Indeed! I generally like awk a lot for simpler CSV/TSV processing, but when it comes to cases where you need things like combining/joining multiple CSV files or aggregating for certain columns, SQL really shines IME.
On a tangent, nice to see Plasmidsaurus using Emu [1], which has been shown to work great for 16S ribosomal RNA analysis on ONT by basically everyone I've heard who tried it. It has a nice algorithm for predicting if variants are due to ONT sequencing errors or are true variants, based on an expectation maximization algorithm, and thus working around the somewhat limited accuracy in ONT reads. Pretty clever stuff.
And if you want to run your own analysis on the raw data using Emu, you might want to try out our Trana pipeline built around Emu in Nextflow [2]. Apart from running Emu, it does some of the preprocessing like filtering, as well as exporting as Krona diagrams etc.
We're just putting it through validation at the clinical microbiology lab at Karolinska here in Stockholm right now.
The main caveat worth mentioning is that the choice of database seems to be able to affect results quite a lot in some cases.
I tried this to try to extract some speech from an audio track with heavy noise from wind (filmed out on a windy sea shore without mic windscreen), and the result unfortunately was less intelligible than the original.
I got much better results, though still not perfect, with the voice isolator in ElevenLabs.
I love Prolog, and have seen so many interesting use cases for it.
In the end though, it mostly just feels enough of a separate universe to any other language or ecosystem I'm using for projects that there's a clear threshold for bringing it in.
If there was a really strong prolog implementation with a great community and ecosystem around, in say Python or Go, that would be killer. I know there are some implementations, but the ones I've looked into seem to be either not very full-blown in their Prolog support, or have close to non-existent usage.
Yea, a bit like a cheating student rote memorizing and copying another students technique for solving a type of problem, and failing hard as soon as there's too much variation from the original problem.
That said the input space of supported problems is quite large and you can configure the problem parametrs quite flexibly.
I guess the issue is that what the model _actually_ provides you is this idiot savant who has pre-memorized everything without offering a clear index that would disambiguate well-supported problems from ”too difficult” (i.e. novel) ones
I was going to ask about wine support. Anyone tried in Bottles (wine distribution)? I've had better luck with Bottles than plain Wine with other software. Hoping to try soon.
In the article [0] (also posted on HackerNews [1]) they share a link to page, which is a guide how to run new Affinity (v3) using Wine/Bottles combo [2].
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