I use coding agents almost exclusively now and I’m going to say yes and no on this one.

Yes, I think there’s the potential to replace some frameworks that abstract away too many details and make things way too complicated for basic apps. A good example of this are ORMs like SqlAlchemy. Every time I use them I think to myself it would be easier to just write SQL myself, but it would be a tremendous amount of boilerplate. Nowadays though it might be worth it for an agent to just write the SQL for you instead!

On the other hand, you have libraries like Django. Sure, an agent _could_ write you your own web server. But wow would it be a waste of tokens and your projects surface area would be dwarfed by the complexity of just building your own alternative to Django. I can’t see that being the right move for years still.

The pendulum swing described here is real but I think the underlying issue is subtler than "AI vs. no AI."

The actual problem most teams have isn't writing code — it's understanding what the code they already depend on is doing. You can vibe-code a whole app in a weekend, but when one of your 200 transitive dependencies ships a breaking change in a patch release, no amount of AI is going to help you debug why your auth flow suddenly broke.

The skill that's actually becoming more valuable isn't "writing code from scratch" — it's maintaining awareness of the ecosystem you're building on. Knowing when Node ships a security fix that affects your HTTP handling, or when a React minor changes the reconciliation behavior, or when Postgres deprecates a function you use in 50 queries.

That's the boring, unsexy part of engineering that AI doesn't solve and most developers skip until something catches fire.

If the author is this Alain di Chiappari, he works for a telehealth and psychology site:

https://theorg.com/org/unobravo-telehealth-psychology-servic...

It is interesting how many telehealth and crypto people are promoting AI (David Sacks being the finest of all specimens).

The article itself is of course an AI assisted mashup of all propaganda talking points. People using Unobravo should take note.

A framework also gives you someone's expertise in a domain so you don't have to develop that expertise yourself and focus on all the other stuff...

Frameworks help you reduce to the point to irreducible complexity.

Not using a framework means creating and maintaining a new and bad one.

And the AI doesn’t even do that. They repeat and create new complexity

> In my mind, besides the self declared objectives, frameworks solve three problems .. “Simplification” .. Automation .. Labour cost.

I think you are missing Consistency, unless you don't count frameworks that you write as frameworks? There are 100 different ways of solving the same problem, and using a framework--- off the shelf or home made--- creates consistency in the way problems are solved.

This seems even more important with AI, since you lose context on each task, so you need it to live within guardrails and best practices or it will make spaghetti.

LLMs finally deliver on the crochety front end dev's dream of writing everything in vanilla JS. Hallelujah.

There is a fourth reason to use a framework: onboarding.

It does not work much for Django, as every project I saw using it has a different shape, but it works very well for Rails, as all projects share the same structure. However, even for Django, there are some practices that a newcomer to a project should expect to find in the code, because it's Django. So, maybe onboarding on a LLM coded project is just picking the same LLM as all the other developers, making it read the code and learning what kind of prompts the other developers use.

By the way, does anybody mind to share first hand experiences of projects in which every developer is using agents? How do those agents cope with the code of the other agents?

I see Libraries and frameworks as a way to capture knowledge and best practices so it can be shared with other people. So looking wat a LLM/AI does, it looks to me that this would be a perfect fit. Without the dependeny hell, unresolved github issues, need to fork and leaving maintainers. It could be opensource on steroïdes, with far shorter feedbackloops (just working in your IDE).

The main burden I see is validation of the output and getting reproducable results. As with many AI solutions.

What they are basically saying : a framework built up from bash-or-makefile-ground by an LLM, is better than any existing framework. I don't agree. When I use LLMs to generate scripts for me, I often have to adapt them to fit in the bigger picture. The more scripts I have, the more blurred becomes what that framework as a whole stands for. Then to become a usable framework, refactoring is needed, which means the calls to those scripts need rewriting and retesting as well.