[hartem_]

It's similar to javascript, but not exactly identical. Here is an example that sends a summary of calendar event two minutes before it starts (uses gmail, openai and slack):

``` function (recipient) {\n when: __0 = GoogleCalendar.when_next_event_is_in(time: B.Duration(120000));\n __1 = GoogleCalendar.get_next_event();\n __2 = OpenAI.get_summary_of(text: __1.description);\n __3 = BardeenCommons.get_string_concatenating_strings(strings: ["Your next event is:", __1.summary, "at", __1.startTime, "Here is a summary of the event:", __2]);\n Slack.send_message(message: __3, recipient: recipient);\n}' ```

Initial accuracy was about 10%, which was pretty meh TBH. With a lot of tweaking and tuning we were able to get it to 70%. This means that it takes about 2-3 attempts to get it to generate what's expected.

The great thing is that we only use AI to generate the DSL description of the automation and let the user tweak and tune it. Once it's there we just execute it with our engine.

[davidbuniat]

nice, pretty impressive to go from 10% to 70%. Do you also do autogpt style looping with reasking and verification until proper DSL is creating?

[hartem_]

Not yet because honestly we were able to get to where we needed in terms of accuracy without it. Creating a feedback loop and turning into an agent-style interaction can be helpful for more complex automations, but honestly, you can get a lot of mileage out of what we have. Having said that, agents look really fascinating and some demos I saw are mind blowing, so we will definitely look into it very closely.