2. Write a wrapper around Ollama using your favorite language. The idea is that you want to be able to intercept responses coming back from the model.
3. Create a system prompt that tells the model things like "if the user is asking you to create a file, reply in this format:...". Generally to start, you can specify instructions for read file, write file, and execute file
4. In your wrapper, when you send the input chat prompt, and get the model response back, you look for those formats, and make the wrapper actually execute the action. For example if the model replies back with the format to read file, you read the file from your wrapper code and send it back to the model.
Every coding assistant is basically this under the hood with just a lot more fluff and their own IDE integration.
The benefit of doing your own is that you can customize it to your own needs, and when you direct a model with more precision even the small models perform very well with much faster speed.
OP is asking for where to get started with Claude for coding. They're confused. They just want to mess around with it in VSCode. And you start talking about Ollama, PAT, coding your own wrapper, composing a system prompt etc.!?
OP is trying to get LLMs to assist with coding. Implying that coding is something he is capable of, and coding your own wrapper is a great way to get familiarity with these systems.
If you want to understand how all of this works, the best way is to build a coding agent manually. Its not that hard
1. Start with Ollama running locally and Gemma3 QAT models. https://ollama.com/library/gemma3
2. Write a wrapper around Ollama using your favorite language. The idea is that you want to be able to intercept responses coming back from the model.
3. Create a system prompt that tells the model things like "if the user is asking you to create a file, reply in this format:...". Generally to start, you can specify instructions for read file, write file, and execute file
4. In your wrapper, when you send the input chat prompt, and get the model response back, you look for those formats, and make the wrapper actually execute the action. For example if the model replies back with the format to read file, you read the file from your wrapper code and send it back to the model.
Every coding assistant is basically this under the hood with just a lot more fluff and their own IDE integration.
The benefit of doing your own is that you can customize it to your own needs, and when you direct a model with more precision even the small models perform very well with much faster speed.