AI use cases Local AI An offline AI coding assistant
An offline AI coding assistant
Use AI coding tools with a local model so code never leaves your machine. Setup workflow, limits and tools ranked on quality and safety.
- Tools compared
- 4
- Open source
- 4of 4
- Reviewed
- 16 Oct
For: Developers · Privacy-focused teams
What the score and shapes mean
Score = public maintenance, adoption and docs signals, as a percentile of 52 tools. Nobody here tested these hands-on. Shapes are the separate safety label: No issues found, No dependencies scanned, Caution.
What it does for you
Developers with confidentiality or offline requirements.
- Autocomplete and chat inside your editor with a local model
- Run a terminal pair programmer against a local endpoint
- Work on air-gapped or confidential codebases
- Avoid per-token API costs
- Try several open coding models
More about this job
Several coding assistants can use a local model through Ollama instead of a hosted API. You keep code on your machine and can work offline, in exchange for weaker results than the best hosted models.
Workflow in 5 steps
- Install a local runner and pull a coding-focused model that fits your memory.
- Point the coding tool to the local endpoint using its documented setup.
- Keep tasks small: single functions, tests, explanations.
- Compare results with a hosted model on a few real tasks to know the gap.
- Update models periodically.
Full steps
- Install a local runner and pull a coding-focused model that fits your memory.
- Point the coding tool to the local endpoint using its documented setup.
- Keep tasks small: single functions, tests, explanations.
- Compare results with a hosted model on a few real tasks to know the gap.
- Update models periodically; open coding models improve fast.
Prompts to copy
Starting points we wrote, not tested results.
Explain what this function does and list any edge cases it does not handle. Keep it under 120 words.
2 more prompts
Write a unit test for this function in the style of the tests in this folder.
Suggest a clearer name for this variable and show every place it must change.
Watch out for
- Local models are usually smaller than hosted frontier models.
- Treat AI changes like a pull request from a new colleague: read the diff, run the tests and keep a human sign-off before anything ships.
- Check each tool's licence before commercial use, and check the licence of any code or text the AI reproduces.
- Local models are slower on laptops and have shorter useful context.
- CautionOllama2 critical-severity known vulnerabilities in runtime dependencies (OSV)
- CautionCline13 critical-severity known vulnerabilities in runtime dependencies (OSV)
- CautionContinue36 critical-severity known vulnerabilities in runtime dependencies (OSV)
- CautionAider18 critical-severity known vulnerabilities in runtime dependencies (OSV)
Why these warnings?
- Local models are usually smaller than hosted frontier models. Expect weaker results on hard reasoning and long contexts, and test on your own tasks.
- Treat AI changes like a pull request from a new colleague: read the diff, run the tests and keep a human sign-off before anything ships.
- Check each tool's licence before commercial use, and check the licence of any code or text the AI reproduces.
- Local models are slower on laptops and have shorter useful context. Agentic multi-file edits are where the gap to hosted models is largest.
- Ollama: 2 critical-severity known vulnerabilities in runtime dependencies (OSV)
- Cline: 13 critical-severity known vulnerabilities in runtime dependencies (OSV)
- Continue: 36 critical-severity known vulnerabilities in runtime dependencies (OSV)
- Aider: 18 critical-severity known vulnerabilities in runtime dependencies (OSV)
Related jobs
Sources for tool facts
ollama/ollama · cline/cline · continuedev/continue · Aider-AI/aider · Aider with Ollama · Continue with Ollama · Cline with Ollama. Data fetched 2 Nov 2026. Scores and labels are automated, relative and not a hands-on test.
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