Actually, I've been saying that even models from 2+ years ago were extremely good, but you needed to "hold them right" to get good results, else you might cut yourself on the sharp edges of the "jagged frontier" (https://www.hbs.edu/faculty/Pages/item.aspx?num=64700) Unfortunately, this often necessitated you to adapt yourself to the tool, which is a big change -- unfeasible for most people and companies.

I would say the underlying principle was ensuring a tight, highly relevant context (e.g. choose the "right" task size and load only the relevant files or even code snippets, not the whole codebase; more manual work upfront, but almost guaranteed one-shot results.)

With newer models the sharper edges have largely disappeared, so you can hold them pretty much any which way and still get very good results. I'm not sure how much of this is from the improvements in the model itself vs the additional context it gets from the agentic scaffolding.

I still maintain that we need to adapt ourselves to this new paradigm to fully leverage AI-assisted coding, and the future of coding will be pretty strange compared to what we're used to. As an example, see Gas Town: https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16d...

Each failed prediction should lower our confidence in the next "it's finally useful!" claim. But this inductive reasoning breaks down at genuine inflection points.

I agree with your framing that measuring should NOT be separated from political issues, but each can be made clear separately (framing it as "training the tools of the oppressor" seems to conflate measuring tool usefulness with politics).

It's a little weird how defensive people are about these tools. Did everyone really think being able to import a few npm packages, string together a few APIs, and run npx create-react-app was something a large number of people could do forever?

The vast majority of coders in employment barely write anything more complex than basic CRUD apps. These jobs were always going to be automated or abstracted away sooner or later.

Every profession changes. Saying that these new tools are useless or won't impact you/xyz devs is just ignoring a repeated historical pattern

You're free to not open these threads, you know!

Democratizing coding so regular people can get the most out of computers is the opposite of oppression. You are mistaking your interests for societies interests.

It's the same with artists who are now pissed that regular people can manifest their artistic ideas without needing to go through an artist or spend years studying the craft. The artists are calling the AI companies oppressors because they are breaking the artist's stranglehold on the market.

It's incredibly ironic how socializing what was a privatized ability has otherwise "socialist" people completely losing their shit. Just the mask of pure virtue slipping...

> If I am unable to convince you to stop meticulously training the tools of the oppressor (for a fee!) then I just ask you do so quietly.

I'm kind of fascinated by how AI has become such a culture war topic with hyperbole like "tools of the oppressor"

It's equally fascinating how little these comments understand about how LLMs work. Using an LLM for inference (what you do when you use Claude Code) does not train the LLM. It does not learn from your code and integrate it into the model while you use it for inference. I know that breaks the "training the tools of the oppressor" narrative which is probably why it's always ignored. If not ignored, the next step is to decry that the LLM companies are lying and are stealing everyone's code despite saying they don't.

Frankly, in this comment thread you appear to be the oppressor.