The closest example I can think of is controversial. Trump has stated that there is a Christian genocide going on in Nigeria. Credible human rights organizations have said that it is terrorism and no single religious group is being targeted. I really don’t know if genocide is the correct label as plenty of Muslims and others have been killed too. Or really whether it could even be considered genocide as Christianity isn’t an ethnicity but people ARE dying.
It took some time for the dark magic absorbed through the ritual sacrifice of Lindsey Graham to fully restore Mitch to a shadow of his living self.
The fact that they aren’t googly is a fucking tragedy.
He’s just resting his eyes… and his brain.
Damn, why does it have to be so complicated?
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Such a fantastic game.
What about domestic propaganda?
It’s got what plants crave.
Oh shit, I just remembered that I have cheese!
If you had told me 30 years ago that China would be “Brave New World” and the US would be “1984” I would have thought that you got it backwards.
It’s all farty butt-talk, if you ask me.
American non-prisoners are often not given prompt or effective medical care too.
It’s not a good situation.
It’s probably a legacy code quality issue too. If your code is a mess, the only fix it can find is a kludgy one. The first thing I had it do, once it was good enough to do real work, was a legacy refactor to get architecture up to best practice standards. Heck, even OpenAI’s Harness Engineering specs say that this is essential to do first. I suspect that many of the people saying it comes up with shitty solutions are the same ones that always complain about over-engineering.
You’ll also get subpar results if you just have it start coding without fully architecting its solution first. Just like a real developer.
Maybe it’s just because Anthropic is so many more miles ahead than OpenAI, but I have not had the “it doesn’t work” experience that people are talking about here. I’ve set up agentic pipelines with code architecture guidelines, the orchestrator calls the implementer, tester, reviewer when needed. If the developed code isn’t up to standard or the work item isn’t fully captured, it’ll reject it and send it back to implementation. It invokes the architect if it runs into something unanticipated. The pipeline can run a feature with high accuracy and very little input.
I’ve even used it to run batteries of frozen experiments on self synthesized data using its own simulation harnesses to discover novel techniques that work in the real world. If it isn’t helpful, I feel like you are doing it wrong.
This sounds sketchy as fuck. I wonder what’s really going on.
Check out Frankies