If one lidar hits another, it will result in at most one bad reading (perhaps a bad column?). This can likely be filtered, or a bad scan (360deg) can be altogether rejected and the data predicted using models based on past sensor readings.
That's a pretty good example. The summary is actually useful, yet it still annoys me.
But I'm not usually reading the comments to learn, it's just entertainment (=distraction). And similar to images or videos, I find human-created content more entertaining.
One thing to make such posts more palatable could be if the poster added some contribution of their own. In particular, they could state whether the AI summary is accurate according to their understanding.
Interesting example! I've been learning AVX512 by using it to optimize Huffman coding. I found _mm512_permutexvar_epi8 and used it to do byte-indexed lookups, but _mm512_permutex2var_epi8 means I can get by with 2 shuffles and 1 comparison instead of 4 shuffles and 3 comparisons. In the end, on my CPU (AMD 9950x), changing to _mm512_permutex2var_epi8 only sped up compression by ~2%.
Compared to Huff0[1] (used by Zstd), my AVX512 code is currently ~40% faster at both compression and decompression. This requires using 32 datastreams instead of 4 used by Huff0.