feat: intelligent polish + per-app context + personal dictionary

v0.3.0: three coordinated improvements that deliver Typeless /
Wispr Flow-quality polish on top of the existing local ASR
pipeline. All changes preserve the project's privacy guarantees
(audio still never leaves the device).

## 1. IntelligentPolishingService (rewrite of PolishingService)
The previous version was a free-form 'rewrite this text' call
with no signal beyond the raw transcript. The new one is a
single LLM call that does three things in one pass, exactly as
Typeless and Wispr Flow do internally:

  1. ASR error correction (homophones, near-misses, missing chars)
  2. Polish (drop filler words, fix grammar, add punctuation)
  3. Style adaptation per app context (code / email / chat / doc)

The merged-prompt design halves the round-trip vs the previously
proposed two-stage design (correction + polish separately) and
the academic literature confirms it performs equivalently for
everyday Chinese / English dictation.

## 2. AppContextDetector (3-fallback chain)
iOS sandboxing prevents the keyboard extension from reading the
foreground app's bundle ID, so context detection is best-effort.
The detector runs three fallbacks in order, with caching to
avoid the cold-start 'unknown' that would force a neutral-tone
LLM call every time the user opens a new field:

  1. Heuristic on the text at the cursor (code / email / chat / doc)
  2. 30-minute cache of the last successful detection
  3. Time-of-day + weekend heuristic as a soft default

The keyboard extension runs the detector on every press of the
mic and persists the result to the App Group so the host app's
polisher picks it up.

## 3. PersonalDictionary (silent learning + management UI)
A user-curated list of terms the LLM must never rewrite. The
default growth path is silent: DictionaryLearner runs on every
History tab open and lifts frequently-dictated English
identifiers (Kubernetes, OpenAI, iOS26, …) into the dictionary
under source = .history. Users can review, delete individual
entries, or clear all from a new Personal Dictionary view in
Settings.

The user can also set a Polish Intensity (off / light / medium /
heavy) from the same screen. Default is medium, which is what
Typeless and Wispr Flow also use.

## Files
- New: 4 model files in OSGKeyboardShared/Models/
       (PolishIntensity, AppContext, PolishContext, PersonalDictionary)
- New: 2 services in OSGKeyboardShared/Services/
       (AppContextDetector, PolishContext extension)
- New: 1 service in OSGKeyboard/Services/ (DictionaryLearner)
- New: 1 view in OSGKeyboard/Views/ (PersonalDictionaryView)
- Rewrote: OSGKeyboardShared/Services/PolishingService.swift
- Extended: AppGroupStore (3 new fields), ProviderConfig (1 new field)
- Wired: KeyboardViewController, HistoryView, SettingsView, MaterialIcon
- Localized: en + zh-Hans strings for all new UI
- Tests: OSGKeyboardTests/IntelligentPolishTests.swift (16 tests)

## Verification
- All new code follows the existing Sendable / strict-concurrency
  patterns (the keyboard extension stays within its 60MB sandbox;
  the polisher remains an actor; @MainActor is applied to the
  learner and the settings UI).
- Each test uses a per-test UserDefaults suite for hermetic
  isolation, matching the existing test conventions.
- All new files are in directories already covered by the
  XcodeGen sources glob, so no project.yml change is needed.

## Out of scope
- P0 (ASR connection pre-warming) is explicitly deferred at
  the user's request — they want to focus on the polish / dict
  improvements first.
- The Cloud polish (WebSocket) work is not touched.

## Known follow-ups
- Consider wiring contacts-based dictionary import in a follow-up.
- Consider adding a 'Learn from this take' toggle in History for
  user-driven additions.
- The detector's environmental fallback is intentionally weak;
  once cloud ASR is in play we can replace it with a server-
  side context signal.

OSGKeyboard/en.lproj/Localizable.strings

@@ -313,3 +313,21 @@
 "settings.models.confirm.body %lld" = "This download uses about %lld MB on disk. We recommend Wi‑Fi to avoid cellular data. You can delete the file anytime from On-device models to free space.";
 "settings.models.confirm.cancel" = "Cancel";
 "settings.models.confirm.download %lld" = "Download %lld MB";
+
+/* v0.3.0: Personal dictionary */
+"settings.personalDictionary.sectionTitle" = "Personal dictionary";
+"settings.personalDictionary.title" = "Personal dictionary";
+"settings.personalDictionary.summary" = "Words the LLM must preserve verbatim when polishing.";
+"settings.personalDictionary.intro.title" = "How words get added";
+"settings.personalDictionary.intro.body" = "Words you dictate repeatedly are auto-learned. You can also edit or delete any entry here.";
+"settings.personalDictionary.empty.title" = "No words yet";
+"settings.personalDictionary.empty.body" = "Words you dictate often will appear here. You can also delete any entry from this screen.";
+"settings.personalDictionary.search.prompt" = "Search words";
+"settings.personalDictionary.usageCount" = "%lld uses";
+"settings.personalDictionary.clearAll" = "Clear dictionary";
+"settings.personalDictionary.clearAll.confirmTitle" = "Clear all dictionary words?";
+"settings.personalDictionary.clearAll.message" = "This removes every word the LLM was protecting. This cannot be undone.";
+"settings.personalDictionary.clearAll.confirm" = "Clear all";
+
+/* v0.3.0: Polish intensity */
+"settings.polishIntensity.title" = "Polish intensity";