Programming

No apps, no code, just intent and execution.

So the only problems you’re left with are:

• Making a precise description of what you want, at high and low levels of detail with consistent terminology
• Verifying that the system is behaving as you expect, by exercising specific parts of it in isolation
• The ability to make small incremental steps from one complete working state to the next complete working state, so you don’t get stuck by painting yourself into a corner

Problems which… code is much better than English at handling.

And always will be.

Almost like there’s a reason code exists other than just “Idk let’s make it hard so normies can’t do it mwahaha”.

Okay, this is fun, but it's time for an old programmer to yell at the cloud, a little bit:

The cost per AI request is not trending toward zero.

Current ludicrous costs are subsidized by money from gullible investors.

The cost model whole house of cards desperately depends on the poorly supported belief that the costs will rocket downward due to some future incredible discovery very very soon.

We're watching an edurance test between irrational investors and the stubborn boring nearly completely spent tail end of Moore's law.

My money is in a mattress waiting to buy a ten pack of discount GPU chips.

Hallucinating a new unpredictable result every time will never make any sense for work that even slightly matters.

But, this test still super fucking cool. I can think of half a dozen novel valuable ways to apply this for real world use. Of course, the reason I can think of those is because I'm an actual expert in computers.

Finally - I keep noticing that the biggest AI apologists I meet tend to be people who aren't experts in computers, and are tired of their "million dollar" secret idea being ignored by actual computer experts.

I think it is great that the barrier of entry is going down for building each unique million dollar idea.

For the ideas that turn out to actually be market viable, I look forward to collaborating with some folks in exchange for hard cash, after the AI runs out of lucky guesses.

If we can't make an equitable deal, I look forward to spending a few weeks catching up to their AI start-up proof-of-concept, and then spending 5 years courting their customers to my new solution using hard work and hard earned decades of expert knowledge.

This cool AI stuff does change things, but it changes things far less than the tech bros hope you will believe.

The future is here! And it costs $10-$50 per 1000 HTTP requests.

The conclusion of this experiment is objectively wrong when generalized. At work, to my disappointment, we have been trying for years to make this work, and it has been failure after failure (and I wish we'd just stop, but eventually we moved to more useful stuff like building tools adjacent to the problem, which is honestly the only reason I stuck around).

There are a couple reasons why this problem cannot succeed:

1. The outputs of LLMs are nondeterministic. Most problems require determinism. For example, REST API standards require idempotency from some kinds of requests, and a LLM without a fixed seed and a temperature of 0 will return different responses at least some of the time.
2. Most real-world problems are not simple input-output machines. When calling, let's say for example, an API to post a message to Lemmy, that endpoint does a lot of work. It needs to store the message in the darabase, federate the message, and verify that the message is safe. It also needs to validate the user's credential before all of this, and it needs to record telemetry for observability purposes. LLMs are not able to do all this. They might, if you're really lucky, be able to generate code that does this, but a single LLM call can't do it by itself.
3. Some real world problems operate on unbounded input sizes. Context sizes are constrained and as currently designed cannot handle unbounded inputs. See signal processing for an example of this, and for an example of a problem a LLM cannot solve because it cannot receive the input.
4. LLM outputs cannot be deterministically improved. You can make changes to prompts and so on but the output will not monotonically improve when doing this. Improving one result often means sacrificing another result.
5. The kinds of models you want to run are not in your control. Using Claude? K Anthropic updated the model and now your outputs all changed and you need to update your prompts again. This fucked us over many times.

The list keeps going on. My suggestion? Just don't. You'll spend less time implementing the thing than trying to get an LLM to do it. You'll save operating expenses. You'll be less of an asshole.

I like writing code myself, its a process I enjoy. If the LLM write it for me, then I would only do the worse part of the job: debugging. Also for many people let the Ai write code means less understanding. Otherwise you could have written it yourself. However there are things when the Ai is helpful, especially for writing tests in a restrictive language such as Rust. People forget that writing the code is one part of the job, the other is to depend on it, debug and build other stuff on top.

But you can do all this anyways, this isn't new or ground breaking.

you can load up Claude Code an a completely empty directory and tell it to build something and it will do it. it'll do it slowly and most of the time incorrectly but it'll eventually build "something" that will sort of work. Unless i'm still waiting for my coffee to kick in and I'm missing something here - companies already do this. Hell a lot of my current clients currently do this. no code, nothing to base anything off, just tells Claude an idea for a project and to build it.

Kinda reminds me of this: I built the most expensive CPU ever! (Every instruction is a prompt)