Beyond Autocomplete: Local AI Code Completion Demystified

How can a small, local AI provide better code completion than giant cloud services? By validating every single suggestion before you see it.

#1about 6 minutes

The case for local AI code completion

While cloud-based AI offers powerful models, a local approach provides better security, lower latency, and no subscription cost by using smaller, specialized models.

#2about 4 minutes

Measuring user experience with online A/B testing

Online evaluation uses A/B testing to measure positive signals like code generation and negative signals like user annoyance to validate feature improvements.

#3about 2 minutes

Guaranteeing code correctness with semantic checks

Suggestions are validated for semantic correctness by the IDE before being shown to the user, eliminating errors like non-existent variables.

#4about 3 minutes

Using a filter model to reduce user annoyance

A secondary machine learning model predicts the probability of a suggestion being accepted, filtering out suggestions that are correct but unhelpful.

#5about 2 minutes

Implementing efficient local model inference

Using a native C++ inference engine like Llama.cpp enables fast, low-level execution of the language model directly on the user's machine.

#6about 2 minutes

Training small, specialized language models from scratch

Training small, language-specific models in-house is cost-effective and allows for extensive experimentation to optimize performance for local execution.

#7about 2 minutes

Accelerating development with offline evaluation

An offline evaluation pipeline runs the IDE in a headless mode to test hypotheses quickly, pre-selecting the most promising changes for slower A/B tests.

#8about 1 minute