Ryan WardandClaude Opus 4.6
940c3b5dff
- Remove unused fields (trigger_offset on Requesting, is_incomplete) - Remove unused methods (selected_item, has_actions, is_menu_open, close_menu, move_selection, confirm_code_action, apply_workspace_edit) - Remove unused import (Shrinkable in signature_help) - Remove all #[allow(dead_code)] annotations - Add build standards to AGENTS.md: zero warnings, zero errors required Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
35 lines
2.2 KiB
Markdown
35 lines
2.2 KiB
Markdown
# Galaxy AI Agents - Ideas & Future Work
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## Build Standards
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The project must always have a **clean build with zero warnings and zero errors**. This applies to both `cargo check` and `cargo build`. Dead code warnings (`unused`, `dead_code`) should be resolved by either using the code, removing it, or adding targeted `#[allow(dead_code)]` annotations with a reason (e.g., code that's intentionally staged for upcoming work).
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---
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## LLM-Powered Predictive Autocomplete
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**Idea:** As the user types in the code editor, stream the current context (surrounding code, file structure, recent edits) to an LLM and predict what they're about to write — offering inline ghost-text completions similar to GitHub Copilot.
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**Scope options:**
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- By line (predict the rest of the current line)
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- By function (predict the full function body)
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- By class/module (predict structural code)
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**Challenges:**
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- Latency: can't hit the LLM on every keystroke. Need aggressive debouncing (500ms+), speculative pre-fetching, and streaming partial results.
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- Cost: high token volume. May need a small/fast model (Haiku) for inline suggestions with a larger model for multi-line predictions.
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- Context window: need to efficiently pack relevant context (current file, imports, related types, recent edits) without blowing the token budget.
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- Cancellation: must cancel in-flight requests when the user keeps typing past the prediction point.
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- UX: ghost text rendering, Tab to accept, partial accept (word-by-word), dismiss on divergence.
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**Possible approaches:**
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- Debounce + streaming: wait 500ms after last keystroke, stream tokens as they arrive, render as ghost text
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- Predictive pre-fetch: on function signature completion or newline, proactively request the likely next block
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- Local model: run a small code model locally for instant line completions, use cloud model for multi-line
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- Hybrid: use LSP completions for symbol-level, LLM for line/block-level predictions
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**Integration points in Galaxy:**
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- `app/src/code/completion.rs` — extend the completion state machine with an LLM provider
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- `crates/ai/` — existing Bedrock/LLM infrastructure can be reused
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- Editor decoration system — for rendering ghost text (similar to inlay hints)
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