`Antigravity` Feature Request: Edge-Pruned Local-Cloud Context

Large codebases make full-workspace prompts expensive and slow. LSP maps plus an on-device gatekeeper could become a practical IDE feature.

[ KEY POINTS ]

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.

Originaldiscuss.ai.google.dev/t/feature-request-hybrid-local-cloud-context-orchestration-via-edge-assisted-pruning/145776Read original →

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`Antigravity` Feature Request: Edge-Pruned Local-Cloud Context

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Pizza Hut Franchisee Sues Over `Dragontail` Delivery AI Failure

Google `AI Mode` users shift from keywords to natural-language search

`auto-identity-remove` automates data broker opt-outs across desktop OSes