What's new

Changes

• Select a GPU for CUDA and Triton problems and see kernel timings across multiple input sizes after a successful submission.
• Compare your kernel against a reference implementation on the same GPU, and keep results from different GPUs side by side for the same code.
• Run kernels on NVIDIA H100, H200, RTX 5090, and RTX PRO 6000, and T4.

Changes

• Ask Toni for hints, explanations, and debugging help directly from study-plan problems.
• Use Toni while practicing interview problems without leaving the editor.

Changes

• Ask Toni, our AI assistant for every problem for progressive hints, debugging guidance, and detailed explanations while keeping the implementation yours.
• Research-problem starter code now includes data types and clearer return formats.

Changes

• Reload the Problems page faster with the initial problem list ready when the page opens.
• See each user's leaderboard rank directly on their profile.
• Free-problem starter code now includes data types and clearer return formats.

Changes

• Open all your saved problem notes from the account menu or directly from the notes editor.
• Check your remaining CUDA, Triton, and GPU project usage from the new GPU usage indicator.

Changes

• Learn how LoRA fine-tunes language models through an interactive LLM Internals guide.
• Pause and resume timed interview prep sessions without losing your remaining solve time.

Changes

• Accepted code and SQL interview prep problems now count toward progress.
• Solved interview prep problems now appear in recent activity.

Changes

• Share interview experiences, ask questions, vote, and reply.
• Continue the LLM Internals series with speculative decoding.

Changes

• Thirteen computer vision visualizations were refreshed.
• Solutions for free problems in the Pandas, NumPy, PyTorch, and SQL Sheets are now available to signed-in learners.

Changes

• Fine-tune Qwen3-4B in a guided GPU notebook.
• Build an inference server and explore Mixture of Experts routing.

Changes

• Follow a guided roadmap from foundations through complete projects.
• Track your TensorTonic 125 progress and continue directly into practice.

Changes

• Build a RAG system and a ReAct agent step by step.
• Create an MCP server through guided notebook exercises.

Changes

• Practice language modeling with NumPy, PyTorch, CUDA, and Triton.
• Move from core components to training and systems exercises.

Changes

• Submit product ideas directly from TensorTonic.
• Browse requests and see what other learners want next.

Changes

• Understand common use cases for conventional machine learning.
• Follow best practices from data preparation through model evaluation.

Changes

• Learn policy networks, value networks, and Monte Carlo tree search.
• Work through the system with interactive visual explanations.

Changes

• Build a scalar autograd engine and neural network from first principles.
• Use TensorTonic in a complete light theme across the platform.

Changes

• Study the Kimi K3 architecture through ten interactive visualizations.
• Inspect its model design and training concepts in one guided page.

Changes

• Add a TensorTonic progress badge to your GitHub profile README.
• Keep a generated index of solved problems in your solutions repository.

Changes

• Walk through GPT pretraining with interactive visualizations.
• Study attention, batching, quantization, KV cache, and MoE serving.

Changes

• Implement transformer components through a structured capstone.
• Run and evaluate the completed model inside TensorTonic.

Changes

• Explore GPT-2, Llama, Gemma 3, Word2Vec, and DenseNet visually.
• Inspect DeepSeek-V3, GLM-4.5, GPT-OSS, and Arcee Trinity.

Changes

• Practice LLM internals and research frontier mathematics.
• Test training, decoding, post-training, and alignment knowledge.

Changes

• Find problem sets and study plans through a redesigned Problems page.
• See the full learning experience through a refreshed landing page.