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to begin, i ordered a few black polycarb macbooks (model a1181) from ebay. they were all pretty beat up and didn't have their batteries, nor did they power on.i then found and order some oem parts of the outer chassis that i guess had never been made into an actual uniti then follow an ifixit tutorial to completely take apart the macbooks, till i was down to just the bare chassis. my main idea was that the used macs were gonna be my test runs before i did anything with the oem parts, since they were the cleanest looking parts.pretty much all of the parts of the mac i discarded, since they didn't work and even on their own, aren't worth alot if i did sell them. i did keep only a few metal brackets that screwed into the bottom of the bottom chassis (after dremeling away the "middle" section for the old removeable ram sticks), and another that's screwed in at the top that actually holds the hinges for the top chassis.
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I’ll give you an example of what this looks like, which I went through myself: a couple years ago I was working at PlanetScale and we shipped a MySQL extension for vector similarity search. We had some very specific goals for the implementation; it was very different from everything else out there because it was fully transactional, and the vector data was stored on disk, managed by MySQL’s buffer pools. This is in contrast to simpler approaches such as pgvector, that use HNSW and require the similarity graph to fit in memory. It was a very different product, with very different trade-offs. And it was immensely alluring to take an EC2 instance with 32GB of RAM and throw in 64GB of vector data into our database. Then do the same with a Postgres instance and pgvector. It’s the exact same machine, exact same dataset! It’s doing the same queries! But PlanetScale is doing tens of thousands per second and pgvector takes more than 3 seconds to finish a single query because the HNSW graph keeps being paged back and forth from disk.，更多细节参见新收录的资料
data[i].value = (i * 7) % 50;
"self_check": ["lint", "typecheck", "关键场景手测"]