It was completely correct and I realized LLM are capable of generalizing beyond their training sets

Stack: go, python Team size: 8 Experience, mixed.

I'm using a code review agent which sometimes catches a critical big humans miss, so that is very useful.

Using it to get to know a code base is also very useful. A question like 'which functions touch this table' or 'describe the flow of this API endpoint' are usually answered correctly. This is a huge time saver when I need to work on a code base i'm less familiar with.

For coding, agents are fine for simple straightforward tasks, but I find the tools are very myopic: they prefer very local changes (adding new helper functions all over the place, even when such helpers already exist)

For harder problems I find agents get stuck in loops, and coming up with the right prompts and guardrails can be slower than just writing the code.

I also hates how slow and unpredictable the agents can be. At times it feels like gambling. Will the agents actually fix my tests, or fuck up the code base? Who knows, let's check in 5 minutes.

IMO the worst thing is that juniors can now come up with large change sets, that seem good at a glance but then turn out to be fundamentally flawed, and it takes tons of time to review

I think Materialize offers a similar product, but last I checked it was only available as a SaaS solution.

I hope to do a proof of concept soon, to compare both solutions

Clojure sort of guides you to simplicity, building everything out of functions and simple datastructures has big advantages when testing and reasoning about code.

I do find that in larger code bases, Clojure lack of types causes friction (spec is just a bandaid, not a fix).

There are languages with immutability and types (like Haskell), but these don't have the get-shit-done factor I seek.

I do thoroughly review of the the LLM answers, and hardly every directly copy paste answer, so I feel this way I still learn the language.

Asking a LLM to translate between languages works really well most of the time. It's also a great way to learn which libraries are the standard solution for a language. It really accelerated my learning process.

Sure, there is the occasional too literal translation or hallucination, but I found this useful enough.

Comments URL: https://news.ycombinator.com/item?id=42907488

Points: 268

# Comments: 47

- Serverless can get very expensive - DevEx is less than stellar, can't run a debugger - Vendor lock-in - You might be forced to update when they stop supporting older runtime versions

We built this huge system with tons of regexes, custom parsers, word lists, ontologies etc. It was a huge effort to get somewhat acceptable accuracy.

It is humbling to see that these days a 100 line Python script can do the same thing but better: AI has basically taken over my first job.