The most interesting thing about everyone trying to position themselves as AI experts is the futility of it: the technology explicitly promises tomorrows models will be better then todays, which means the skill investment is deflationary: the best time to learn anything is tomorrow when a better model will be better at doing the same work - because you don't need to be (conversely if you're not good at debugging and reverse engineering now...)
the best time to learn anything is tomorrow when a better model will be better at doing the same work
doesn’t that presume no value is being delivered by current models?
I can understand applying this logic to building a startup that solves today’s ai shortcomings… but value delivered today is still valuable even if it becomes more effective tomorrow.
I'm pretty much just rawdogging Claude Code and opencode and I haven't bothered setting up skills or MCPs except for one that talks to Jira and Confluence. I just don't see the point when I'm perfectly capable of writing a detailed prompt with all my expectations.
The problem is that so many of these things are AI instructing AI and my trust rating for vibe coded tools is zero. It's become a point of pride for the human to be taken out of the loop, and the one thing that isn't recorded is the transcript that produced the slop.
I mean, you have the creator of openclaw saying he doesn't read code at all, he just generates it. That is not software engineering or development, it's brogrammer trash.
That’s true for “tips and tricks” knowledge like “which model is best today” or “tell the model you’ll get fired if the answer is wrong to increase accuracy” that pops up on Twitter/X. It’s fleeting, makes people feel like “experts”, and doesn’t age well.
On the other hand, deeply understanding how models work and where they fall short, how to set up, organize, and maintain context, and which tools and workflows support that tends to last much longer. When something like the “Ralph loop” blows up on social media (and dies just as fast), the interesting question is: what problem was it trying to solve, and how did it do it differently from alternatives? Thinking through those problems is like training a muscle, and that muscle stays useful even as the underlying technology evolves.
I have a reading list of a bunch of papers i didn't get through over the past 2 years. it is crazy how many papers on this list are completely not talked about anymore.
I kinda regret going through the SeLU paper lol back in the late 2010s.
You nailed it. Thats exactly how I feel. Wake me up when the dust settles, and i'll deep dive and learn all the ins and outs. The churn is just too exhausting.
> the technology explicitly promises tomorrows models will be better then todays, which means the skill investment is deflationary
This is just wrong. A) It doesn’t promise improvement B) Even if it does improve, that doesn’t say anything about skill investment. Maybe its improvements amplify human skill just as they have so far.
Either the business makes a profit before it gets swept away, or it doesn't. This should be your goal: make money before your business dies. If you do that, you succeeded. Businesses are always ephemeral.
This is very well put. I think this platform can be useful but I doubt it can be something as big as the think it will be. At the end of the day it’s just storing some info with your data. I guess they are trying to be the next GitHub (and clearly have the experience :)). I doubt that success can be replicated today with this idea, even with $60 mil to burn
XorNot|19 days ago
rgbrenner|19 days ago
doesn’t that presume no value is being delivered by current models?
I can understand applying this logic to building a startup that solves today’s ai shortcomings… but value delivered today is still valuable even if it becomes more effective tomorrow.
unknown|19 days ago
[deleted]
ljm|19 days ago
The problem is that so many of these things are AI instructing AI and my trust rating for vibe coded tools is zero. It's become a point of pride for the human to be taken out of the loop, and the one thing that isn't recorded is the transcript that produced the slop.
I mean, you have the creator of openclaw saying he doesn't read code at all, he just generates it. That is not software engineering or development, it's brogrammer trash.
IMTDb|19 days ago
On the other hand, deeply understanding how models work and where they fall short, how to set up, organize, and maintain context, and which tools and workflows support that tends to last much longer. When something like the “Ralph loop” blows up on social media (and dies just as fast), the interesting question is: what problem was it trying to solve, and how did it do it differently from alternatives? Thinking through those problems is like training a muscle, and that muscle stays useful even as the underlying technology evolves.
SpaceManNabs|19 days ago
I kinda regret going through the SeLU paper lol back in the late 2010s.
frogperson|19 days ago
srcreigh|19 days ago
This is just wrong. A) It doesn’t promise improvement B) Even if it does improve, that doesn’t say anything about skill investment. Maybe its improvements amplify human skill just as they have so far.
direwolf20|19 days ago
BloondAndDoom|19 days ago
heliumtera|19 days ago
hsbauauvhabzb|19 days ago