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CleverKeys Roadmap

This document outlines the planned development path for CleverKeys, focusing on expanding device support, improving neural prediction capabilities, and enhancing customization.

✅ Recently Completed (v1.2.x)

v1.2.9

  • Timestamp Keys - Insert formatted date/time with custom patterns
  • Pre-defined Timestamp Shortcuts - 8 ready-to-use timestamp keys

v1.2.8

  • Quick Settings Tile - Add keyboard to Android Quick Settings panel
  • Password Manager Clipboard Exclusion - Privacy for 1Password, Bitwarden, etc.
  • Test Keyboard Field - Built-in text field in Settings for testing
  • Android 15 Edge-to-Edge Fix - Navigation bar no longer covers keyboard
  • Monet Theme Crash Fix - Fixed dynamic color extraction on tablets

v1.2.4

  • Selection-Delete Mode - Swipe-hold backspace to select and delete text
  • TrackPoint Navigation - Joystick-style cursor control on nav keys

v1.2.0-1.2.3

  • Space Key Repeat on Hold - Long-press space now repeats like delete key
  • Finger Occlusion Compensation - Configurable Y-offset setting (0-50%)
  • Per-Key Short Swipe Customization - 204+ commands assignable to any key
  • Multi-Language Swipe Typing - 11 languages with accent recovery
  • Language Quick Toggle - Swap between primary/secondary languages instantly
  • Profile System - Layout import/export with gesture customizations
  • New Website - Tailwind dark-mode homepage with demo at /demo/
  • User Guide Wiki - 38-page comprehensive user documentation

🚀 Core Features & Stability

  • Expanded Terminal Support

    • Replace hardcoded com.termux checks with a robust TerminalUtils system.
    • Support for broad range of terminal emulators: Termius, JuiceSSH, ConnectBot, Android Virtualization Framework (com.android.virtualization.terminal), and others.
    • Custom Package Config: Allow users to manually designate apps as "Terminal Mode" to force Ctrl+W deletion and raw key cursor movement.

  • Custom Word Handling (Completed v1.1.88)

    • On-the-fly Dictionary Addition → Dictionary Manager with 3-tab UI
    • Custom Dictionary Weighting → Per-language beam search trie with priority

🧠 Neural Network & Prediction

  • Vocabulary Expansion (Fine-tuning)

    • Address gaps in current vocabulary (e.g., words like "popsicle", "narcissist").
    • Implement a federated-style on-device learning mechanism to fine-tune the ONNX model on user's typing history without data leaving the device.

  • Layout-Agnostic & Multi-Script Gesture Model (target: Q2–Q3 2026)

    • Current limitation: The v1 gesture engine is trained on English + QWERTY. Quality is acceptable for other Latin-script QWERTY languages (es, fr, pt, it, de, nl, id, ms, tl, sw) but degrades on non-QWERTY layouts (AZERTY, QWERTZ, Dvorak, Colemak, Neo2) and on non-Latin scripts (Cyrillic for Russian/Ukrainian/Bulgarian/Serbian, Greek, Arabic, Devanagari, CJK romanization, etc.), where swipe is currently auto-disabled on non-QWERTY row shapes (see #9).
    • Goal: Two complementary swipe pipelines — keep the trained transformer for QWERTY+Latin (current strength) and add a geometric/template path-matcher for arbitrary layouts and scripts. The geometric engine resembles Urik and AnySoftKeyboard's gesture approach: dictionary-driven candidate scoring against the swipe path's nearest-key sequence, no per-layout training required.
    • Why dual-path: Geometric matchers ship instantly for any layout (Russian Cyrillic, AZERTY, QWERTZ, Dvorak, Colemak, Arabic, Greek) once a dictionary exists, and they compose with our existing dictionary infrastructure. Trained transformer stays the default for English+QWERTY where its accuracy lead is largest; geometric path takes over when the active layout doesn't match the trained geometry.
    • Implementation plan:
      • Build a geometric scorer that walks a swipe trajectory against per-layout key centroids and ranks dictionary words by path-length + nearest-key alignment cost.
      • Add Russian (Cyrillic ЙЦУКЕН) layout + dictionary as the first non-Latin target. Validate with native-speaker swipe sessions.
      • Extend to AZERTY, QWERTZ, Dvorak, Colemak, Neo2 using the same engine — just new layout geometries + existing dictionaries.
      • In parallel, explore extending the trained encoder to consume layout geometry as a conditioning input so the transformer eventually catches up on alt layouts.
      • Ship behind a feature flag; auto-route per layout: transformer for QWERTY-Latin, geometric for everything else.

  • Next-Word Prediction

    • Top-of-suggestion-bar word prediction based on the previous word(s), distinct from autocorrect/swipe.
    • Distilled transformer (BERT-style or n-gram blend) optimized for ARM64 CPU; sub-50ms latency budget on mid-range devices.
    • Per-language models, sharing the dictionary infrastructure already in place.

🛠 Customization & UI

  • Smart Profile System

    • Auto-switch profiles based on active app (e.g., "Code" profile for Termux, "Chat" profile for WhatsApp).

  • Advanced Theme Engine

    • Shareable JSON-based themes.
    • Dynamic wallpaper-based theming (Material You/Monet) expansion.

🤝 Community & Open Source

  • Dataset Contribution Pipeline: Optional, privacy-preserving mechanism for users to donate anonymized swipe trajectories to improve the open-source model.
  • Documentation: Comprehensive guides for creating custom layouts and retraining the model.