Schedule tasks on the web

I’d very like to see such demonstration. Where someone hands over a department to an agent and let it makes decisions.

> This convo now turns into the "AI is not profitable and this is a house of cards" theme?

Where did I say that? I didn’t even mention money, just the broader resource term. A lot of business are mostly running experiments if the current set of tooling can match the marketing (or the hype). They’re not building datacenters or running AI labs. Such experiments can’t run forever.

I'm sorry, but only someone who never maintained software long-term would say something like this. The further along you are in development, the magnitude of costs related to changing that increases, maybe even exponentially.

Correct the design before you even wrote code, might be 100x cheaper (or even 1000x) than changing that design 2 years later, after you've stored TBs of data in some format because of that decision, and lots of other parts of the company/product/project depends on those choices you made earlier.

You can't just pile on code on top of code, say "code is cheap" and hope for the best, it's just not feasible to run a project long-term that way, and I think if you had the experience of maintaining something long-term, you'd realize how this sounds.

The easiest part of "software engineering" is "writing code", and today "writing code" is even easier. But the hardest parts, actually designing, thinking and maintaining, remains the same as before, although some parts are easier, others are harder.

Don't get me wrong, I'm on the "agentic coding" train as much as everyone else, probably haven't written/edited a code by myself for a year at this point, but it's important to be realistic about what it actually takes to produce "worthwhile software", not just slop out patchy and hacky code.

Humans learn.

Agents regurgitate training data (and quality training data is increasingly hard to come by).

Moreover, humans learn (somewhat) intangible aspects: human expectations, contracts, business requirements, laws, user case studies etc.

> Verifiable domain performance SCALES, we have no reason to expect that this scaling will stop.

Yes, yes we have reasons to expect that. And even if growth continues, a nearly flat logarithmic scale is just as useless as no growth at all.

For a year now all the amazing "breakthrough" models have been showing little progress (comparatively). To the point that all providers have been mercilessly cheating with their graphs and benchmarks.

Awesome you've backed this up with real literature. Let's just include this for now to easily refute your argument which I don't know where it comes from: https://transformer-circuits.pub/2022/in-context-learning-an...

> It also doesn't help that every new context is a new dawn with no knowledge if things past.

Absolutely true that it doesn't help but: agents like Claude have access to older sessions, they can grok impressive amounts of data via tool use, they can compose agents into hierarchical systems that effectively have much larger context lengths at the expense of cost and coordination which needs improvement. Again this is a temporary and already partially solved limitation

> A bunch of Memento guys directing a bunch of other Memento guys don't make a robust system, or a system that learns, or a system that maintains and retains things like business context.

I think you are not understanding: hierarchical agents have long term memory maintained by higher level agents in the hierarchy, it's the whole point. It's annoying to reset model context, but yet you have a knowledge base of the business context persisted and it can grok it...

> We've heard this mantra for quite some time now.

yes you have, and it has held true and will continue to hold true. Have you read the literature on scaling laws? Do you follow benchmark progression? Do you know how RL works? If you do I don't think you will have this opinion.

> yap yap yap. The result is anything but your rosy description of these amazing reasoning learning systems that handle business context.

Well that's fine to call an entire body of literature "yap" but don't pretend like you have some intelligible argument, I don't see you backing up any argument you have here with any evidence, unlike the multitude of sources I have provided to you.

Do you argue things have not improved in the last year with reasoning systems? If so I would really love to hear the evidence for this.

That's your bar for understanding business context? I thought we were talking about what you actually said which is: understanding business context. If I brainstorm about a feature it will be able to pull the compendium of knowledge for the business (reports, previous launches, infrastructure, an understanding of the problem space, industry, company strategy). That's business context.

> Where did I say that? I didn’t even mention money, just the broader resource term. A lot of business are mostly running experiments if the current set of tooling can match the marketing (or the hype). They’re not building datacenters or running AI labs. Such experiments can’t run forever.

I misunderstood you then, I wasn't sure what point you were trying to make. Is your point "companies are trying to cajole Claude to do X and it doesn't work and hasn't for the last year so they are giving up"? If so I think that is a wonderful opportunity for people that understand the nuance of these systems and the concept of timing.