IMO anyone using any kind of metrics effectively is measuring multiple things and everything has a counterbalance. if youre measuring PR throughout you should measure release cadence, change failure rate, mean time to restore, etc.
correlating all of these let's you know if you're accelerating one activity metric but getting worse in an outcome metric - lots if slop PRs but more incidents is obviously the wrong outcome, and can be addressed by improving local verification, safer deployments, faster rollbacks, better observability, etc.
even with AI writing the code (and much more) all of these things matter. actually seeing positive outcomes (and thus RoI from AI use) requires knowing what's signal and what's noise. ive spoken to founders who see anecdotal increases in everything negative as their AI code volume goes up, and none of them are actually measuring anything effectively enough to understand what to do about it
"ntsc-rs is written in Rust, and is multithreaded and SIMD-accelerated."
This made me lol. I know so few video editor types that would use a plugin like this that would know what Rust was to be "impressed" by this bit of PR.
Yesterday, John Romero did a historic livestream on his Twitch channel.
He played through a few maps while sharing details about the origins of Quake, design decisions, how Nine Inch Nails ended up doing the soundtrack, and a lot of rich information for fans of the game, at least for me, since I still play QW to this day and didn't know any of this.
But one thing I notice about Romero, or maybe I’m just imagining it, is that he doesnt seem to have anywhere near the same affection for Quake as he does for Doom. Maybe the internal issues back then had something to do with it.
Hell yeah, I still see it as the optimal arena FPS. The sequels add too much IMO. Quake 1 (QuakeWorld) was such a raw competitive arena shooter that is just pure action, no weird extra mechanics. Pick up items, get some health, shoot people. Q2/Q3 are totally fine too, but I feel like QW really just nailed everything.
How can I learn the practical side of computer vision in 2026?
I'm not interested in understanding papers or the math behind it, but rather in how to put a system into production, whether it's object detection, running 20 cameras in parallel on a single computer, like sizing hardware for a specific task, and so on.
Try a coding agent for writing and tuning the OpenCV part, and have it explain its choices. That's probably the most practical path to shipping a working system.
Speaking from experience: never used OpenCV before, recently vibe coded a tool that makes supercuts of pool videos, trimming each clip from the cue ball's first strike to when the motion stops.
One of the great things about OpenCV is how ubiquitous it is, there's a ton samples online and well represented in frontier model training data. I recently vibe-coded an object detector for my own personal photo library so I could separate out my pictures with humans in them. Very approachable with Codex + feeding it a sample from Github.
Elevated creatinine levels (in absence of supplementation) is a sign of kidney issues. There are many studies showing no reverse link i.e. supplementing creatine damaging kidneys
Okay but logically kidney issues leading to high creatine does suggest that creatine is not all that good for the body, no? else why would the kidney keep it under control?
No, elevated creatinine levels is only a sign of kidney disease if all else remains equal. Consuming more creatine will naturally increase production of creatinine because creatinine is a byproduct of the kidneys processing creatine.
In other words, it's not a very reliable signal for kidney disease. There is a more reliable blood test called a "Cystatin C" test.
