So you’ve read the theory, but want to understand Bitcoin programmatically. In this post we will analyse the core codebase written in C++ to understand how one can generate and include a block in the chain. Starting with the atomic unit of construction in this context, a block is a grouping of transactions that alter the state of the ledger. For example, a coinbase transaction may be included by a miner to collect a block reward.

One of my favorite ways to write tests in Go is with a table driven test. If you are unfamiliar with the concept check out the excellent Dave Cheney post. If you need to unit test a component under a multitude of edge cases, then this is good technique to adopt. Anyway, I’m learning Rust in my spare time so I thought I’d best give it a go. Hopefully this will suffice, but if you think it could be improved please hit me up on twitter.

You may be familiar with the concept of a Request for Comments (RFC) from a body such as the Internet Engineering Task Force (IETF). They are just technical documents that describe the specifications for a particular technology - HTTP/1.1 is described by RFC-2616 for instance. Application level standards in the Ethereum ecosystem are thus named accordingly, forming one possible part of an Ethereum Improvement Proposal (EIP). For more details on this, check out EIP-1.

In traditional governance systems, we rely upon human entities to enact policies on behalf of constituents. This may viewed as in contemporary politics or in the hierarchical structure of an enterprise. An historical issue however, is that it is not always possible to trust the judgement of those in charge. In economics, this is known as the principle-agent problem. A DAO is a form of self-enforcing protocol, typically modelled as a smart contract.

I was recently tasked with developing a new encoding library for Hyperledger Burrow to further interoperability with the broader Ethereum ecosystem. Recursive Linear Prefix (RLP) is a data format used to store state in Ethereum, more precisely it is an algorithm for representing arbitrary data structures in binary form. Unlike other serialization techniques however, the output is position dependant in that recovering the original object requires knowledge of the input structure.

I was extremely humbled to attend the first ever Interplanetary File System (IPFS) camp last week. It comprised approximately three days of tutorials, lectures and problem solving in the idyllic Campus La Mola, a short distance from Barcelona, Spain. There were roughly 150 attendees from around the globe who came together to discuss ideas for the distributed web, focusing on content addressable methods of data dissemination and persistence but expanding on higher level technologies which build upon these ideas.

Welcome to the first post from what will hopefully become a series on my adventures with Go! I’m really lucky to be able to experiment with some super awesome technologies which I will endeavour to write about more, so if you find this post helpful please let me know on twitter! If you’re new to Go, follow the getting started docs. You’ll also need to configure access to a Kubernetes cluster, or install Minikube - a single local node.

We’ve undergone a lot of infrastructure changes recently at work. We actually submitted the very first DLT framework into Helm’s stable charts over a year ago. This allows anyone with a Kubernetes cluster to deploy a custom blockchain courtesy of Burrow (our contribution to the Hyperledger Greenhouse). We’re a great believer in cloud first and open source technologies so not only is Kubernetes a great fit for what we do, but Helm extraordinarily simplifies the whole deployment process through Go templating.

CryptoKnight is a framework I recently released which follows the methodology described in my publication for synthesizing a scalable dataset of cryptographic primitives to feed a unique Convolutional Neural Network. In effect, this allows us to generate and model a substantial amount of data to quickly identify cryptographic algorithms (such as AES, RSA, RC4, or MD5) in reference binary executables. By safely learning this statistical representation, malware analysts can efficiently compare it against crypto-ransomware samples in a controlled environment.