ml0x Research
Technical references, cheatsheets, and detailed writeups from the ml0x research pipeline.
Telegram's own bot docs on privacy mode and Guest Mode, read line by line, checked against three live bots' published data models: what an anonymous-posting or locked-message bot actually stores.
One metered month behind a Max 20x seat came to 33.3 billion tokens, $26,187 at Opus 5 API list prices. The same traffic priced on ten alternatives, the cache-read arithmetic that decides the ranking, and a calculator for your own mix.
Real 2026 GPU rental prices, the break-even token volume before owning compute wins, why Kimi K3 fits no 8-GPU node, and why self-hosting it costs more than the vendor's own API. Includes the five claims an adversarial audit removed.
The complete evidence behind the cost study. Every price table, model size computed from weight files, published throughput measurement, the break-even grid, and the claims an adversarial audit removed.
The definitive ML cheatsheet covering supervised and unsupervised algorithms, evaluation metrics, sklearn code snippets, hyperparameter tuning, data preprocessing, and common pitfalls. One page, everything you need.
Interactive feature engineering checklist with progress tracking and data type detector. Covers numeric, categorical, text, date/time features, and missing data handling. Paste sample data to get suggestions.
Free visual ML pipeline builder. Drag and drop preprocessing, feature engineering, model, and evaluation steps. Generates production-ready scikit-learn Pipeline and PyTorch code instantly.
Interactive comparison of regularization techniques: L1 (Lasso), L2 (Ridge), Elastic Net, and Dropout. Visualize coefficient shrinkage paths, neuron deactivation, and the effect of lambda on model complexity.
Interactive transfer learning guide with decision tree, model compatibility matrix, and layer freezing calculator. Choose between feature extraction, fine-tuning, and training from scratch based on your dataset size, ...