feat(llm): wire Phase 3 local LLM runtime via llama-cpp-2

kon-llm now owns a real LlamaBackend + LlamaModel, with three Qwen3 tiers
(1.7B Q4, 4B-Instruct-2507 Q4, 14B Q5) selectable per hardware. Downloads
are resumable with SHA-256 verification and stored under ~/.kon/models/llm.

Engine exposes three high-level surfaces — all greedy/temp-0, GBNF-constrained
where output shape matters:
- cleanup_text (prompt-injection-hardened system prompt; profile terms
  appended as "preserve these spellings" suffix)
- decompose_task (3–7 micro-steps, constrained JSON array)
- extract_tasks (optional-array; empty when no explicit commitments)

post_process_segments now takes an Option<&LlmEngine> and, when loaded and
format_mode != Raw, joins segments → cleanup → replaces segments with the
cleaned text (first segment span). Rule-based path still runs first; LLM
errors log and keep rule-based output.

Tauri commands: recommend_llm_tier, check_llm_model, download_llm_model,
load_llm_model, unload_llm_model, delete_llm_model, get_llm_status,
cleanup_transcript_text_cmd, extract_tasks_from_transcript_cmd,
decompose_and_store (LLM-backed subtasks).

Settings: AI tier toggle (off / cleanup / tasks), model picker with
downloaded/loaded status, download progress events via
kon:llm-download-progress.

Dictation: ensureLlmModelLoaded on mount, cleanupTranscriptIfEnabled after
stop when tier != off and format_mode != Raw, LLM task extraction when
tier=tasks (regex fallback on failure).

Interim: both llama-cpp-sys-2 and whisper-rs-sys statically link their own
ggml, so src-tauri/build.rs emits -Wl,--allow-multiple-definition on Linux.
Replace with a system-ggml shared-lib setup as a follow-up.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

crates/llm/src/prompts.rs

@@ -0,0 +1,12 @@
+pub const DECOMPOSE_TASK_SYSTEM: &str = "\
+You are a task-decomposition assistant. Given a task description, produce \
+between 3 and 7 concrete, physical micro-steps. Each step must be a short \
+imperative sentence, actionable today, with no commentary. Output ONLY a \
+JSON array of strings.";
+
+pub const EXTRACT_TASKS_SYSTEM: &str = "\
+You are a task-extraction assistant. Given a transcript of spoken notes, \
+output a JSON array of action items the speaker committed to. Each item must \
+be a short imperative sentence. Omit observations, wishes, and background \
+context that are not explicit commitments. Output an empty array if there are \
+no action items.";