In-Memory C++ Leap in Blockchain Analysis

We've been thinking about it, but according to Show HN rules, blog posts are considered off-topic.

Do you see crypto as anything more than scams/crime/speculation?

Most people involved in crypto pretend it's the future and their business models depend on pumping up crypto. That might be the same for you all but I figure of anyone in the space, a group dedicated to tracking down where coins are moving for government agencies (I assume for scams/crime reasons) might not have the wool so pulled over their eyes.

First of all, at Caudena, we are not involved in crypto projects or investments ourselves. Our expertise lies in analyzing blockchains and providing deep technical insights into how various blockchains operate. We focus on tracking and understanding the flow of digital assets, often in support of government agencies investigating scams, fraud, and other illicit activities.

That said, we absolutely believe that blockchain and cryptocurrency will shape the future of the financial system. When you look beyond the noise of scam tokens, speculative NFTs, and high-profile scandals, there is significant and meaningful financial innovation happening. This extends beyond DeFi to include the tokenization of RWA, where major institutions like BlackRock and JPM Chase are actively exploring and implementing blockchain-based solutions. Numerous projects are driving real progress, and there’s a slow but steady movement toward a more decentralized and transparent financial ecosystem.

Assuming by undoing you mean splitting the cluster:

A linked list can be split in two in O(1). When it comes to updating the roots for all the removed nodes, there is no easy way out, but luckily:

- This process can be parallelized.

- It could be done just once for multiple clustering changes.

- This is a multi-level disjoint set, not all the levels or sub-clusters are usually affected. Upper level clustering, which is based on lower confidence level, can be rebuilt more easily.

If by undoing you mean reverting the changes, we don’t use a persistent data structure. When we need historical clustering, we use a patched forest with concurrent hash maps to track the changes, and then apply or throw them away.

We use a single instance for all clients, but when one CFD server processes new block data, it becomes fully blocked for read access. To solve this, we built a smart load balancer that redirects user requests to a secondary CFD server. This ensures there's always at least two servers running, and more if we need additional throughput.