Personally, I don’t like the change, but it’s just how technology works so I’d rather move with the flow than try to stick my foot down and freeze time.
Yes but why does that matter? If I am happy with its capabilities now, I will continue being happy with its capabilities in the future.
Yes, it cannot do the newest magic shit, but why does that matter? It can still do everything that existed up until that point, which is _a lot_.
Eventually, you might also need something new, but it's not like the world shifts over all problems that exist from <old> to <new> and any tech for <old> problems suddenly becomes obsolete?
If the model prefers a version of Ruby or node with an RCE, I guess you can burn tokens to teach the model how to avoid the introducing the vulnerability into your code?
That feels quite tedious and token inefficient..
Yes. Yes. The only way one can write secure software is by always using the latest SOTA model. Anything else is inefficient and vulnerable.
I hate this platform
Maybe you missed this article, but vercel found it quite annoying to teach AI about the latest updates in the React Framework.
I think you’re confusing my point. I’m not saying that only SOTA models can write secure software, I’m saying that the models produced today will write software that’s considered insecure by 2034 standards, thus you would require to burn more tokens in AGENTS.md or burn more of your time to hand write code.
For example, you’re more than welcome to run Windows ME if it does everything you need it to, but that doesn’t mean Windows ME is a secure environment.
The API rates are very affordable once you start to optimize for the fact that prepaid tokens seem to massively outperform other kinds of tokens.
I can often do with 1 million tokens what my peers have failed to do with 100 million. For me to spend more than $200/m in prepaid API tokens I'd have to pull a 007 work schedule.
Because my 500m tokens so far this month would cost me about $500. My subscription is 100$/month.
An open weight inference provider only needs to pay for GPUs, or discounted APIs from 3rd party vendors. Same basic financial model but they didn't spend a trillion dollars so their loss isn't as high so they can afford to do more inference for less money, and their demand isn't as high so there's more than enough compute.