Currently, the largest defined code point is 0x10FFFF. That gives us a space of about 1.1 million code points.
About 170,000, or 15%, are currently defined. An additional 11% are reserved for private use. The rest, about 800,000 code points, are not allocated at the moment. They could become characters in the future.
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Like many companies, earlier this year we saw an opportunity with LLMs and quickly (but thoughtfully) started building a capability. About a month later, we released Query Assistant to all customers as an experimental feature. We then iterated on it, using data from production to inform a multitude of additional enhancements, and ultimately took Query Assistant out of experimentation and turned it into a core product offering. However, getting Query Assistant from concept to feature diverted R&D and marketing resources, forcing the question: did investing in LLMs do what we wanted it to do?
The short answer is... mostly. Query Assistant correlates with some extremely positive activation metrics and it’s inexpensive to run, thanks to OpenAI’s reasonable pricing. However, it doesn’t correlate strongly with every product activation metric we hoped it would, and although adoption is higher than we thought it would be, it isn’t as high as we hoped it could get. Overall though, it continues to be a worthy addition to the Honeycomb platform and we’re excited to continue investing in LLMs moving forward. Let’s dig into a few lessons learned, insights captured, and things we’ll continue to tweak!
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UUID Version 7 (UUIDv7) is a time-ordered UUID which encodes a Unix timestamp with millisecond precision in the most significant 48 bits. As with all UUID formats, 6 bits are used to indicate the UUID version and variant. The remaining 74 bits are randomly generated. As UUIDv7 is time-ordered, values generated are practically sequential and therefore eliminates the index locality problem.