I think having a codebase that can use any harness is the optimal setup. I often switch between harnesses and models all the time at work. We have our state/spec stored in git, so we can pack it up on friday and start fresh on monday. Or more commonly, using gemini/opus to create rich specifications and using Luna to implement them. Then switching again for reviews etc.
When done properly, yes. But AI agents are doing a lot of data logic in the frontend that should be in the backend behind secure api's. When this happens, the frontend becomes a massive risk.
I was originally a Flash developer, when that died when Steve Jobs banned it from iPhones, I pivoted to frontend. I got to see web evolve over the next decade or so and loved every part of it.
Now, still as a frontend engineer albeit much more senior. The writing is on the wall, again. It's time to reskill for AI.
It's not bad news.
We as experienced engineers are needed to help build in the guardrails, frameworks, the new tooling, etc. There is a lot of work that is needed that requires actual engineers with technical experience.
Like the early internet, I see this as a new beginning and we get to decide how it evolves.
I think that is the nature of being a software engineer, even before AI you had to keep learning new libraries/SDKs/APIs/languages to keep up with the industry trend. I think it will remain the same in the age of AI, good engineers will adapt.
IMO the reskilling is not that hard. Leetcode and applying for jobs has been way harder. In fact, the AI reskilling has been a boon for me. Suddenly I'm able to get a job way more easily and get to call myself a senior, all of a sudden. And the fact that I'm a generalist is really playing to my strength for the first time [1].
So yea, I really welcome the change.
[1]
What I mean with being a generalist:
Did iOS dev as a student, web dev bootcamp instructor, teaching assistant (programming), teaching fellow (programming), found some SQL injections + docker vulnerabilities + replicated rowhammer with WebGL, did a lot of full-stack engineering (5 years), did a UX bootcamp, studied 2 bachelors + 1 masters + research master which is 2 years (psych, business, CS and game-design). Each full-stack engineering job was in a different language (C#, Java, JavaScript, TypeScript, Pharo/Smalltalk, currently JS & Python).
>And the fact that I'm a generalist is really playing to my strength for the first time
I was just speaking with my wife about this a few days ago, that I am actually happy that I didn't specialize in one specific discipline, or rather spend my time around learning a little bit of everything, which now gives me the foothold into any direction that I want to go.
I'm just curious, what do you think that looks like for you and for others? AI is not so much a skill as it is an interface, and one that is quite possibly one of the easiest to use that we've ever created. It is an interface for information, essentially. It's the skills that you come to the table with that determine the results that you're going to get back, and whether they're going to be quality or slop.
It sounds like what you're describing is similar to this new course that Theo Browne (ugh) and Kent C Dodds (double ugh) are launching called "mega.dev", and it appears that it's essentially just talking about the systems and tooling that's going to go into utilizing agents, and code is no longer even a topic.
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