Comments URL: https://news.ycombinator.com/item?id=47663002

Points: 2

# Comments: 1

I agree, although I think the problem usually comes in writing the spec in the first place. If you can write detailed enough specs the agent will usually give you exactly what you asked for. If you're spec is vague, it's hard to eyeball if the tests or even the implementation of the tests matches what you're looking for.

Someone I know wrote the code and the unit tests for a new feature with an agent. The code was subtly wrong, fine, it happens, but worse the 30 or so tests they added added 10 minutes to the test run time and they all essentially amounted to `expect(true).to.be(true)` because the LLM had worked around the code not working in the tests

For example instead of: Duolingo - I practice with my friends Calorie tracking - I have planned meals from my dietitian Workout tracking - I have WhatsApp with my PT, who adjusts my next workouts from our conversations Reminders - A combo of Siri + Fantastical + My Wife

I'm sure my way is more expensive but I don't know, there is also a non tangible cost of not having friends/personal connections as well.

There kind of is - it's the same reason B2B SaaS tend to make more money than B2C - it's easy (easier) to sell someone something if they can make money from it.

If I can pay you Y to rivet some sheet metal together and sell the finished product for Y * 10, that's a much better outcome for me (economically) than paying someone to take care of my elderly parents. In fact, maybe I'm not mean, maybe _I_ don't make enough money to afford to pay someone to take care of my elderly parents.

It's a testament to how big and deep the ocean is

Is it open to the public? I'd like to check it out

In fact - I'd say we're looking at this backwards - GPUs used to be the thing that did math fast and put the result into a buffer where something else could draw it to a screen. Now a "GPU" is still a thing that does math fast, but now sometimes, you don't include the hardware to put the pixels on a screen.

So maybe - CPX is "just" a GPU but with more generic naming that aligns with its use cases.

Even for use cases Graph dbs knock out of the park Neo4j (historically, I haven't used it in like 10 years) didn't work very reliably compared to modern competitors.

But as always it's about picking the right tool for the job - I tried to build a "social network" in mysql and neo4j, and (reliability aside) neo4j worked way better.

Arguably that's a GPU? Other than (currently) exotic ways to run LLMs like photonics or giant SRAM tiles there isn't a device that's better at inference than GPUs and they have the benefit that they can be used for training as well. You need the same amount of memory and the same ability to do math as fast as possible whether its inference or training.

And yes, these are all "skill issues" - as in, if they had known better this wouldn't have happened to them, however I think it's fair to call these possibilities out to counter balance the AI is amazing and everyone should use it for everything type narratives as to instil at least a little caution.