pcraig3

It handles hundreds of thousands of requests a day, that's pretty good!

But you are right, SOC2 is the next step for sure.

Like Phil, I always assume the negative comments have a 35% accuracy rating.

I hope your child builds the next unicorn startup using this API!

You can try and run it yourself but you are going to need Stargate-level hardware to do it.

Sorry to hear you are out.

I've found "T/F" vals are not always portable between languages ("True" in Python vs "true" in JS) whereas integer comparisons are always interpreted correctly.

The job of Prognosticating Groundhog has weathered many technical revolutions: I don't think AI is going to be the one that puts them out of business.

Yes, important context

Hello all,

LLM hype is everywhere these days, but actual usage data is pretty hard to find. I see tons of companies pushing AI tools at me but I have almost no idea what user behaviour/preferences are.

I built a TaxGPT.ca, an AI chatbot for answering people's (Canadian) tax questions and ran a bunch of numbers over the questions submitted between March 4 to April 31, 2024.

Highlights include:

- Over 8,500 questions answered - Users frequently asked about basic tax info and business expenses - Feedback is not very common but surprisingly positive - Experiments in tagging questions in relevant and for 'complexity'

My verdict here is that these bots have potential but you have to have really quick feedback loops and pretty good product chops — you can't just throw AI at some problem and assume users will find their own ways to make it useful.

I know, and not only are they fewer, but they are also mostly not real, alive groundhogs once you get that far.

The pictures of real groundhogs are all there in the individual profiles, it just turns out that half of them are stuffed.