[brokencode]

Ok, here I am living in the real world finding these models have advanced incredibly over the past year for coding.

Benchmaxxing exists, but that’s not the only data point. It’s pretty clear that models are improving quickly in many domains in real world usage.

[toraway]

I use agentic tools daily and SOTA models have certainly improved a lot in the last year. But still in a linear, "they don't light my repo on fire as often when they get a confusing compiler error" kind of way, not a "I would now trust Opus 4.6 to respond to every work email and hands-off manage my banking and investment portfolio" kind of way.

They're still afflicted by the same fundamental problems that hold LLMs back from being a truly autonomous "drop-in human replacement" that would enable an entire new world of use cases.

And finally live up to the hype/dreams many of us couldn't help but feeling was right around in the corner circa 2022/3 when things really started taking off.

[mrbungie]

Yet even Anthropic has shown the downsides to using them. I don't think it is a given that improvements in models scores and capabilities + being able to churn code as fast as we can will lead us to a singularity, we'll need more than that.

[Freedom2]

I agree completely. I think we're in alignment with Elon Musk who says that AI will bypass coding entirely and create the binary directly.

It's going to be an exciting year.

[baq]

There’s about as much sense doing this as there is in putting datacenters in orbit, i.e. it isn’t impossible, but literally any other option is better.