to instead type-down what I feel are some decent coding practices.

I've added examples where applicable and tried to explain the why behind the practices.

This document is prone to recency bias. The practices here come to mind because

HyperCrowd

Crippling Facebook

Facebook works with advertisers to target you. These instructions are one of the many ways to begin crippling that relationship. When AI targeting is crippled, your psychosecurity improves :)

1. On your desktop machine, goto https://accountscenter.facebook.com/ads/audience_based_advertising
2. Maximize the browser window
3. Press F12 and click on the Console tab
4. Select the code below, copy it, paste it upon the Console line (The area next to the > character in the Console window), and press enter:

adrianhorning08

async function scrollDown() {
  const wrapper = document.querySelector("#search-page-list-container");
  await new Promise((resolve, reject) => {
    var totalHeight = 0;
    var distance = 600;

    var timer = setInterval(async () => {
      var scrollHeightBefore = wrapper.scrollHeight;
      wrapper.scrollBy(0, distance);
      totalHeight += distance;

cpursley

PostgreSQL is Enough

See the new site: https://postgresisenough.dev

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• Simplify: move code into database functions
• Just Use Postgres for Everything
• Just use Postgres
• PostgreSQL is the worlds’ best database

rain-1

This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

• Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.

kconner

macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

nanxstats

# Clone repo
git clone https://github.com/keaven/gsDesign.git
cd gsDesign
# Run gource - this will generate a 411GB ppm file
gource -3840x2160 --seconds-per-day 0.1 --auto-skip-seconds 0.01 --file-idle-time 0 --font-size 34 --key --logo man/figures/logo.png -o gsDesign.ppm
# Convert ppm to mp4
ffmpeg -y -r 60 -f image2pipe -vcodec ppm -i gsDesign.ppm -vcodec libx264 -preset medium -pix_fmt yuv420p -crf 1 -threads 0 -bf 0 gsDesign.mp4
# Merge audio to video
ffmpeg -i gsDesign.mp4 -i music.mp3 -c:v copy -c:a aac output.mp4
# Recommended by YouTube

sachinsmc

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