Frequently Asked Questions From Dereference AI Codetabs
What is Dereference AI Codetabs—and why was it built this way?
Dereference AI Codetabs is a locally executed, session-centric AI IDE engineered for developers who demand full ownership of their AI interactions. It was built to eliminate the privacy compromises baked into most AI coding tools—replacing cloud-dependent inference with CLI-native orchestration, and replacing stateless chats with versioned, branchable reasoning. It’s designed for those who say “show me the logs” before trusting an AI assistant with production code.
Which AI models integrate natively—and how is compatibility ensured?
Dereference AI Codetabs doesn’t host models. It orchestrates them—via your existing CLI tools. Officially supported: Claude Code (via official CLI), GPT-4 (through openai-cli or compatible wrappers), and Gemini (via gemini-cli). Any CLI tool that accepts stdin/stdout and emits structured JSON responses can be integrated in under 5 minutes using our extensible adapter framework—no SDKs, no vendor lock-in.
Where does my data live—and how is it protected?
Your data lives exclusively on your device. Prompts, responses, session history, and branch snapshots are stored in encrypted, user-owned directories (e.g., ~/.dereference/sessions). No telemetry agent runs. No background processes phone home. No “improved suggestions” require uploading your code. If your machine is secure, your AI workflow is secure—full stop.
Does “native performance” mean it requires high-end hardware?
No. Dereference AI Codetabs itself consumes minimal resources—a few MB of RAM and negligible CPU. Performance bottlenecks reside only in your chosen LLM CLI tools and local hardware acceleration (e.g., CUDA for local Llama.cpp). We optimize for *orchestration efficiency*, not model inference—so whether you’re running tiny quantized models on a laptop or massive fine-tuned variants on a workstation, the IDE stays snappy and predictable.
How do atomic branches differ from simple “copy-paste” conversation cloning?
Atomic branches preserve *execution state*, not just text. They capture the full context window—including hidden system messages, temperature, top_p, and even CLI flags used to invoke the model. More importantly: they enable *cross-branch analysis*. You can diff two branches to see exactly where reasoning diverged, highlight conflicting assumptions, or generate a consensus summary—functions impossible with manual duplication.
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