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I'm completely fine with it. Currently I work with a set of principles that I try to uphold when working with agents (many of which are existing principles that tries to scale for the volume of code being written).

* 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.

This is about consolidation of power.

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.

It's really disappointing to see social media dismissing gemini so easily.

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.

It feels like people are comparing Uber the brand in their own country and applying that to other countries

* 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

I started using llm's on a $20 claude license, then it became a $100 license.

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.

> LLMs aren’t lazy. They don’t cut corners because a simpler solution feels good enough. If they know how to solve something thoroughly, they will.

This is a severe misunderstanding in how LLMs work..

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

I'm more interested in the business impact of this

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?

Super appreciate you replying to my comment.

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.

> How can a LLM be assigned an emotion as being "proactive"

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!

How can a LLM be assigned an emotion as being "proactive". This is highly misleading to anyone that scans just the headlines.

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.

For me it depends on who you listen to

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.

it's a net positive for robinhood, not the trader necessarily

Product market fit and profitability are two different things.

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.

So the article doesn't mention how the individual should prepare, but rather that government should prepare. Are individuals just that powerless against these perceived outcomes with AI?

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

Why would you replace an existing codebase like this instead of forking the repo instead and then making the changes?