Stack recipe

Local Coding Assistant Stack

A coding stack for testing open coding assistants with local or self-hosted models, repository-aware tools, and human code review.

Best for

Developers who want coding help close to their editor while keeping model choice, context, and review workflows under control.

Core tools

Continue
Aider
Ollama
LM Studio
OpenCode

Recommended models

Qwen3 Coder
DeepSeek Coder V2
DeepSeek R1 distills
Small Qwen or Mistral variants for local tests

Hardware notes

Small coding models can run on many developer machines. Larger coding models need more VRAM or a hosted/self-hosted inference endpoint.

Setup steps

Pick one assistant interface for your normal coding workflow.
Connect it to a local runtime or a controlled model endpoint.
Test on a small repository task with clear expected changes.
Review every diff before applying it.
Track which model works best for your language, framework, and repo size.

Trade-offs

Local coding assistants can reduce data exposure but may be slower or weaker than frontier hosted models. Generated patches still need careful review.

Alternatives

Use Continue for IDE-native workflows.
Use Aider for terminal-first git-aware edits.
Use a hosted model when quality matters more than local control.

FAQ

Can local coding assistants edit files automatically?

Some can, but you should keep review steps in place and avoid unattended writes until the workflow is proven.

Which model should I test first?

Start with a coding-focused model that fits your hardware, then compare it on real tasks from your own repositories.

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