Human coders are still better than LLMs

Summary

antirez 1 hour ago. 14783 views. This is a short story of how humans are still so much more capable of LLMs. Note that I'm not anti-AI or alike, you know it if you know me / follow me somewhere. I use LLMs routinely, like I did today, when I want to test my ideas, for code reviews, to understand if there are better approaches than what I had in mind, to explore stuff at the limit of my expertise, and so forth (I wrote a blog post about coding with LLMs almost two years, when it was not exactly cool: I was already using LLMs for coding and never stopped, I'll have to write an update, but that's not the topic of this post). But, still: the current level of AI is useful, great too, but so incredibly behind human intelligence, and I want to remark this as lately it is impossible to have balanced conversations. So, today I was working to Vector Sets for Redis, to fix a complicated bug: during the time I stopped working at Redis my colleagues introduced resistance against corruption RDB and RESTORE payloads, even when the checksum of the data passes. This feature is disabled by default, but provides an enhanced layer of safety for people wanting it. But… there is a but as big as an elephant: In order to make HNSWs fast to save into Redis RDBs and to load back, I serialized the *graph* representation, and not the element-vector pairs, otherwise I would have to re-insert back data into HNSWs, and that would be, like, 100 times slower (!). So I store all the links the nodes have with other nodes, as integers, and then I resolve them into pointers, it’s a nice trick and works great. But if you mix this and random corruptions of the representation, and the fact that my own twist on HNSWs enforce reciprocal links between nodes (I wrote my own implementation of HNSWs with many useful features, but reciprocal links are needed to enable many of them) then this could happen: 1. We load corrupted data that says A links to B, but B no longer links to A (corrupted node IDs). 2. We del...