Adds a pixel-art restaurant to Anki where reviews bring tipping guests, with a pomodoro timer, collectible pets, decorations, stats and a home-screen widget.
Alternatives to π PomoVN - Pomodoro Timer for Anki - from AnkiVN with β€οΈ
AnkiWeb addon 1469762619
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Similar addons (found without AI)
Adds an all-in-one study workspace with study plans, deadlines, Pomodoro timer, notebook, PDF viewer, mind maps, AI assistant, gamified XP, ranks, streaks, and themes.
Adds a draggable floating Pomodoro and study timer during review sessions, with automatic breaks, pause/resume, card counter, optional Focus Rhythm feedback, session summaries, and customizable display modes.
Plays your own focus music and runs a Pomodoro timer inside Anki, tracking streaks, tomato-garden progress, and shareable study recaps.
Monitors card activity during Anki reviews to pause or restart a configurable Pomodoro focus timer, show study stats, plan decks around exam dates, and optionally sync sessions.
Runs a customizable Pomodoro focus timer on the review screen with break games, session stats, adaptive coaching, optional prayer-time pauses, and offline background sounds.
Turns Anki reviews into an RPG-style progression system with XP, levels, streaks, retention bonuses, at-a-glance study stats, and a floating stats window.
Replaces Anki's deck and review screens with a customizable widget dashboard featuring activity heatmaps, Pomodoro timer, habit tracking, exam countdowns, Quizlet-style study modes, card previews, six themes, and four languages.
Displays review activity heatmaps, daily review goals, progressive streak levels, retention percentages, 60-day trends, and interactive card history, with customizable colors and date ranges.
Displays a customizable Pomodoro timer while studying flashcards, rings a bell at session or break end, tracks completed cycles with sushi symbols, and can be toggled from the Tools menu.
Provides a detailed analytics dashboard for Anki review history, including heatmaps, retention, consistency, efficiency scores, trends, session and lapse analysis, and time-range filters.
Predicts remaining study time and finish time using an offline machine-learning model, with customizable progress displays, metrics, CSS styling, and fatigue alerts.