Exactly the same case here. A few years ago, pre-ChatGPT, my wife was working with a massive database in SPSS or R (can't remember which). Now these "databases" are really just huge CSV/TSV files, with millions of records, one record per line. She was applying some very complicated mathematical functions to every single row, and she was frustrated because it was going to take days to apply a particular calculation she was working on to the whole database.
So I took a look for about 30 mins figuring out the code (I don't know SPSS or R), and it turns out she was doing some calculations that were identical for every single line, regardless of that line's fields. So I pre-calculated a few things, prior to starting the per-record-loop, and then just used the pre-calculated values every time. After that, every run took less than 5 minutes. She's amazing at math and economics, but optimizing low-level code is not something she has done in the past.
Yeah, very similar. For each and every image, the code loaded every image into memory, several times each, then did a pixel-by-pixel walk through the entire image, identified all objects, and then compared all of the objects to all of the objects from all of the previous images (which it would not use the calculated result for, but rather recompute that too) to establish which one was moving - or which one was staying still.
The main fix was not making it hideously IO bound, as not only was it loading every image multiple times, it was then grinding away in swap as we’re talking tens of gigabytes of raw image data, in an era when 1gb of ram was a lot - and then making it spiral out from the last known location of the object of interest rather than brute-forcing it. My solution wasn’t even optimal, as it was literally just an afternoon of tooling around as a favour.
They don't have produce a good compiler. They just have to take the ones that already exist go much faster. That's likely much easier, and you can validate using existing test suites.
Like others have said, it's not reasonable to ask this.
I propose we defeat this with the obvious: Simply, figure out what are some of the markers Claude and others will use for these tools, and sprinkle them randomly on everything we type or produce, all the time, 100%. If users flood the tools, and everything returns as AI-generated, then the tools become useless.
Omnigent and OpenRouter are different in the sense that OpenRouter is where you can go to call the actual model but Omnigent is intended to be the place where you go describe the high level task to be done, and work is farmed out to various harnesses and models. Those sandboxes can themselves be using OpenRouter for capacity!
We're calling the layer coordinating harnesses "meta-harness'
Omnigent seems to compete more against Orca https://github.com/stablyai/orca
They both went to be the Agent IDE layer, where you come with your tasks and everything is taken care of. I've been using Orca for a handful of tasks and have been largely enjoying it. My default barebones workflow is ghostty + zmx on ssh connections.
So I took a look for about 30 mins figuring out the code (I don't know SPSS or R), and it turns out she was doing some calculations that were identical for every single line, regardless of that line's fields. So I pre-calculated a few things, prior to starting the per-record-loop, and then just used the pre-calculated values every time. After that, every run took less than 5 minutes. She's amazing at math and economics, but optimizing low-level code is not something she has done in the past.