I also have heard a lot of recommendations for OrbStack, but I haven't had problems with speed either. And I could never stomach using a proprietary system for such a core part of my workflow.

For context I use containers for practically everything and I run some decently complex workflows on them: fullstack node codebases, networking, persistent volumes, mounting, watch mode, etc. Red Hat knocked it out of the park with podman!

Personally, I don’t bother with nextjs at all.

LLMs reduce the effort to create a plausible PR down to virtually zero. Requiring a human to write the code is a good indicator that A. the PR has at least some technical merit and B. the human cares enough about the code to bother writing a PR in the first place.

> intermittent outages

Those seem like conflicting statements to me. Last outage was only 13 days ago: https://news.ycombinator.com/item?id=45915731.

Also, there have been increasing reports of open source maintainers dealing with LLM generated PRs: https://news.ycombinator.com/item?id=46039274. GitHub seems perfectly positioned to help manage that issue, but in all likelihood will do nothing about it: '"Either you have to embrace the Al, or you get out of your career," Dohmke wrote, citing one of the developers who GitHub interviewed.'

I used to help maintain a popular open source library and I do not envy what open source maintainers are now up against.

1: https://www.decodingdiscontinuity.com/p/open-source-inflecti...

I have seen multiple people mention openrouter multiple times here on HN: https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...

Again, I'm not claiming malicious intent. But model performance depends on a number of factors and the end-user just sees benchmarks for a specific configuration. For me to have a high degree of confidence in a provider I would need to see open and continuous benchmarking of the end-user API.

The real issue is that there is no reliable system currently in place for the end user (other than being willing to burn the cash and run your own benchmarks regularly) to detect changes in performance.

It feels to me like a perfect storm. A combination of high cost of inference, extreme competition, and the statistical nature of LLMs make it very tempting for a provider to tune their infrastructure in order to squeeze more volume from their hardware. I don't mean to imply bad faith actors: things are moving at breakneck speed and people are trying anything that sticks. But the problem persists, people are building on systems that are in constant flux (for better or for worse).

And, agreed, each individual technique seems marginal but they really add up. What seems to be missing is some automated layer that determines the best way to chunk documents into embeddings. My use case is mostly normalized mostly technical documents so I have a pretty clear idea of how to chunk to preserve semantics. But I imagine that for generalized documents it is a lot trickier.