Sure, is there anyone nostalgic for debugging bash files by hand?
Any sense of grief for writing C++ template headers, with all boilerplate?
Hmm, does anyone like manually re-writing makefiles these days?
I suspect the enthusiasts of coding craft will struggle to maintain their wonder
after ~4h deep in any of these magical adventures, which surely
involve inventing ad-hoc duct tape and novel, never-before-seen algorithms.
When you make forums you compete with forum aggregators
with more history and social clout, i.e. a reddit replacement.
If someone made better Reddit, it could have a chance, however
reddit-type aggregators crypronite is hosting their own videos/media,
which makes it prohibitively expensive for small companies without
ads and sponsored posts which in turn make them less of "forum aggregator" and
more like facebook social feeds: mainly video/image based dopamine rides
instead of actual knowledge worth keeping.
People are allowed to make niche websites. We can disaggregate. It's probably better for the web if we did disaggregate given how many of the aggregators such as Wikia and Reddit and more have shown the tragedy of the commons that an aggregator over-specialize in being ad companies (or other questionable business models) over the long term rather than directly continuing to benefit the communities that trusted them as hosts in the first place, and often to the detriment and diaspora of those vary communities as they lose trust piecemeal and have fewer chances to "lift and shift" the entire community as a whole out of the aggregator in the same semi-easy manner they were aggregated in the first place.
The "social clout" advantage the giant aggregators have gained they are often working to lose a few communities at a time. Building a niche community site outside of the aggregators isn't competing with them in the same way any longer.
Maybe it is a time for building smaller niche sites again?
This seems useful beyond agents.
It will save tons of traffic for scripts, text browsers, low-bandwidth connections,etc
markdown is incredibly compact and easy to parse.
The code written by AI in most cases is throwaway code to be improved/refined later.
Its likely to be large, verbose and bloated. The design of some agents
have "simplify/refactor" as final step to remedy this, but typically
your average vibe coder will be satisfied that the code just compiles/passes the minimal tests. Lines of code are easy to grow.
If you refine the AI code with iterative back-and-forth questions,
the AI can be forced to write much more compact or elegant version
in principle, but you can't apply this to most large systems without breaking something,
as AI doesn't have context of what is actually changing:
so e.g. an isolated function can be improved easily, but AI can't handle when complexity of abstraction stacks and interfacing multiple systems, typically because it confuses states
where global context is altered.
I've been working to overcome this exact problem. I believe it's fully tractable. With proper deterministic tooling, the right words in context to anchor latent space, and a pilot with the software design skills to do it themselves, AI can help the pilot write and iterate upon properly-designed code faster than typing and using a traditional IDE. And along the way, it serves as a better rubber duck (but worse than a skilled pair programmer).
the article misses log-math and log-log-math which
would use 64-bit (d)oubles are exponents in 10^d and 10^(10^d) respectively,
which allows far higher range of possible values but somewhat more
awkward math operations(addition/subtraction), though much more practical & faster to compute.
There is a point in there, long-range analysis and debugging without AI
is much harder, AI spots lots of non-obvious stuff very fast.
If we consider "spotting non-obvious flaws" a skill, this will
atrophy as beginners will learn to use AI to scan code for
flaws,it is effective but doesn't teach anything, reading long blocks
of code and mentally simulating it is a incredibly valuable skill
and it will find stuff AI misses(something that is too complex, e.g. nested/recursive control flow,async
and co-routines/threads interacting,etc), AI goes for obvious stuff first and
has to be manually pointed to "identify flaws, focusing on X".
Quntity of exercise cannot build quality.
Its self-selecting the people who can handle running and injuries
better so they adapt. The rest get injured enough to stop.
II don't think trying to "push it to using 100% of potential" is
worth any long-term health risk, especially with no long-term reward:
so you win X race/competition once and then what?
So now you're on a Wheaties box and making millions in endorsements, that's what.
Even if it shortens their life, many athletes would take a drug that guarantees athletic success: https://en.wikipedia.org/wiki/Goldman%27s_dilemma The results have been disputed, but the take-away for me is that athletes have a variety of motivations, and the perceived rewards might be very different than what you might find rewarding.
