How much must one tie their self-worth to a chatbot to debase themselves like that? To think that a winner in the arms and intelligence race of animal kingdom, a member of the species that made this chatbot, would put down themselves like that in the defense of the thoughtless silicon is absolutely laughable and depressing at the same time.

I'm merely pointing out the logical fallacy of thinking complex systems can't arise from simpler components in an obtuse fashion. Ants are stupid individually, yet they're able to create giant structures in the wild. Hating on AI and calling it next word prediction isn't going to save anyone's jobs. Organizing will. Voting will.

You don't even know what "main character syndrome" is.

I heard that a year ago, I guess we still need to wait a bit more. Thought agents were fast!

LLM proponents always use some language like "these old, stuck up dinosaurs with their manual labor vs us cool smart kids with automated labor", but they forget one thing - with automated labor the performance and cost difference was easily measurable in favor of the automation. With LLMs it's neither measurable nor visible (no better software, no faster delivery overall in the industry), and the costs are pretty bad. Besides personal anecdotes of someone toiling away at yet another AI harness project on GitHub.

Right now, to get some good results from AI and save time, you will have to spend a lot of tokens and money. Maybe in the future, the things will get better, I don't know.

Saving money is the wrong reason to use AI now. AI is expensive if you want good results.

But what AI is good for, is it allows you to build fast.

Also, I don't see everything being automated. To get good results you have to drive the AI.

The factories still have workers supervising the process and doing some high value manual processes even if most of the production is done by machines.

Well, since the fundamental underlying structure is still the same, yes.

It's not exactly what it is; they now model an incredibly complex markov process, and harnesses that control how that thinking is done.

Is this any different than how a PM gets a programmer to work on a project? They think, then they deliver. If given more time, maybe they deliver something better. Maybe they consult some text and try to apply a design pattern.

The LLM in this use case is perfect because almost everything involved is text based, and the model is able to take in all the expressive that is language.

> Is this any different than how a PM gets a programmer to work on a project?

Yes, it's very different. You seem to be suggesting that the current frontier LLMs, when tied to their tools and harnesses, have emergent properties that are similar to human consciousness. If you truly believe that, I'm not sure how to have a productive discussion here.

I think they have the capabilities to execute a well defined plan. If you truly don't believe that, then you I suspect any work you do as a programmer will not survive the coming changes.

It's not just that, but the core is just that, even with reasoning models. Harness can only get you closer to the good result, but can't save you from every pitfall. As for PM analogy - don't forget that models don't learn and keep doing same stupid stuff they were doing a month ago.

I would suggest you examine current harness memory persistence. Any reprimand you give your model will be remembered, in the same way a puppy that has a bad social experience will become more shy.

They will not save you from every pitfall, but that isn't the point; engineers walk into pitfalls all the time. This can get you in, and out, much much quicker.

Agents are perfectly capable of learning. Why would the model need to learn? The harness and tooling are all that matter.

They aren't, it's just populating context window until "memory" is pushed out.

But its not useful because even humans are like that - a bunch of neurons slapped together. Overall a tired analogy that is more suited to stay in 2024 where it belongs. Right now it is clear that it is _much_ more than a statistical model semantically. It is misleading to claim it is _just_ that just like a human is _just_ a statistical model.

Neurons? Go lower. Just atoms. Dumb, senseless atoms.

Not only we are looping, we are also adding layers of Idiocracy upon each loop. I kinda envy retired software engineers now, they can just grab the popcorn and watch this dumpster fire without a worry.

Gary Tan building something AI-related on top of your work is an anti-flex.

I can’t take anyone who puts probability on events like that seriously. How would you even explain the calculations behind that with a straight face?

Coding is anything but “easily” verifiable.

It's extremely verifiable. The reinforcement finetuning strategy I'm referring to involves LLM creating coding tasks with an expected output, implementing the code, and then having a compiler (or interpreter in the case of languages like python) succeed or fail to run the code. Then compare the output to expected output. The verification process (run interpreter + run test) can be done in seconds. One can generate millions of datasets like this for free and there is extensive research showing with the right policy, an agent will be able to learn to reason - first as good as human, and in many cases superior to a human.

For basic primitives with known output it’s verifiable, but as long as you’re dealing with real systems with tons of inputs and side effects this no longer holds true.

> research showing with the right policy, Rest of the owl.

This article has all the correct conclusions and solutions based on one assumption that doesn’t have any hold in reality - that someone would be insane enough to allow direct DB access to an AI agent.

someone is potentially insane enough it would seem: https://news.ycombinator.com/item?id=47911579