So you spend billions of dollars training the model, only for it to be used in the US.

Then interesting to see where most of anthropic revenue comes from. If it's the US then they're fine but if it's global then they'll see a drop in revenue?

Then add to this decision, companies are going to significantly reduce their token spend.

So what does all of this mean for their IPO?

I think I understand where you're coming from now. What confused me is that the post is written in a way that it seemed like what Fable was doing was actually better. Maybe I should've looked at post as an exploratory post on Fable instead.

I can't edit my post, this is wrong. "Proactive" is defined as a behaviour instead of an emotion.

Thanks to everyone pointing it out!

What actually happened is that the user started a prompt, and Claude took $12 worth of tokens to resolve the issue. How it did so was basically looping until it got to the answer

How is this proactive? It's literally being token greedy and maximising revenue for the LLM owner. People really need to be putting on business hats at this stage, because we are being lead to believe that "more tokens = better". It is not, there are efficient ways to solve a problem and there are inefficient ways to do so too.

Each problem solved incurs a cost, and is expected to yield an ROI at some point. This is how we should be viewing things now.

If you're following a bunch of people who are from LLM labs, you're going to be more incentivised to tokenmaxx because it's in the Lab's best interest tonget you to behave that way.

Practically, many companies aren't labs with endless runway. Companies hopefully follow a PnL model. And when you look at things with that lens, many of the times the LLM use case falls apart.

You're seeing a bunch of companies starting to realise that tokenmaxing yields very little ROI.

Even the LLM labs, the guy that spent $1+mil tokens has nothing to show for it in terms of revenue to the company. And you have to keep sinking that much into AI for ... "features".

There are some good use cases for AI. I ended up with a positive ROI on a greenfield project myself, albeit on a small scale.

The way that AI has been making people have totally irrational decisions on executive, pure business and technical standpoints is simply mindblowing. I don't understand how people can't take a step back and see what's actually happening from a macro perspective.

Arguably product market fit was fond last November already. I don't think agents were the turning point that caused this.

Profitability, not yet. For me, it depends on whether companies are seeing a positive ROI from their ai investments. This website is skewed more towards big tech companies, but everyone that's not big tech needs to see positive ROI with using ai tools. Short term might see a profit, but medium to long term we still need to wait a bit.

My opinion is that AI can be a force for good, but why is everything so aggressively framed as a class war? Why must such a path be taken?

I wonder if this is how things were during the industrial revolution as well

Like the fine means nothing to meta, and they'll still be the beneficiaries of their infringement.

In this current state, you really just need to have enough money to bypass this lawsuit and be on your way.

* How much CPU/token usage does openclaw users use in general? Similarly, how much does high volume openclaw users use vs "normal" claude high volume users?

* Are there political elements we can't see that's affecting this? OpenClaw and anthropic doesn't have a good history in general and this is just a continuation of that?

Something I don't understand, there's a lot of complaints yet people are reluctant to stop using the service? Are folks already vendor locked or is it a case of "well, this doesn't seem to affect me?" The consumer behaviour of these complaints is very interesting.

I've never had such an obsession to a platform or an activity as this. Some might say this is unhealthy, but I admire folks who can reach this level of obsession in their craft. It's just a joy to read about for me

* you can't blame ai if your production token is on the same machine as the staging/ development environment?

* you can't blame ai if you didn't know that the production api token gave access to all apis.

Like if this is the level of operational thinking going into this app, then I'm sorry no ai agent or platform can prevent this from happening.

Everything else in this "post mortem" is performative at best.

The only real question one could ask railway is why do they have api endpoints that can affect production available? Maybe these should only be performed on the platform itself instead?

How could this have happened? Well, the code was shipped but no customer was running it in production.

Maybe one pointer for others is that people are genuinely curious about learning new things, but as experts we choose not to engage these types of posts, why is that?

OP, in your case you need to move from theory into actually building your own agent and make it do things. Start by solving small problems and then make them more complex over time.

They just seemed to fix their technology choices and got the benefits.

There's existing golang versions of jsonata, so this could have been achieved with those libraries too in theory. There's nothing written about why the existing libraries aren't good enough and why a new one needed to be written. Usually you need to do some due diligence in this area, but no mentions of it in this post

In order to measure the real efficiency, gnata should've been benchmarked against the existing golang libraries. For all we know, the ai implementation is much slower.

The benchmarks in the blog are also weird. The measurement is done within the app, but you're meant to measure the calls within the library itself (e.g calling the js version in its isolated benchmark vs go version in its isolated benchmark). So you don't actually know what the actual performance of the ai written version is?

The only benefit, again, is that they fixed their existing bad technology choice, and based on what is observed, with a lesser bad technology choice. Then it's layered with clickbait marketing titles for others to read.

I'll probably need to expect more of these types of posts in the future.

There didn't seem to be any marketing for it. Like I can't even remember an ad for it or any content creator type of person pushing Sora actively.

To get access to Sora I believe you needed to be on a paid plan?

It's really difficult to get user generated content going when it's behind a paywall.

It's also hard to tell if this means that openai is in trouble, or if this is just a badly managed product that deserved to be killed. With the negative sentiment on openai, folks might think the former.

Only a few get lucky with funding, only a few have a profitable business.

On the site itself, the opinion seems to be that stackoverflow is not dead and it's okay if there's reduced traffic and questions to the site.

Yet off site, the opinion is that stackoverflow is dead and added to that, the community isn't very friendly anymore.

It's just really interesting to see the perspectives between the two.

LOC will never be a good metric of software engineering. Why do we keep accepting this?

I can generate 1 million LOC if I really wanted to.

As long as LOC is the main metric for these setups, they will never be successful.