Show HN: Why write code if the LLM can just do the thing? (web app experiment)

It will be difficult to incorporate relative access or restrictions to features with respect to users current/known state or actions. Might as well write the entire web app at that point.

When products have limitations, those are usually acceptable to me if I know what they are or if I can find out what the breaking point is.

If the breaking point was me speaking a bit unclearly, I'd speak more clearly. If the breaking point was complex questions, I'd ask simpler ones. If the breaking point is truly random, I simply stop using the service because it's unpredictable and frustrating.

Instead I would like to shift the focus on the benefits of LLM. I know the costs are high, very very very high, but you seem to think that the benefits are also so high measured in time saved. That is the amount of tasks automated are enough to save humans doing similar tasks by miles. If that is what you think I disagree. LLMs have yet to prove them selves with real world application. We are seeing when we actually do measure how much LLMs save work-hours, that it the effects are at best negligible (see e.g. https://news.ycombinator.com/item?id=44522772). Worse, generative AI is disrupting our systems in worse way, where e.g. teachers, peer-reviewers, etc. have to put in a bunch of extra work to verify that the submitted work was actually written by that person, and not simply generated by AI. Just last Friday I read that arXiv will no longer accept submissions unless they have been previously peer-reviewed because they are overwhelmed by AI generated submissions[1].

There are definitely technologies which have saved us time and created a much more efficient system then was previously possible. The loom is a great example of one, I would claim the railway is another, and even the digital calculator for sure. But LLMs, and generative AI more generally are not that. There may be utilities for this technology, but automation and energy/work savings is not one of them.

1: https://blog.arxiv.org/2025/10/31/attention-authors-updated-...

On the one hand, there’s „classical“ software that is developed here and deployed there — if you need a change, you need to go over to the developers, ask for a change & deploy, and thus get the change into your hands. The work of the developers might be LLM-assisted, but that doesn’t change the principle.

The other extreme is what has been described here, where the LLM provides the software „on the fly“.

What I‘m imagining is a software, deployed on a system and provided in the usual way — say, a web application for managing inventory.

Now, you use this software as usual.

However, you can also „meta-use“ the software, as in: you click a special button, which opens a chat interface to an LLM.

But the trick is, you don’t use the LLM to support your use case (as in „Dear LLM, please summarize the inventory“).

Instead, you ask the LLM to extend the software itself, as in: „Dear LLM, please add a function that allows me to export my inventory as CSV“.

The critical part is what happens behind the scenes: the LLM modifies the code, runs quality checks and tests, snapshots the database, applies migrations, and then switches you to a „preview“ of the new feature, on a fresh, dedicated instance, with a copy of all your data.

Once you are happy with the new feature (maybe after some more iterations), you can activate/deploy it for good.

I imagine this could be a promising strategy to turn users into power-users — but there is certainly quite some complexity involved to getting it right. For example, what if the application has multiple users, and two users want to change the application in parallel?

Nevertheless, shipping software together with an embedded virtual developer might be useful.