dm3

Monthly payment depends on Kanton as well - in the German part you can still get basic insurance for 250 USD/month.

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.

What would be the largest difference to Kurrent (former EventStore)?

I find LLMs via Aider great for:

* 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.

Oh wow, do you find this sort of a career fulfilling?