Building Your Own Coding Assistant with AI Tools

Building your own coding assistant with AI tools is no longer a daunting task, even for those without technical knowledge or coding experience. In this article, we will explore how to utilize existing large language models dedicated to coding assistance, choosing the best model for your use case, and selecting the appropriate size of the model based on the number of parameters suited for your machine configuration. We will also delve into a working example of how to utilize a built coding assistant for generating code, testing the generated code, building and pushing a Docker image for the generated code, and deploying Python code in Kubernetes with the generated Docker image locally on Minikube.

Introduction to Coding Assistants

Introduction to coding assistants and their potential in enhancing coding productivity

The speaker introduces himself and explains that he will be discussing how to use existing models meant for coding-related activities. He emphasizes the importance of setting up the necessary tools to utilize these models.

Setting Up the Necessary Tools

Setting up the necessary tools, including Ollama and MsTY, for coding assistance

The speaker mentions the two tools needed: Ollama and MsTY. He refers to his previous videos that explain how to install and use these tools. The main intention of the current video is to explain how to set up publicly available large models dedicated to coding assistance.

Publicly Available Large Language Models

Publicly available large language models for coding assistance

The speaker explains how to find publicly available large language models trained for coding assistance. He demonstrates how to search for these models on Ollama and mentions the importance of checking how frequently the models are updated.

Choosing the Best Model

Choosing the best model based on the number of parameters and update frequency

The speaker discusses the factors to consider when choosing a model, including the number of parameters and how recently the model was updated. He explains that the number of parameters affects the model's performance and that more parameters generally provide better assistance.

Working Example: Generating Code

Working example of generating code using the chosen model

The speaker demonstrates how to use the chosen model to generate code. He asks the model to provide Python code for creating TLS certificates and receives a response with the necessary code.

Testing the Generated Code

Testing the generated code and requesting modifications

The speaker tests the generated code and asks the model to modify it to include additional functionality. He demonstrates how the model can remember the context and generate new code based on the modifications requested.

Building and Pushing a Docker Image

Building and pushing a Docker image for the generated code

The speaker explains how to build a Docker image for the generated code and push it to Docker Hub. He demonstrates the simplicity of this process using the modeling tool.

Deploying the Docker Image

Deploying the Docker image on Kubernetes using Minikube

The speaker demonstrates how to deploy the Docker image on Kubernetes using Minikube. He explains the benefits of using Minikube for local deployment and testing.

Conclusion and Final Thoughts

Conclusion and final thoughts on the potential of coding assistants

The speaker concludes by emphasizing the potential of coding assistants in enhancing productivity. He encourages viewers to explore different models and tools to find what works best for them. He also mentions the importance of considering the number of parameters and update frequency when choosing a model.