And vibe coding, if we define it here, is you tell an agent to build software for you. You do not look at the implementation. That, to me, is what separates vibe coding from programming or, let's say, agent-accelerated development.
The other thing I'd say is that most organizations don't know what they want. They don't know how to make it better. They're not bottlenecked on implementation. They're bottlenecked on ideas. They're bottlenecked on vision. They're bottlenecked on taste. And if you don't have those element in excess of your implementational capacity, it doesn't help. So you can make a lot of shitty ideas come true, then what?
buuuut my roadmap is’nt ten times shorter. and I’m definitely not ten times better at deciding what is worth building. teams haven’t started casually shipping a year of product work every month. you can look around and and it’s hard to say what software has gotten meaningfully better in the last year.
I do the research I do the plan I do the implementation I throw the docs out and the next time I need research I just do it from scratch because tokens are cheap and my time is expensive
This took some special care, in particular avoiding any C++ standard library I/O functionality. Current frontier AI cannot handle this detail on their own.
I gave pi a skill for using the search engine SearXNG (which aggregates many search engines together at the same time), and one for calling into a daemon that I wrote that gives it access to read my email and Signal messages, and send-to-self, and send to others only with human confirmation.
Tariffs, and especially the erratic implementation of them, are teaching our allies and enemies alike that the US is no longer a reliable trading partner.
Vibe coding can execute instructions, but deciding what the software should do and why is not automated. Product managers and designers must still do user research, market analysis, and creative brainstorming. In that sense, vibe coding changes the implementation phase more than the planning phase of the product lifecycle.
To some extent, training a model does effectively nothing. They have a model. The thing that Dario is sort of speaking to is the implementation of that model, once trained to then create huge economic growth, huge increases in military capabilities, huge increases in productivity of people, betterment of lives.
What I’m saying is that art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception.
Good tools let the user choose when to switch between implementation and evaluation. When I work with a chatbot, I'm forced to frequently switch between the two modes.
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