Sevii

I assume that all labs are training on any data they can get their hands on.

I enjoy getting into the zone while listening to synthwave and banging out code. But I'm not going to ignore that I can code most programs 100x as fast using claude code. I don't enjoy coding so much that I will spend 8 hours getting a side project together when I could get more done in 30 minutes with AI.

I had the same issues a year an a half ago. You just have to get over it. It sucks and not much can be done about it.

On July 12th they will extend it again.

With OpenRouter you can pay flat rate and try nearly any model on the market.

Open Weights models are only about a year behind. Once a model is released its open weights forever. In a few years we will look back at 2026 era hardware as incredibly puny for AI workloads. Simultaneously, edge models will continue to get more expensive and more powerful.

I don't want to talk to AI.

Exactly, that is why US ports are the most efficient in the world.

There are more indie games than ever before. The issue is AAA games have become billion dollar projects. They are funded and structured far more like AAA movies than other software. Making games is easy. Getting the money together to spend $500 million on development and $500 million on marketing isn't easy.

Employee pay depends on the margins of the industry. High margin industries can afford to pay high wages. Game publishers have nowhere near the margins of Google.

These are people who spend billions on whatever this decade's hype cycle is.

It's very insulting. I don't need them to talk to an AI. I talk to AI all day already. If all a person is doing is forwarding messages to AI why do we need them? Just have an AI do their job.

The current hype cycle is about measurers getting fired. But as far as I can tell there is no new product using AI to track performance going around. What exactly is AI doing to replace the measurers?

If you run a local LLM and an open source agent harness you are pretty close to that.

How did Google blow their AI lead? Why is Google the 2nd or 3rd tier player in the AI coding market? Why can't GCP supplant AWS?

Because google can't help but constantly shoot its customers and itself in the foot.

It's doubtful they have the compute to make mythos publicly available even after the SpaceX datacenter deal. And why sell it publicly if people are still willing to pay for Opus 4.7?

I haven't been bothered by hallucinations in premier models since early last year. Still see it in smaller local models though.

Anthropic needs any compute they can get. So if Elon wants to build orbital data centers Anthropic would be happy to run models on it. There isn't really any doubt Elon can build orbital data centers the question is if they are economical compared to earth based.

I don't know, I've been using Claude Code since it came out and it really doesn't seem to be getting worse.

He found a way to charge people for open source

How is this not effectively a ban on representing yourself in court? The lawyers and judge are going to be using AI. But the layman isn't allowed to use it?

While it's true that 'figuring out what exactly needs to be programmed' was always the hard part. It's not the part that the most money was spent on. Actually programming the thing always took up the most time and money.

Continuous delivery really killed QA.

Adsense is designed to have as many footguns as possible.

Nope its totally dead

The problem is that health insurance companies squander immense amounts of money on adjudicating claims. Huge amounts of GDP are spent on fights between insurers and providers over what is covered.

A lot of it seems to be porting open source projects to rust for other open source projects to consume.

AI providers can only charge what the market can bear. AI isn't worth 20k/month for 'PHD' level work. But people are willing to pay for several $200/month subscriptions.

But fundamentally AI compute is a commodity. GPUs are made in factories at scale. Assuming AI quality tapers off eventually supply will catch up to demand.

Finally open weights models are good enough that the leading labs cannot charge high margins.

Works for me with a Pro sub at https://gemini.google.com/app

Are agents actually capable of answering why they did things? An LLM can review the previous context, add your question about why it did something, and then use next token prediction to generate an answer. But is that answer actually why the agent did what it did?