abetlen

This is not true, GPT-3 can perform chain-of-thought reasoning through in-context learning either by one/few-shot examples or zero-shot by starting a prompt with "let's think step by step" (less reliable).

GPT-3.5 (what's being used here) is a little better at zero-shot in-context learning as it's been intstruction fine-tuned so it's only given the general format in the context.

I think you're focusing on a few narrow examples where LLMs are underperforming and generalising about the technology as a whole. This ignores the fact that Microsoft already has a succesful LLM-based product in the market with Github Copilot. It's a real tool (not a party-trick technology) that people actually pay for and use every day.

Search is one application, and it might be crap right now, but for Microsoft it only needs to provide incremental value, for Google it's life or death. Microsoft is still better positioned in both the enterprise (Azure, Office365, Teams) and developer (Github, VSCode) markets.

Cool trick, thanks for sharing. I don't get why there isn't a suitable snscanf function that takes the buffer length as an argument and returns the number of bytes parsed?

> I'm pretty sure you could also maintain a temp table and use some kind of "insert...where...returning" construct to squeeze that into a recursive query.

I'm not sure if this is possible in SQLite, as far as I know the WITH clause is limited to SELECT statements.

> Are they actually efficient in sqlite even for trees?

Recursive common table expressions work by adding returned rows to a queue and then performing the recursive select statement independently on each row in the queue until it's empty.

You can use WITH RECURSIVE to traverse a tree by adding the root node to the queue and recursively visiting adjacent rows until the queue is empty. This works correctly and quickly because trees have only a single path between nodes. If you try the same query on a DAG though it will return every path to a given node, you then have to perform a GROUP BY to find the shortest path outside of the recursive query. In the worst case, if you have a graph with many paths between nodes, this method is exponentially slower than a standard BFS.

The issue I found with WITH RECURSIVE queries is that they're incredibly inefficient for anything but trees. I've looked around and there doesn't seem to be any way to store a global list of visited nodes. This means that when performing a traversal of the graph the recursive query will follow all paths between two nodes.