Is there something special about yolov8 over later models (9-12)? It seems most of the research and working examples default to v8 despite it being 3 years old. Or just because it is what fits on this hardware?
Mainly because YOLOv8 is well-supported by the Rockchip/RKNN toolchain.
The goal here was an end-to-end RK3588S pipeline rather than comparing detector families: training/export, ONNX graph fixing, INT8 RKNN conversion, C++ postprocessing, and runtime inference across the 3 NPU cores. YOLOv8 has known-good export paths and Rockchip examples, so it was the most practical baseline.
Newer YOLO versions may be possible, but usually require more work around RKNN export compatibility.
I didn’t know how electrolysis really worked, and probably still don’t but I know a little more, and these cheap ion filtering membranes are absolutely wild, and turning the graphite fire blanket into a super high surface area electrode is super interesting. I remember when he re-made the “lost” aerogel-like substance a while back and wonder if we can make graphite aerogel-likes using similar processes somehow for super high surface area electrodes. Sometimes I think I’m more interested in practical materials science than software engineering, but at 39 years old I am probably past the point of going to university to actually study. Of course, unless we all get UBI quickly because of Claude 8.5 taking everyone’s jobs.
> at 39 years old I am probably past the point of going to university to actually study.
That largely depends on your financial situation. If you have a strong technical background and you've already secured your retirement you could certainly do a masters or phd. But if you aren't financially secure then yeah, accepting (somewhat worse than) minimum wage for the next 5 years followed by a high degree of uncertainty sounds like a really bad idea.
That said materials science is something of a bastard child of inorganic chemistry, applied physics, and engineering. The theory side of it can be absolutely brutal. Before embarking on an adventure I'd suggest looking over the coursework for physical chemistry to see if you can handle the quantum mechanics stuff.
The problems I was interested in led me to the techniques in the field. I guess the idea was data science, but might need to pivot into LLM or AI engineer.
For my use case, wanting to see rendered markdown with images, fonts, working links, mermaid diagrams, code blocks etc and not wanting a bundled editor, the advantage is that it’s a small binary that I can bind to my keybinds, open, drag a markdown file onto, and view and have it hot reload was all I needed and thus all I implemented. If you like, you can use it, if not that’s okay.
I wonder if they’re just activating small parts of the brain at once. It seems to me that they aren’t simulating every single neuron at all times, and I do not see being able to run the model in real time. Are they also modelling the ventral nerve cord for the walking?
I'm having the same issue. I thought it was just me or something with their cloud network. I also haven't been able to download Android Studio from the website for a month. I couldn't even download it from my Macbook so probably not the same issue.
AWS Support initially pushed back and suggested it's because of high replication lag but they were looking at metrics that were more than 24 hours old. What kind of failure did you encounter? I really want to understand what edge case we triggered in their failover process - especially since we could not reproduce it in other regions.
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