iCommit vs TokenLab

Learn something new — 5 minutes at a time. iCommit turns learning software engineering, computer science, and AI into a daily habit you'll actually keep. Each day you make one "commit": a bite-size lesson you finish in under 5 minutes, then a quick review that locks it into memory. WHY ICOMMIT • Bite-size by design — real concepts in 5-minute micro-lessons, no filler. • Learn to code across the stack — software engineering, computer science fundamentals, algorithms and data structures, web development, machine learning, and AI. • Remember what you learn — built-in spaced repetition resurfaces lessons right before you'd forget them. • Start at your level — a quick placement check meets you where you are, beginner to advanced. • Build a streak — a GitHub-style commit grid turns consistency into something you can see. • Level up Spark — your mascot evolves through 14 tiers as you grow, with badges to earn. HOW IT WORKS 1. Pick your topics — coding, CS, ML, web development, and more. 2. Get today's commit — one focused micro-lesson at your level. 3. Review & retain — a short quiz and spaced repetition make it stick. 4. Keep the streak alive — daily reminders keep your habit on track. PERFECT FOR • Beginners learning to code from scratch • Students reinforcing computer science fundamentals • Developers sharpening skills between projects • Anyone preparing for coding interviews • Busy people who only have 5 minutes a day Free to start. Learn to code, one commit a day.

TokenLab teaches you how modern language models work by letting you build, train, evaluate, and use small models directly on your iPhone. Follow an 18-module learning path that begins with machine learning foundations, tokenization, embeddings, attention, and transformer architecture. Continue into model training, fine-tuning, alignment, reinforcement learning, preference optimization, RAG, agents, evaluation, on-device optimization, embodied AI, and robotics. LEARN BY DOING - Each lesson combines concise technical explanations with: - Interactive diagrams and model controls - Worked examples grounded in real training concepts - Knowledge checks designed to test understanding - Guided experiments that connect theory to measurable results - Reference materials and saved lessons for later review TRAIN MODELS ON DEVICE - Use the Training workspace to conduct real micro-model experiments on supported iPhones: - Train a tiny transformer from scratch - Adapt a pretrained model with a compact adapter - Create and edit training datasets - Configure model size, context length, learning rate, steps, and sampling - Track training and validation loss - Inspect generalization, latency, memory, and test results - Save local checkpoints and compare previous runs - Stop experiments manually or automatically when device conditions require it EXPERIMENT IN PLAYGROUND Chat with the pretrained model or select one of your saved checkpoints. Compare how training decisions affect model responses and return to earlier conversations when testing new ideas. Playground conversations and model checkpoints are stored locally and can be deleted from within the app. APPLY EACH MODULE IN A LAB Guided labs turn concepts into structured experiments. Work with validation splits, tokenizers, model architecture, adapters, retrieval, evaluation, robotics policies, and other practical workflows. TRACK REAL PROGRESS - Build a visible record of your development through: - A structured skill tree - Module and lesson completion - Lab results - Mastery checks - Experience levels and weekly missions - Saved experiment history DESIGNED FOR SERIOUS LEARNING TokenLab is built for developers, students, technical professionals, and anyone who wants to understand what happens inside a language model. The interface supports Light and Dark Mode and follows your iPhone’s system appearance automatically. Lesson progress, assessments, and training records can sync securely to your TokenLab account. Model execution takes place on device. TokenLab’s micro models are designed for education and experimentation. They are not intended to replace production-scale language models.