Remaining Time + ⏳

“All models are wrong, but some are useful.” — George Box Advanced ETA Prediction for Anki Time Left+ is a highly customizable progress and ETA tracking add-on for Anki built around a lightweight, local Machine Learning engine. Most ETA add-ons rely on a simplistic formula (e.g., dividing remaining cards by your average speed over the last 5 minutes). That approach becomes inaccurate almost immediately once your pacing changes or you switch to harder cards. Time Left+ instead learns your actual review behavior in real time and continuously updates its predictions to provide highly accurate estimates of: Remaining study time Estimated finish time Session pacing Retention metrics Fatigue-related slowdowns Expected distraction/zone-out time 🧠 Smart Machine Learning Predictions No TensorFlow. No PyTorch. No cloud processing. The add-on securely analyzes your Anki review history fully offline and trains a lightweight predictive model directly on your machine. The system evaluates dozens of dimensions of study behavior using a multi-tiered architecture that blends specific historical medians with Stochastic Gradient Descent (SGD) regression: Deep Context & Historical Card Lookup Per-Card History: If you've reviewed a specific card before, the model knows. It actively tracks your historical median time for individual cards to provide hyper-accurate baseline predictions. Context Lookup Tables: If a card is new, the model falls back to a personalized lookup table based on your speed for that exact Deck + Tag + Queue combination. Text Metrics: Word count, image count, and linguistic readability/complexity (Flesch-Kincaid). FSRS Integration: Fully supports Anki's native FSRS algorithm by extracting and weighing the exact Difficulty (D) and Stability (S) of every card. Adaptive Bias Correction & Time Psychology Active Bias Correction: The model tracks the error of its predictions over your last 50 cards. If you are having a fast day and reviewing 2 seconds quicker than expected, the model instantly auto-corrects its bias to match your current brain-state. Cyclical Time-of-Day Encoding: Reviewing at 11 PM is different than 8 AM. Time Left+ encodes the hour of the day using advanced cyclical sine/cosine math so the model understands that 11 PM and 1 AM are chronologically close, learning your exact circadian rhythm. Pacing: Current streak velocity, short-term pacing trends, and historical review speed are all fed directly into the model. Adaptive "Zone-Out" Modeling Accidentally leave a card open for 15 minutes? Instead of just ignoring these outliers, the add-on runs a bifurcated model optimized with Huber Loss (to prevent extreme outliers from ruining the regression). It calculates your raw reading speed on normal cards, but actively tracks your distraction probability . If you historically "zone out" on 2% of your reviews, the model intelligently predicts and buffers that exact amount of lost time into your ETA. 📊 Fully Customizable Progress Metrics Build your own status line using configurable variables. You can freely combine metrics and style them using custom CSS: %(elapsedTime) Time spent studying so far %(remainingTime) ML-predicted remaining time left %(ETA) Exact finish time (24-hour format) %(ETA12) Exact finish time (12-hour AM/PM format) %(CPM) Current Cards Per Minute pacing %(RR) Session Retention Rate 🎨 Advanced UI Customization Every UI component exposes targetable CSS classes for deep styling control. Customize: Progress bar appearance Font styling, colors, and spacing Metric layout and screen positioning Includes built-in options to pin the progress bar to the bottom of the screen, minimize UI clutter, and create persistent HUD-style layouts. 🏃 Dynamic Velocity Tracking & Fatigue Alerts Time Left+ continuously compares: Short-Term Sprint Velocity (last ~10 cards) Long-Term Baseline Velocity (last ~100 cards) When your pacing drops significantly — such as a >50% slowdown — the add-on can proactively alert you that fatigue may be affecting performance. This improves: ETA stability and pacing awareness Long-session consistency Burnout prevention The system adapts dynamically in real time instead of assuming your review speed remains constant throughout a session. ⚙️ Under the Hood The predictor uses a pure-Python Stochastic Gradient Descent (SGD) with Huber Loss blended with a multi-tiered dictionary lookup. It is optimized for lightweight local execution. No external ML frameworks are required. Local Execution: Fully offline computation with SQLite-backed storage. Efficiency: Minimal memory usage and near-zero performance overhead (no lag between cards). Data Persistence: Your trained model weights and personal statistics are safely stored in a `user_files/` directory that survives add-on updates. Privacy: No background telemetry and no cloud APIs. 🚀 Getting Started On first launch, Time Left+ automatically imports approximately your most recent 10,000 review records and trains the prediction model in the background. You receive personalized ETA predictions immediately without needing weeks of usage data. Configuration & Statistics Path Tools → Add-ons → Time Left+ → Config Tools → Time Left+: ML Stats & Analysis Tools → Time Left+: Export ML Stats to CSV 🔒 Privacy All calculations occur entirely on your machine using data Anki already stores locally. No review history is uploaded or collected. Compatibility Supported: Desktop Anki Not Currently Supported: AnkiDroid / AnkiMobile Based on the original add-on: Remaining Time **Back my education** 🩺

Min Anki	Max Anki	Updated
2.1.1	+	2026-09-22

AnkiWeb

Maintenance

GitHub

Will it work on my Anki?

Similar addons

Description