It is a very rare occasion where I hear about someone doing things completely different from the main stream. Something in which a series of esoteric or raw computing science concepts are brought together to create a cohesive whole.
Today's episode starts with Adventures in the Rho Calculus. And onwards to
thinking beyond programming languages with applied category theory, context, persistent memory, and single-level memory.
Backing up a bit, an interesting quote from the first:
Essentially Goldman built a distributed transaction log of everything their global corporate machine did — an enterprise-wide blockchain of sorts. But writing software for this sort of system is not trivial and certainly not something that fits with traditional Silicon Valley thinking. It’s only when multiple banks started trying to figure out how to co-mingle these transactions for traceability and regulatory purposes did the topic of blockchain-esque designs start in earnest. SecDB was software built for risk management — with the primary risk being the software system itself. This was a hot topic after the 2008 crash and probably not coincidence that Craig Wright’s research was in this same area and Satoshi Nakamoto emerged about the same time.
With a note to the paper on Rho Calculus
It is surprising how many of today's techniques hearken back to Lisp (WebAssembly) and Multics.
Unbundle Enterprise Capabilities introduces and refines concepts of early binding (static analysis compilers like C++) and late binding (iterative tools like Python). The article is one of several which incorporate CodeBuilders, a code/data analysis platform. A passing reference is made to SecDB (securities DB) built by Goldman, in which they recorded all trades and related information, which helped them survive the 2008 financial meltdown.
In the article Collaboration… Inception style, we get this interesting quote:
Developers must work through outdated file-based methodologies and deal with jarring edit-run processes (with only limited hot loading capabilities). While that may have been acceptable decades ago, Singh and Higgins proved there are better alternatives.
... which would tend to indicate that there are better ways of creating solutions that typically encountered.
Another article, AI Automation of Software Development, gets heavy:
... monadic structures to retain and manipulate computing state remain nebulous and are a long way from becoming easy to use or first-class structures (at least outside Haskell). NYC was an early adopter of a functional optimization technique called memoization that stores and tracks interim model state. The analogy in general computing (and getting back to the cone of specialization) is something called ‘function currying’. Wall Street found an interesting way to persist and manage graphs of curried functions.
A random reference elsewhere: Protocol Labs -- originators of Interplanetary File System.
