 So I had a couple of people asking about the YouTube analytics thing. So it's normally default training in the last 28 days. So I had like 3,000 views. It actually tells me it's 2,000 more than usual. Watch time, just like the most important number of subscribers going up. Well, it's jumping up and down. Here you can see live count ads of the subscribers. And here you can see the live last 48 hours. So the general stats are updated only every 48 hours. But you can see live stuff as well. For example, this video is trending. I don't know why. It's doing way better than typical, typically. It's that gray area there. This one is shooting up. But normally I don't know if it's the algorithm pushing it or is it organic. So 4.2 watch hours to subscribers out of that video. But yeah, it seems to be saturated in plateauing. So the algorithm kind of forgets about it. I used to post a lot on LinkedIn and stuff. But yes, say for this video externally, it was only found by 1.7%. So now I'm doing more selective posting on LinkedIn. Because this essentially suggests that this specific video was mainly found through the YouTube page. So it's YouTube. I'm recommending it or doing something with it. Yeah, average percentage view is not great at all. It's actually pretty bad. It's shockingly bad. So probably YouTube will stop recommending it because of this. It must be this very low percentage view. So I don't know if people are clicking on it, not expecting it's a longer video. Or the label is not correct. Yeah, normally after a while, it's generating subtitles. So it's transcribing text to speech. So if I pop this, pop it into a GPT, it is my video. What's it called? Captions summarize and suggest titles to really remember what is it about. It's got changes. It's summary. Right, I actually remember doing this. So GPT4 is pretty accurate. But if it's complete though, a summary actually can be up to 5000 characters. Yeah, that's meant to be GD. Yeah, that's probably a better title. Yeah, I was probably doing one wishes with the title. YouTube perfecting a web application for IG spectrum visualization, several hurdles emerged that required immediate attention. From the outset, the application refused to display anything. The perplexing part was that despite the absence of visualization, there errors were being reported. The core of the issue seemed to revolve around then, not a number, values. Upon diving deeper, it was revealed that these nan values were being produced during the data filtering process. This raised questions about the filtering method being utilized, and what might be causing these aberrant values. One critical discovery was the instability of the filter, which was attributed to an excessively large filter order. While filters are essential in refining and processing data, their stability is paramount to ensure accurate results. The instability not only skewed the data, but also presented challenges in achieving a clear visualization on the web application. A practical solution was proposed by simply reducing the filter order to a more reasonable number. In this case, four, the filter stability was restored. However, even after stabilizing the filter, another challenge cropped up. The raw signal chart, a fundamental component of the application, was not displaying as intended. Given the intricacies of data structures and their direct impact on data visualization, the user suspected that the root of this problem might be tied to how...