Ryan WardandClaude Opus 4.6
d421b9583f
- Tab/Enter confirms the selected completion item - Up/Down arrows navigate the completion menu - Escape dismisses the menu (via existing dismiss_lsp_overlays) - Editor intercepts keys via completion_intercept_keys flag when menu is showing - Add AGENTS.md with LLM-powered predictive autocomplete idea Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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1.8 KiB
Galaxy AI Agents - Ideas & Future Work
LLM-Powered Predictive Autocomplete
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.
Scope options:
- By line (predict the rest of the current line)
- By function (predict the full function body)
- By class/module (predict structural code)
Challenges:
- Latency: can't hit the LLM on every keystroke. Need aggressive debouncing (500ms+), speculative pre-fetching, and streaming partial results.
- Cost: high token volume. May need a small/fast model (Haiku) for inline suggestions with a larger model for multi-line predictions.
- Context window: need to efficiently pack relevant context (current file, imports, related types, recent edits) without blowing the token budget.
- Cancellation: must cancel in-flight requests when the user keeps typing past the prediction point.
- UX: ghost text rendering, Tab to accept, partial accept (word-by-word), dismiss on divergence.
Possible approaches:
- Debounce + streaming: wait 500ms after last keystroke, stream tokens as they arrive, render as ghost text
- Predictive pre-fetch: on function signature completion or newline, proactively request the likely next block
- Local model: run a small code model locally for instant line completions, use cloud model for multi-line
- Hybrid: use LSP completions for symbol-level, LLM for line/block-level predictions
Integration points in Galaxy:
app/src/code/completion.rs— extend the completion state machine with an LLM providercrates/ai/— existing Bedrock/LLM infrastructure can be reused- Editor decoration system — for rendering ghost text (similar to inlay hints)