The hottest Tokenization Substack posts right now

The tokenized funds market issuance on public blockchains grew from $100 million to over $800 million in 2023.
DeFi tokenization is gaining popularity among financial institutions like Franklin Templeton and Fasanara's Untangled Finance.
There are exclusive insights and analyses available for paid subscribers in the DeFi tokenization space.

Crypto has evolved through Proof of Work, ICOs, and Airdrops for token distribution, each with lessons on network growth.
The concept of Progressive Ownership suggests using revenue sharing and allowing users to opt into ownership for sustainable growth.
Building products that meet user needs, using revenue sharing, and transitioning loyal users to economic ownership are key steps in implementing Progressive Ownership.

The dominance of English in technology can limit inclusivity in AI models
Processing non-English languages in AI can be more costly due to tokenization concepts like fertility
Efforts are being made to enhance representation of non-English languages in AI, promoting diversity and inclusivity

Tokenization is a powerful way to reduce costs and secure card payments by isolating parts of payment applications for PCI compliance.
Tokens are non-exploitable and require a vault to store the actual data, providing security in case of a breach.
Using Tokenization as a Service providers can strengthen a startup's position by avoiding vendor lock-in and enhancing pricing power.

Points in loyalty programs are a way to move users up the loyalty ladder by rewarding behaviors with tangible benefits and creating emotional connections with the brand.
When designing a points program, consider if points should target user acquisition or retention, if they should be redeemable, and what types of rewards are enticing for users.
Onchain tokens have advantages over traditional points in terms of composability, but drawbacks like speculation impacting consumer behavior and legal/regulatory challenges need to be considered.

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Large language models achieve state-of-the-art compression rates on text, image, and audio data.
Increasing model size on fixed datasets initially enhances compression rates, but can lead to deterioration.
The impact of token vocabulary size on compression rates varies for different model sizes.

When creating a new programming language, start by making examples to define how it will look and function.
Select a language for your compiler that you are comfortable with, and consider libraries to simplify parsing and generating output.
Include key components like a tokenizer, parser, and generator in your compiler, and focus on type checking and editor tooling for a better user experience.