#0001
`Antigravity` Feature Request: Edge-Pruned Local-Cloud Context
50radar
AntigravityAI coding IDE — Gemini-powered agent workspace
Large codebases make full-workspace prompts expensive and slow. LSP maps plus an on-device gatekeeper could become a practical IDE feature.
- Proposed stack: local
LSP/Tree-sitter indexing, a compact code model, then cloud inference for the heavy reasoning. - The cloud model would request missing context via commands like
find_dependencies("TargetClass"), avoiding full repo upload. - This is not shipped; it is a one-user feature request. Value is the product signal, not an immediately usable workflow.
- Clear SaaS angle: token pruning, privacy filtering, and dependency retrieval can be sold as agent tooling middleware.
Source: discuss.ai.google.dev/t/feature-request-hybrid-local-cloRead original →
