Samuel Devdas SamuelDevdas

[Project Name]

Every time you choose to apply a rule(s), explicitly state the rule(s) in the output. You can abbreviate the rule description to a single word or phrase.

Project Context

[Brief description ]

[more description]
[more description]
[more description]

Minimal Prompt Chainables

Sequential prompt chaining in one method with context and output back-referencing.

Files

main.py - start here - full example using MinimalChainable from chain.py to build a sequential prompt chain
chain.py - contains zero library minimal prompt chain class
chain_test.py - tests for chain.py, you can ignore this
requirements.py - python requirements

Setup

Create a new directory with these three files (requirements.txt, main.py, README.md)
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python main.py
Update main() to run the example prompt chains

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

	import asyncio
	from bleak import BleakScanner, BleakClient
	from enum import IntEnum
	import struct
	import time

	# Service and Characteristic UUIDs
	UART_SERVICE_UUID = "6E400001-B5A3-F393-E0A9-E50E24DCCA9E"
	UART_TX_CHAR_UUID = "6E400002-B5A3-F393-E0A9-E50E24DCCA9E" # Write
	UART_RX_CHAR_UUID = "6E400003-B5A3-F393-E0A9-E50E24DCCA9E" # Read/Notify

	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

Samuel Devdas SamuelDevdas

[Project Name]

Project Context

Minimal Prompt Chainables

Files

Setup

Setup

Anti-hype LLM reading list

Foundational Concepts

Pre-Transformer Models