this article is a bit tough... feels like a marketing piece for some Google report.
did I read that wrong or was this whole analysis based on percentages... like what does 76% -> 24% drop in memory related bugs mean in terms of nominal bugs or nominal bugs / kloc
also, it mostly credited memory safe languages but then also just threw this out from the Google report
> Based on what we've learned, it's become clear that we do not need to throw away or rewrite all our existing memory-unsafe code
tl;dr android may be producing less memory-related vulnerabilities and it's not exactly clear how
maybe Ubuntu? the web development flow (so closely tied to firebase) might suffer a little bit, but I imagine a fork for non-web env's would be quick and welcome
Mostly linux agnostic, but in case it helps, its was all right for me. The practice exercises at the end of each section offer some hands-on help with some linux tools
Thank you. This was an amazing explanation. I am new to SVM's but did not make the connection that margin points (observations along the margin of the hyperplane) become your support vectors. This makes a lot more sense.
And if I am following correctly, it would make sense that the final step would then be:
We would maximize the dot product of a new observation with the support vectors to determine its classification (red or blue)
did I read that wrong or was this whole analysis based on percentages... like what does 76% -> 24% drop in memory related bugs mean in terms of nominal bugs or nominal bugs / kloc
also, it mostly credited memory safe languages but then also just threw this out from the Google report
> Based on what we've learned, it's become clear that we do not need to throw away or rewrite all our existing memory-unsafe code
tl;dr android may be producing less memory-related vulnerabilities and it's not exactly clear how