disler

What is an AI Coding Assistant?

If the coding assistant can't run ITERATIVE CRUD on ALL of your code, it's not a True AI Coding Assistant (TACA)

Standards for TRUE AI Coding Assistants

1. Must work on existing codebases
2. Must have a file context mechanism
3. Must be iteratively controllable

mberman84

git clone https://github.com/OpenDevin/OpenDevin.git
cd OpenDevin
conda create -n od python=3.10
conda activate od
docker ps
(optional) install docker if not already installed
docker pull ghcr.io/opendevin/sandbox
export OPENAI_API_KEY={your key}
(optional I had to install rust) curl --proto '=https' --tlsv1.2 -sSf [https://sh.rustup.rs](https://sh.rustup.rs/) | sh
(optional) restart terminal

disler

Setup

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

disler

This is not working complete code.

This is strictly a v0.2, scrapy, proof of concept version of a personal AI Assistant working end to end in just ~726 LOC.

This is the second iteration showcasing the two-way prompt aka multi-step human in the loop. The initial, v0, assistant version is here.

It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.

To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.

disler

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

disler

Four Level framework for prompt engineering

Watch the breakdown here in a Q4 2024 prompt engineering update video

LLM library

Ollama

Level 1: Ad hoc prompt

• Quick, natural language prompts for rapid prototyping
• Perfect for exploring model capabilities and behaviors

disler

Prompt Chaining with QwQ, Qwen, o1-mini, Ollama, and LLM

Here we explore prompt chaining with local reasoning models in combination with base models. With shockingly powerful local models like QwQ and Qwen, we can build some powerful prompt chains that let us tap into their capabilities in a immediately useful, local, private, AND free way.

Explore the idea of building prompt chains where the first is a powerful reasoning model that generates a response, and then use a base model to extract the response.

Play with the prompts and models to see what works best for your use cases. Use the o1 series to see how qwq compares.

Setup

• Bun (to run bun run chain.ts ...)

disler

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

yifanzz

[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]

disler

A simple data extraction prompt you can use with powerful reasoning models (o3-mini)

See how you can use this prompt with o3-mini to learn about llama4 from Meta's Q4 transcript