If you want to explore navigation I made an app: https://alprwatch.org/navigation. It works fully offline, you just need to download the maps and overlays
I made a version which does the avoidance dynamically at runtime, works for any tracks you want to use: https://alprwatch.org/navigation. It works fully offline after you download the maps and overlays
About 40k new cameras each year from what I have seen.
If you find yourself with some time, there is now a DeFlock app that helps with mapping. It also includes locations where people suspect there might be a camera, though that is limited to about a third of the states so far.
I will be surprised if there is not a startup in the coming years that pays you to run a surveillance mining app that shares data as a way to subsidize the cost of the hardware. The surveillance state equivalent of ads
Why accept surveillance and tracking without a narrow and well-defined scope? It is one thing to track cars for estimating traffic velocity, it is another entirely to create a national database of all locations any given individual has been seen, with minimal oversight to the purpose for collecting and using that data.
An app to screen properties based on your own esoteric criteria. Things like, "I want to have a balcony facing south, looking at a park." Then you can send a list of suitable properties to a real estate agent to scout, or set a notification in case any of them come into the market
Where do you turn for a variety of opinions, activities, and social interactions over the long term? There is incredible value in growing alongside people over a period of time and acting as a mutual support network
Absolutely not an exaggeration. I have encountered masters students who balked at the idea of opening a file in Python, despite that ostensibly being their language of choice for a machine learning class. The concept of where the data physically resided was also a mystery to them
One of the big changes was to make many basic tools into lazy iterators instead of greedy lists (map, filter, range, etc.). I can remember having to teach my fellow scientist/engineer colleagues not to just loop over an index variable when working with certain datasets, but this was a major bit of friction because they were so used to Matlab and other languages where direct indexing is the primary method. While lazy execution is great for many things, it is not something that is necessarily common knowledge among people who use coding as a means to an end.
Additionally, because so many of the core libraries were 32-bit only or Python 2 only, you ended up having to either write your own version of them or just go back to 32-bit Python 2. Numpy in particular (and therefore transitively anything halfway useful for science and engineering) took several years to stabilize and I have many memories of having to dig into things like https://www.lfd.uci.edu/~gohlke/pythonlibs/ to get unofficial but viable builds going for the Windows machines we used. It was enough of a pain to deal with dependencies that I actually ended up rolling my own ndarray class that was horrendous but just good enough to get the job done.