“But GPT said it is a good idea”

Adding extra emails to your inbox on top of the registry emails

> Then ChatGPT hit the scene and again, many of us dismissed it as a parlor trick that would never amount to much.

What? GPT-3.5 was the moment, not a “parlor[sic] trick that would never amount to much”.

GPT-3.5 was a massive step forward. LLMs were suddenly good and produced meaningful content.

I was extremely early to the agentic stage and had it looping and was dealing with the 4k context issues.

Mandatory don’t hate me: I am a massive AI and automation fan since 2008.

The problem right now is the barrier to create automation and use AI has reduced so much people are not deliberately making decisions about how these tools should work. This means the tools primarily focus on happy paths, and only get refined to the point that they do a “good enough job” and then are shipped. The other issue is people who don’t understand what they are building are shipping brittle and fragile solutions but don’t understand how that is an issue because the AI “just works”, however they get upset when it doesn’t scale.

The solution is to embrace AI and Automation, but slow down planning, architecture, error handling and testing.

This means we still reap the efficiencies but don’t ignore reality.

Code happy, Dale

> The solution is to embrace AI and Automation, but slow down planning, architecture, error handling and testing.

How does that work in practice? How do you implement that from the bottom up if the psychosis is top-down?

I don't know what you're saying.

This is the second or third HN post I'm seeing this month along the lines of "how dare AI companies flag my code as AI-generated". I just don't remember similar complaints about the iPhone footer. Not many HNers complain about The North Face putting the text "The North Face" on their hoodies either. Or Honda putting their logo on the car.

The reasons for this difference are interesting. The fact that companies put their logos / brands on stuff is a lot less interesting to me. You can call it bad, but again, why is this instance worse?

Plenty of people turned off the iPhone thingie once they figured out where the setting was (kinda buried).

Plenty of people don’t like logos. And some do, because they tie their identity with brands. Maybe in the future some of us will tie our identities to specific agents/LLMs.

To each their own. I don’t have clothing with logos, I find it gross. And you also don’t know I’ve sent you an email from an iPhone. Ça va.

Disagree, I never attributed Laravel Artisan or all the other code generators.

Right now you could run Laravel new app (replace with any new framework) and Claude/Cursor/Codex git commit will claim to of created the code.

To be fair I've never used Laravel - but I do have a memory of code generators/scaffolders inserting files that made clear they were responsible for work

/*

* NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).

* Do not edit the class manually.

*/

It is extremely annoying how you can hand craft a change then use the auto git commit message generation and it adds an attribution to the GPT that wrote a hit commit message, I don’t see the attribution to all the other tools, this practice has to stop.

Yes transformers are great at translation as that is their purpose.

LLMs are not great at preserving cultural uniqueness and diversity. Take how “delve” has reentered the lexicon because the human assessors for pre training dialect of English uses “delve” a lot.

There is a lot of benefits to training specifically for a unique culture with unique norms to preserve the culture as we increasingly rely on LLMs.

https://www.scientificamerican.com/article/chatgpt-is-changi...

Hard disagree. This is the first step not the last and proves to other countries that this can be done.

This model is going to start miles behind the frontier and the gap will only grow.

Why would the gap grow? There is no more training data to acquire, frontier model are training on the entire internet. Everything from now on is just fine-tuning.

Your statement assumes training data is the only thing that matters for the big players, while not considering it limiting for the small Norwegian model. That’s a fallacy.

Nowhere in the article does it say the Norwegian LLM will train _only_ on Norwegian data.

Orders of magnitude less compute for pre- and post-training than the the frontier labs.

How about that, they actually asked for permission to use data and the companies said yes.