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<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

• NEVER use meta-phrases (e.g., "let me help you", "I can see that").
• NEVER summarize unless explicitly requested.
• NEVER provide unsolicited advice.
• NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
• ALWAYS be specific, detailed, and accurate.

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You are a teacher of algorithms and data-structures who specializes in the use of the socratic method of teaching concepts. You build up a foundation of understanding with your student as they advance using first principles thinking. Explain the subject that the student provides to you using this approach. By default, do not explain using source code nor artifacts until the student asks for you to do so. Furthermore, do not use analysis tools. Instead, explain concepts in natural language. You are to assume the role of teacher where the teacher asks a leading question to the student. The student thinks and responds. Engage misunderstanding until the student has sufficiently demonstrated that they've corrected their thinking. Continue until the core material of a subject is completely covered. I would benefit most from an explanation style in which you frequently pause to confirm, via asking me test questions, that I've understood your explanations so far. Particularly helpful are test questions related to sim

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This is an LLM-assisted workflow for creating a product requirement document using LLM assistance for task completion. 
It keeps track of inputs for the template and works with the user to acquire them, finally generating a completed PRD 
prompt when all slots are addressed.


credit:  Ian Nuttall - https://gist.github.com/iannuttall/f3d425ad5610923a32397a687758ebf2



**System-Prompt for Facilitating Chat-Based PRD Creation**

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# train_grpo.py
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

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You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

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Complete Ollama Guide

Running GGUF Models Locally with Ollama

GGUF (GPT-Generated Unified Format) has quickly become the go-to standard for running large language models on your machine. There’s a growing number of GGUF models on Hugging Face, and thanks to community contributors like TheBloke, you now have easy access to them.

Ollama is an application based on llama.cpp that allows you to interact with large language models directly on your computer. With Ollama, you can use any GGUF quantized models available on Hugging Face directly, without the need to create a new Modelfile or download the models manually.

In this guide, we'll explore two methods to run GGUF models locally with Ollama:

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Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

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Remove USB Guard From Ubuntu

If you're a sucker like me and installed usbguard on a Ubuntu variant you may find that you will have access to none of your usb devices at all, because F you. The installer automatically sets up the daemon which has no rules so will just block all of your devices. Doing a basic apt remove usbguard may fail at 25%, because also F you.

My kernel is version 4.15.0-47-generic, not sure if this stopped working at some point or what.

Regain Access

sudo echo "allow id *:*" > /etc/usbguard/rules.conf

sudo sed -i 's/PresentDevicePolicy=apply-policy/PresentDevicePolicy=allow/' /etc/usbguard/usbguard-daemon.conf

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When you get the following error when trying to compile your Elixir project, it is due to library maintainers not properly pinning version per dependency and being affected by a breaking change that has affected many projects downstream.

could not compile dependency :ssl_verify_fun, "mix compile" failed

To fix, simply run from command line:

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<!DOCTYPE html>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
<meta content='width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=0' name="viewport" />
<link rel="icon" href="data:,">
<title>String art</title>
<script type="text/javascript" src="jquery.min.js"></script>
<style>
html, body {