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5 articles
Tokenized real-world assets are emerging as a solution to crypto's persistent counterparty risk and settlement inefficiencies, offering streamlined, transparent trading and reduced capital tying up.
Tokenization drift is a subtle but critical issue in AI where minor formatting differences in input text cause models to behave inconsistently. This phenomenon can lead to unexpected performance drops without any changes to data or logic.
This article explains how AI companies like Anthropic are being used as payment for real estate transactions through blockchain-based tokenization, demonstrating advanced concepts in digital asset valuation and smart contract technology.
Learn how tokenizers work in AI models and why changes to text processing can dramatically affect costs, even when prices per token stay the same.
Learn how to prepare for the next generation of language models with extended context windows by implementing token management, text chunking, and reasoning mode simulation techniques.
