I'm currently building my own library, and I have absolutely no need to use a highly complex library burdened with legacy baggage. Furthermore, I believe this is an era of creation — we can
reference projects like Spec Kit and OpenClaw, but more importantly, we should explore and experiment on our own.
Trust your own taste. The best taste often comes from individuals, not organizations.
Thanks for this, real food for thought. My immediate reaction is two-fold.
One, I don't think see how the models we have today really solve the "Simplification" problem like frameworks do. I think LLMs are a real multiplier to the top engineers. But I feel like the mid-low level engineers will struggle to level up in the new age of engineering.
The real danger is that no one seems to be interested in solving this. How do we develop prompt techniques and train new developers? For example, I come from the 3rd world and only now starting to incorporate models into my development workflow. If I want to use frameworks, I immediately get access to documentation on what to do and lots of example of how to use them. With LLMs though, only I get from the providers are toy examples. And even the larger ecosystem seems to be lacking in documentation. My biggest fear is that the divide between the haves and have-nots will only grow wider.
The second counterpoint is that I'm not convinced that models can self-improve, at least at the moment. Frameworks would evolve over time, improving either features, architecture or ease of use. How will LLMs do this? Will we be stuck with the architecture the model was trained on? In short, are we sure LLMs can do "think about" the problems in what they generate, research and improve their output?
And though you say engineering is about solving the problems you have right now, rather than what we might encounter tomorrow, we still have to prepare for the problems of tomorrow. How will this be done?
On the simplification gap: you're right that LLMs currently reward those who already have strong fundamentals. Probably this is more of an ecosystem maturity issue than a permanent reality, but I'm skeptical myself. Early frameworks also lacked good documentation and examples, the community built those over time. We're already seeing prompt engineering patterns, structured guides, and better tooling emerge. On self-improvement: I'd frame it slightly differently. LLMs don't need to self-improve the way frameworks iterate, the ecosystem around them does. Better fine-tuning, RAG pipelines, evaluation tools, agents, and each new model generation all represent iteration. The improvement cycle is different from traditional frameworks, but it's arguably faster.
On preparing for tomorrow: this is the timeless engineering challenge, and LLMs don't change the principle, just the tools. Strong fundamentals, adaptability, and continuous learning have always been the answer.
I completely agree with your point of view.
I'm currently building my own library, and I have absolutely no need to use a highly complex library burdened with legacy baggage. Furthermore, I believe this is an era of creation — we can
reference projects like Spec Kit and OpenClaw, but more importantly, we should explore and experiment on our own.
Trust your own taste. The best taste often comes from individuals, not organizations.
Thanks for this, real food for thought. My immediate reaction is two-fold.
One, I don't think see how the models we have today really solve the "Simplification" problem like frameworks do. I think LLMs are a real multiplier to the top engineers. But I feel like the mid-low level engineers will struggle to level up in the new age of engineering.
The real danger is that no one seems to be interested in solving this. How do we develop prompt techniques and train new developers? For example, I come from the 3rd world and only now starting to incorporate models into my development workflow. If I want to use frameworks, I immediately get access to documentation on what to do and lots of example of how to use them. With LLMs though, only I get from the providers are toy examples. And even the larger ecosystem seems to be lacking in documentation. My biggest fear is that the divide between the haves and have-nots will only grow wider.
The second counterpoint is that I'm not convinced that models can self-improve, at least at the moment. Frameworks would evolve over time, improving either features, architecture or ease of use. How will LLMs do this? Will we be stuck with the architecture the model was trained on? In short, are we sure LLMs can do "think about" the problems in what they generate, research and improve their output?
And though you say engineering is about solving the problems you have right now, rather than what we might encounter tomorrow, we still have to prepare for the problems of tomorrow. How will this be done?
Great points, a few thoughts:
On the simplification gap: you're right that LLMs currently reward those who already have strong fundamentals. Probably this is more of an ecosystem maturity issue than a permanent reality, but I'm skeptical myself. Early frameworks also lacked good documentation and examples, the community built those over time. We're already seeing prompt engineering patterns, structured guides, and better tooling emerge. On self-improvement: I'd frame it slightly differently. LLMs don't need to self-improve the way frameworks iterate, the ecosystem around them does. Better fine-tuning, RAG pipelines, evaluation tools, agents, and each new model generation all represent iteration. The improvement cycle is different from traditional frameworks, but it's arguably faster.
On preparing for tomorrow: this is the timeless engineering challenge, and LLMs don't change the principle, just the tools. Strong fundamentals, adaptability, and continuous learning have always been the answer.
Thanks for your comment btw, appreciated!