People still write in languages that force you to manage your own memory. Once performance starts to matter (either due to scale or time requirements) abstractions always have tradeoffs you can't accept.
So then, how can garbage collection ever be solved if it’s a trade-off
And how can backends be abstracted away if there is a trade off?
As long as compute is a meaningful percentage of spend, the trade off will matter.
> Because they do things. With their hands. That no one else does That's only true of surgeons :) What if your specialty is nonsurgical (internal medicine, pediatrics, psychiatry, etc)?
Not even true of all surgeons, the ones that make the most money use machines to work on things their hands couldn't do
pathologists are some of the highest paid doctors and they are right in the crosshairs of what AI is getting better at performing.
And how can backends be abstracted away if there is a trade off? As long as compute is a meaningful percentage of spend, the trade off will matter.
Right. So what does it look like for garbage collection to be solved? You’re saying it’s not ever possible
I am saying it's not possible for the foreseeable future, yes. The same way backends becoming an abstraction most developers don't need to worry about is also not going to happen in the foreseeable future.
One thing I found peculiar is that for the GraySort benchmark it dispatches to Polars by default to do the actual sorting, not DuckDB: https://github.com/deepseek-ai/smallpond/blob/ed112db42af4d0... .
The function argument defaults to polars, but the actual benchmark code sets duckdb by default.
We are seeing more and more specialized query engines. This is a query engine specialized for training pipelines. It is not general purpose - it is for providing batches of training data at workers. It uses Ray for parallelization. The kind of queries you need are random reads (to implement shuffling across epochs), arrow support (zero copy to Pandas DataFrames), and efficient checkpointing.
Data operations are increasingly happening near the GPU side to boost efficiency—especially for compute-heavy workflows. Talking about Arrow file processing and zero-copy queries on DataFrames, which are becoming crucial for modern data pipelines. I think another option worth considering is chdb, which supports these features and fits well with this shift. (author of chdb here)
Is the code written by the deepseek model? I should probably give up on being a software engineer if it is.
Give up and become what? Most white collar jobs will be automated in the coming years. You think doctors' jobs are safe?
If your white collar job consists of simply using software, like copying numbers you see to an excel sheet, maybe. Otherwise they are pretty safe. People have been building tools and automation for thousands of years, yet nobody invented a fully automated cook for your fancy family dinner.
Yes, doctors are safe. Because they do things. With their hands. That no one else does.
> Because they do things. With their hands. That no one else does That's only true of surgeons :) What if your specialty is nonsurgical (internal medicine, pediatrics, psychiatry, etc)?
Psychiatrists do that triangle shape with their hands.