Comment by ctoth

Ironically, the human tendency to read far too much into things for which we have far too little data, does seem to still be one of the ways we (and all biological neural nets) are more sample-efficient than any machine learning.

I have no idea if those two points, ML and brains, are just different points on the same Pareto frontier of some useful metrics, but I am increasingly suspecting they might be.

I went from using ChatGPT 3.5 for functions and occasional scripts…

… to one of the models in Jan 2024 being able to repeatedly add features to the same single-page web app without corrupting its own work or hallucinating the APIs it had itself previously generated…

… to last month using a gifted free week of Claude Code to finish one project and then also have enough tokens left over to start another fresh project which, on that free left-over credit, reached a state that, while definitely not well engineered, was still better than some of the human-made pre-GenAI nonsense I've had to work with.

Wasn't 3 hours, and I won't be working on that thing more this month either because I am going to be doing intensive German language study with the goal of getting the language certificate I need for dual citizenship, but from the speed of work? 3 weeks to make a startup is already plausible.

I won't say that "software engineering" is dead. In a lot of cases however "writing code" is dead, and the job of the engineer should now be to do code review and to know what refactors to ask for.

The cope + disappointment will be knowing that a large population of HN users will paint a weird alternative reality. There are a multitude of messages about AI that are out there, some are highly detached from reality (on the optimistic and pessimistic side). And then there is the rational middle, professionals who see the obvious value of coding agents in their workflow and use them extensively (or figure out how to best leverage them to get the most mileage). I don't see software engineering being "dead" ever, but the nature of the job _has already changed_ and will continue to change. Look at Sonnet 3.5 -> 3.7 -> 4.5 -> Opus 4.5; that was 17 months of development and the leaps in performance are quite impressive. You then have massive hardware buildouts and improvements to stack + a ton of R&D + competition to squeeze the juice out of the current paradigm (there are 4 orders of magnitude of scaling left before we hit real bottlenecks) and also push towards the next paradigm to solve things like continual learning. Some folks have opted not to use coding agents (and some folks like yourself seem to revel in strawmanning people who point out their demonstrable usefulness). Not using coding agents in Jan 2026 is defensible. It won't be defensible for long.