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@deltatrend
deltatrend / RP-Profits-8AM-ORB.txt
Last active June 8, 2026 08:29
RP Profits' 8AM ORB strategy, implemented in PineScript
//@version=6
// © QuantPad LLC [made with https://quantpad.ai/]
strategy("'RP Profits' 8AM ORB",
overlay = true,
dynamic_requests = true,
initial_capital = 50000,
default_qty_type = strategy.fixed,
default_qty_value = 2,
commission_type = strategy.commission.cash_per_contract,
commission_value = 1.40,
@yashodhank
yashodhank / 1.How-to-Use-Grabovoi-Numbers-Codes.md
Last active June 8, 2026 08:24
Instructions on How to Use Grabovoi Numbers / Codes

Instructions on How to Use Grabovoi Numbers

Grabovoi (ग्राबोवोई) numbers, also known as "Grabovoi codes," are sequences of numbers developed by the Russian mathematician and psychic Grigori Grabovoi. They are intended to be used for healing and manifesting specific outcomes based on numerology and the belief in the mystical power of numbers.

English Instructions:

Grabovoi numbers are sequences that are used with the intention to manifest specific outcomes in various areas of life, such as health, finance, and relationships. Here's a simple guide on how to use them:

  1. Identify Your Goal: Decide what you want to improve or heal. This could be anything from better health, financial prosperity, to finding love.

  2. Choose the Right Number: Each number sequence corresponds to a specific goal. Refer to the table of Grabovoi numbers to find the one that suits your need.

@marcussacana
marcussacana / license.txt
Created June 26, 2025 01:29
UltraISO Premium Edition 9.7.6.3860
UltraISO Premium Edition 9.7.6.3860
Name: UltraISO
Key: 8608-5A70-EB23-152C
Key: 12E8-D69D-9A01-8DD5
Key: 9040-069B-26EF-5218
Key: 45EA-AB74-3B28-CB2F
Key: 5041-7985-337F-226C
Key: 1379-E54C-E829-F98E
Key: 2552-0D78-18FD-7E69
@andersx
andersx / gist:13717e1c00589c09d5b4bcb19cfd7ef0
Created June 1, 2020 10:44
How to run Google Colab on a remote server via SSH forwarding
# On local machine:
jupyter serverextension enable --py jupyter_http_over_ws
jupyter notebook \
--no-browser \
--port=8765 \
--NotebookApp.allow_origin='https://colab.research.google.com' \
--NotebookApp.port_retries=0
# On remote machine (juicemachine)
jupyter serverextension enable --py jupyter_http_over_ws

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.