I'm very surprised you find this workflow more efficient than just writing the code. I find constructing the mental model of the solution and how it fits into existing system and codebase to be 90% of effort, then actually writing the code is 10%. Admittedly, I don't have to write any boilerplate due to the problem domain and tech choices. Coding agents definitely help with the last 10% and also all the adjacent work - one-off scripts where I don't care about code quality.
Disadvantages don't list performance hit for proxying every operation through another indirection layer. Can this sort of interface be implemented with zero overhead in Rust?
That's why you see such a difference in time saved using LLMs for programming across the population. If you have all the domain knowledge and the problem is generic enough it's a 100x multiplier. Otherwise your experience can easily range from 0.1x to 10x.
This approach to security is backwards. It's way harder to find security issues than to never include them in the first place. This approach might work for another webapp but I highly doubt a retroactive security analysis is practical for a more involved system.
Looks like we're in a similar situation. What is your current go-to for setting up lean incremental data pipelines?
For me the core of the solution - parquet in object store at rest and arrow for IPC - haven't changed in years, but I'm tired of re-building the whole metadata layer and job dependency graphs at every new place. Of course the building blocks get smarter with time (SlateDB, DuckDB, etc.) but it's all so tiresome.
* code translation - e.g. convert a self-contained implementation of a numerical algorithm from one language to another and generate test cases and property tests which make sure the implementations are equivalent. The goal is to avoid having to proof read the generated code.
* one-off scripts - any task where code design doesn't matter, the amount of code is limited to couple hundred lines (GPT-4o) and the result will be thrown away after use.
* API exploration - producing examples for APIs and languages I'm not fluent in. Reading reference documentation gives a better understanding, LLMs get the results out faster.
> I think it's a bit of a myth that (ignoring FPGAs) that writing a low-latency software trading system is a time/cost expensive process.
This depends a lot on the complexity of the trading system and the trading venue specifics. A system to trade single stocks or futures can be built, certified and running in 3 months. A system for options market making will take a lot longer.
Interesting that this is Clojure and it doesn't mention Hoplon/Javelin[0] as prior work. I've used Hoplon/UI[1] to implement a moderately complex web app ~6 years ago. The library is practically a prototype, pretty much dead by now. However, I found the ideas interesting.
I find the biggest benefit of using a fringe library like this is the ability to read and understand the whole implementation. It's really simple compared to something like React.
Had to do the same. However, I do handstand push ups on small parallettes[0] which keeps the wrist in the similar position as doing fist push ups, but doesn't hurt my knuckles.
Very good points. Agree with most of them - especially the lack of interfaces. I'm currently working on a ~30k LOC Julia codebase which was mostly written by myself. This is split into ~80 packages within a monorepo. I've never ran into problems with tests taking too long as tests for a single module take a reasonable amount of time to run.
I'd really love a PackageCompiler that would "just work" though. Julia 1.6+ made a lot of progress in pre-compile times. However, nothing beats being able to easily release a binary...
They were probably also sourcing their state from the events. However most of their problems come from sharing the events between the modules/services which is not a part of Event Sourcing.
I'll answer for Switzerland which also has a strong bicycling culture. People cycle while they're physically able. I have a neighbour couple who are around 70 and cycle everywhere in the city limits.