We just released a big new update to Jerboa that adds a lot of much needed features and polish. We had 14 new contributors too!

I imagine it’ll be possible in the near future to improve the accuracy of technical AI content somewhat easily. It’d go something along these lines: have an LLM generate a candidate response, then have a second LLM capable of validating that response. The validator would have access to real references it can use to ensure some form of correctness, ie a python response could be plugged into a python interpreter to make sure it, to some extent, does what it is proported to do. The validator then decides the output is most likely correct, or generates some sort of response to ask the first LLM to revise until it passes validation. This wouldn’t catch 100% of errors, but a process like this could significantly reduce the frequency of hallucinations, for example.

Consider charging at home, if you can. If your typical driving patterns consist of driving <100 miles from your home and it’s possible to plug in at home (a standard 120V outlet is sufficient typically), then you don’t need public charging stations. Just plug your car in at night and it’ll be full every morning.