191–200 of 396 posts
Are these kinds of articles a new breed of rage bait? They keep ending up on the front page with thriving comment sections, but in terms of content they're pretty low in nutritional value. So I'm guessing they just rise because they spark a debate?
We don't commit compiled blobs in source control. Why can't the same be done for LLMs?
After working with the latest models I think these "it's just another tool" or "another layer of abstraction" or "I'm just building at a different level" kind of arguments are wishful thinking. You're not going to be a designer writing blueprints for a series of workers to execute on, you're barely going to be a product manager translating business requirements into a technical specification before AI closes that gap…
I think it's doubtful you'll be even that; certainly not with the salary and status that normally entails.
> I'm very convinced non-technical people will be able to use these tools
This suggests that the skill ceiling of "Vibe Coding" is actually quite low, calling into question the sense of urgency with which certain AI influnecers present it, as if it were a skill that you need to invest major time & effort to hone now (with their help, of course), lest you get left behind and have to "catch up" later. Yet one could easily see it being akin to Googling, which was also a skill (when Google was usable), one that did indeed increase your efficiency and employable, but with a low ceiling, such that "Googler" was never a job by itself, the way some suggest "prompt engineer" will be. The Google analogy is apt, in that you're typing keywords into a blackbox until it spits out what you want; quite akin to how people describe "prompt engineering."
Also the Vibe Coding skillset--a bag of tricks and book of incantations you're told can cajole the model--has a high churn rate. Once, narrow context windows meant restarting a session de novo was advisable if you hit a roadblock, but now it's usually the opposite.
If this all true, then wouldn't the correct takeaway, rather than embracing and mastering "Vibe Coding" (as influencers suggest), be to "pivot" to a new career, like welding?
> The irony is that I haven't seen AI have nearly as large of an impact anywhere else. We truly have automated ourselves out of work, people are just catching up with that fact
What's funny is artists immediately, correctly perceived the threat of AI. You didn't see cope about it being "just another tool, like Photoshop."
Are these kinds of articles a new breed of rage bait? They keep ending up on the front page with thriving comment sections, but in terms of content they're pretty low in nutritional value. So I'm guessing they just rise because they spark a debate?
What I find interesting is that similar propositions made 2 years ago was ragebait to the same people but they ended up coming true.
Earlier quoted context omitted.
Right, but that's the point -- prompting an LLM still requires 'thinking about thinking' in the Papert sense. While you can talk to it in 'natural language' that natural language still needs to be _precise_ in order to get the exact result that you want. When it fails, you need to refine your language until it doesn't. So prompts = high-level programming.
You can't think all the way about refining your prompt for LLMs as they are probabilistic. Your re-prompts are just retrying until you hit a jackpot - refining only works to increase the chance to get what you want. When making them deterministic (setting the temperature to 0), LLMs (even new ones) get stuck in loops for longer streams of output tokens. The only way to make sure you get the same output twice is to us…
And provably correct one-shot program synthesis based on an unrestricted natural language prompt is obviously an oxymoron. So, it's not like we are clearly missing the target here.
If we can treat the prompts as the versionable source code artefact, then sure. But as long as we need to fine-tune the output that's not a high level language. In the same way no one would edit the assembly that a compiler produces
Imagine a machine that does the job sometimes but fails on some other times. Wonderful isn't it?
Earlier quoted context omitted.
I may have bad news for you on how compilers typically work.
Deterministic compilation, aka reproducible builds, has been a basic software engineering concept and goal for 40+ years. Perhaps you could provide some examples of compilers that produce non-deterministic output along with your bad news.
Earlier quoted context omitted.
Right, but that's the point -- prompting an LLM still requires 'thinking about thinking' in the Papert sense. While you can talk to it in 'natural language' that natural language still needs to be _precise_ in order to get the exact result that you want. When it fails, you need to refine your language until it doesn't. So prompts = high-level programming.
You can't think all the way about refining your prompt for LLMs as they are probabilistic. Your re-prompts are just retrying until you hit a jackpot - refining only works to increase the chance to get what you want. When making them deterministic (setting the temperature to 0), LLMs (even new ones) get stuck in loops for longer streams of output tokens. The only way to make sure you get the same output twice is to us…