The problem ChainSpecter solves

The traditional process of conducting audits for smart contracts has been burdened with high costs, requires intensive manual testing, and often requires significant lead time. This poses significant challenges for projects seeking to ensure the security and reliability of their code. However, recent advances in AI enable fast and accurate analyis of contracts and even allow smart AI driven fuzzing and static analyis. It's not a stretch of the imagination to suggest that automated AI powered auditing tools will outperform traditional manual auditing in the near future.

The tool not only democratizes access to cutting-edge AI powered auditing capabilities but also establishes an open and verifiable system for audit reports. With our platform, developers can ensure the security of their contracts, demonstrate their commitment to robust code practices, and build trust within their Web3 community.

Challenges I ran into

The main issue was I tried to do too much for a single person. I spent a considerable amount of time playing with APIs and different models to perform static analysis of the code. Integrating different chains amplified the time needed also.

Whilst I was successful in getting basic AI auditing of smart contracts, it would have been good to be able to finish integrating the woke fuzzer directly into the processes. Because of time I ended up removing this which is a shame as using AI to generate test cases with reinforced learning was a really exciting part of what I wanted to get done in this project.

Still, I demonstrated the fundamental approach, much iteration and tweaking is still required but the principle is sound.