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This guide focuses on on-device AI coding for production systems, with practical trade-offs for reliability, security, and cost.
Cloud models are default — until privacy, offline laptops, or 30ms autocomplete constraints say otherwise. On-device and edge completions should be the default path for local IDE ghost text, with cloud as escalation for hard tasks.
⚡ TL;DR: Route by task class. Tiny local model for complete-current-line; cloud for multi-file refactors. Version local models; measure quality floors; never send secrets off-box when local suffices.
on-device AI coding: production guidance
python
def route(task: dict) -> str:
if task["kind"] == "line_complete" and task["secret_risk"] == "low":
return "local_small"
if task.get("needs_repo_rag"):
return "cloud_rag"
if task["kind"] == "multi_file_refactor":
return "cloud_large"
return "local_small"
| Path | Strength | Weakness |
|---|---|---|
| On-device | Privacy, latency | Weaker reasoning |
| Edge (VPC) | Control | Ops cost |
| Cloud | Quality | Data egress |
Failure modes
Stale on-device models after a breaking tokenizer change. Pin model+tokenizer pairs and fail closed if checksum mismatches. Users disabling local path “because cloud is smarter” for secrets-laden files — enforce local for high secret_risk.Closing checklist
- [ ] Task-based router, not one model forever
- [ ] Local model update channel + checksums
- [ ] Quality floor evals for local path
- [ ] Secret-aware: prefer local when diff has keys scrubbed poorly
- [ ] UX shows which path answered
Series navigation
Day 88: Simulation and Digital Twins for Tools · Day 90: Project: Incident Timeline Summarizer Last updated September 11, 2026 Related: Day 83: Multimodal Coding: Screenshots of Broken UIs; Day 57: Coding Agents That Only Emit Intents; Day 70: Project: Secure Coding-Agent Sandbox on ECS Fargate.Watch: 100 Days of AI on YouTube
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