WikiSQL VS hanakotoba

WikiSQL

Natural language processing/understanding/generation is solved (at least in English).

LLMs produce perfectly fluent output and can understand natural language input as well as any human.

However knowledge representation is not solved. We still don't know how to interface a perfect LLM to other systems in the same way a human does things like looking up facts we aren't confident of or using a calculator to do math we cant' do in our head.

These are very significant problems and super important. But they are more adjacent to NLP in the same way tasks like something like Text-to-SQL [1] isn't a pure NLP task.

[1] for example https://github.com/salesforce/WikiSQL

Thanks for sharing, that looks great! That Datapane example is more a hello world web app that runs Python code in the backend (so requires a backend server - Fly.io in this example).

An example of a standalone report would be something like this, from one of our users: https://cloud.datapane.com/reports/dkjbvwk/literature-in-blo... (code: https://github.com/ryancahildebrandt/hanakotoba) or https://cloud.datapane.com/reports/aAMaqoA/when-fact-is-fals...