It's the same claim like bitcoin. Many would say that if they bought bitcoin, then they would've had a massive return. But the bitcoin price is the way that it is because of people not buying early. There's no guarantee that the price would've moved the same if you bought earlier.

Figured I'd just give some food for thought to hopefully make you feel better.

I call it "re-algorithming" your data. Basically you're not happy with the algorithm of a product, then you use it's data and introduce your own.

It also seems like folks are tired of the youtube algorithm and want something different instead? I'm willing to bet that many sites and apps have this opportunity in them.

But why do we anchor naming to someone who made something popular? Why do we upvote similar names too? For example, openclaw became popular then there were similar projects with the "claw" naming.

It's just a very fascinating psychological thing to do, and I don't understand why. Because if you copy part of a name, generally you'll never get more popular than the original name. So why do this?

* Code is meant for reading, not writing. You will spend more time reading code so optimise for reading. I got this advice from a previous Principal Engineer and it has stuck with me ever since. Yes you can generate slop, but you have to be able to read it. Otherwise, for me, sloppy code becomes the context for the next feature, then the feature after that, until the codebase is really messy and brittle.

* The other principle I work with is that more code means more bugs and more recently security vulnerabilities. We're churning more and more code, and looking less at the code. Another agent can very easily find vulnerabilities in your website or app. Sometimes I feel that this is inevitable with the way folks are doing things

* Then my most recent principle. Always add value, know your customer, know your product, know your business metrics. I'm seeing more and more the statistic of "I merged X PRs in a day/week". Just like before AI, these things are very superficial. How many tokens did it cost? How many dollars did it cost? How much revenue did it earn? How many customers are adopting the feature? This applies to internal and customer facing features. The amount of vanity metrics that AI can create now is astounding, and people are still falling for it regularly.

* In a new project, I start with a few commits to see if the agent understands my desired architecture. Once that passes, everything else falls into place and slowly trust the code more and more. Sometimes when I need to add a complex feature, I would sit more with the design than the implementation, then I would skim the generated code to see if it's adjusting accordingly.

Open weight models are catching up to the frontier. It also seems like frontier models reached some limit, whether this is capex related, business model related or something else. Nobody knows but it's happening to all frontier labs it seems.

It's been fear mongered many times that ai will kill us all. But this time, a person with around 2-3 months of tenure at anthropic managed to go viral with no previous social media account activity, gets picked up by all news outlets and kicks off yet another round of fear mongering.

So the real questions to ask

* is all of this to increase the valuations before IPO?

* what is the real barrier to entry for open weight models to be used by the public?

* what is the Financials of these companies showing that's causing this outcry on safety?

People need to think really critically about the things happening around them. Don't only just look at the face value of what's being presented here.

I think the worst thing we can do is have loyalty towards models. I used to be loyal towards Claude, and my viewpoint changed dramatically when I used codex.

I highly recommend that if you are someone who only used one model so far, that you really give another model a shot and see how it goes. It's very eye opening and gives you a more holistic perspective.

Vendor locking is a big problem when it comes to models, and I hope the software world doesn't do this blindly.

* People assume that Uber is a big brand in Nigeria and Uganda, this might not be true

* People assume that Uber is popular in these countries, this might also not be true

Then all the mythos warnings happened, then the fable access issues started.

At that point, I just wanted to try something else because I didn't want to feel vendor locked by anthropic.

Then I tried codex on the $100 plan. I've never been happier. It does everything claude can do, without any token limit issues or anything for me.

I don't see myself going back to claude anytime soon. I also highly recommend folks to not vendor lock yourself to a model. You need to try multiple models for yourself and see what's possible. There should be zero loyalty to any frontier lab because of how fast things change and how volatile model access, uptime and customer experience can be with these things.

This is a severe misunderstanding in how LLMs work..

I don't know how this got on my front page....

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.