dm3 · HackerTrans

قبل 10 أشهرdiscuss

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.

dm3

قبل 11 شهرًاdiscuss

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?

dm3

قبل 11 شهرًاdiscuss

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.

dm3

قبل 12 شهرًاdiscuss

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.

dm3

قبل 12 شهرًاdiscuss

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.

dm3

السنة الماضيةdiscuss

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

dm3

قبل سنتينdiscuss

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.

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