Notes on the nature of Intent-Based Markets.

Towards a Proof-Based Future - Written with 0xkrane.

The unprecedented rise of Large language models (LLMs) has seen a quick adoption of its technology in various spheres. While language models and transformers have been used before for Google Search, translation, and sentiment recognition, ChatGPT can be credited for bringing the transformer architecture and LLMs to the forefront of public attention and usage, seeing as many as 100 million users within a few days.

Towards unlocking a new class of applications. Not the compute we need, but the compute we deserve.

A reminder of progress and problems unsolved.

Getting to know about an ~~~intense~~~ intent-centric world & some other thoughts.

Upgrading our Fee Markets to the final form.

Exploring the second part of the Theory of MEV. :D

Constant Function Market Makers (CFMMs) have emerged as an innovation in decentralized finance, providing an efficient and computationally cheap on-chain solution for exchanging digital assets without needing traditional order books. CFMMs revolutionize how assets are traded, enabling a more seamless and accessible marketplace and have become the most widely used decentralized crypto product. However, as the popularity of decentralized finance grows, so does the need to address the privacy concerns arising from the transparent nature of blockchain-based transactions. In this article, we will delve into the nature of privacy in CFMMs and discuss them in reference to the works “A note on privacy in Constant Function Market Makers” by Angeris et al. and “Differential Privacy in Constant Function Market Makers” by Tarun Chitra et al.