How does one keep up with all this change? I wish we could fast-forward like 2-3 years to see if an actual winner has landed by then. I feel like at that point there will be THE tool, with no one thinking twice about using anything else.
Obsession?
Having to tack on top of that 2-4h of work per day is not normal, and again, it's probably unhealthy.
Its a bit like the media cycle. The more jacked in you are, the more behind you feel. I’m less certain there will be winners as much as losers, but for sure the time investment on staying up to date on these things will not pay dividends to the average hn reader
This should be written on the coffin of full stack development.
assuming it doesn't all implode due to a lack of profitability, it should be obvious
That said, code+git+agent is only acceptable way for technical staff to interact with AI. Tools with sparkles button can go to hell.
https://a16z.com/llmflation-llm-inference-cost/ https://openrouter.ai/anthropic/claude-sonnet-4
We don't actually need a winner, we need 2-3-4 big, mature commercial contenders for the state of the art stuff, and 2-3-4 big, mature Open Source/open weights models that can be run on decent consumer hardware at near real-time speeds, and we're all set.
Sure, there will probably be a long tail, but the average programmer probably won't care much about those, just like they don't care about Erlang, D, MoonScript, etc.
As Heraclitus said "The only constant in life is change"
(and maybe Emacs)
Figuring out how to stay sane while staying abreast of developments will be a key skill to cultivate.
I’m pretty skeptical there will be a single model with a defensible moat TBH. Like cloud compute, there is both economy of scale and room for multiple vendors (not least because bigco’s want multiple competing bids).
1. Where they can be used as autocompletion in an IDE at speeds comparable with Intellisense 2. And where they're good enough to generate most code reliably, while using a local LLM 3. While running on hardware costing in total max 2000€ 4. And definitely with just a few "standard" pre-configured Open Source/open weights LLMs where I don't have to become an LLM engineer to figure out the million knobs
I have no clue how Intellisense works behind the scenes, yet I use it every day. Same story here.
Given how much better the bleeding edge models are now than 6 months ago, as long as any model is getting smarter I don’t see stagnation as a possibility. If Gemini starts being better at coding than Claude, you’re gonna switch over if your livelihood is dependent on it.
A look at fandom wikis is humbling. People will persist and go very deep into stuff they care about.
In this case: Read a lot, try to build a lot, learn, learn from mistakes, compare.
Oh, but it is.
Imagine you were then, back in those days. A few years after VHS won, you couldn't find your favorite movies on Betamax. There was a lot more hardware, and cheaper, available, for VHS.
Mass adoption largely wins out over almost everything.
Case in point from software: Visual Basic, PHP, Javascript, Python (though Python is slightly more technically sound than the other ones), early MySQL, MongoDB, early Windows, early Android.