👍VIEW/CLONE ALL MY NOCODE APPS + support my work: www.patreon.com/jamesnocode 👍GET MY NEW TRAINING - MASTERING FLUTTERFLOW: masteringflutterflow.com 👍GET MY NEW TRAINING - MASTERING SUPABASE: masteringsupabase.com
Hey James! Any way you could make a video on making edge functions for webhooks in supabase?? Thank you so much for bringing so much value to the no code space. Been subscribed for a couple years. Thank you 🙏
Hi James! I liked your 2nd example - the Recommendations. However, having been involved with many projects, what I found is that attributing flat points to various interaction types, starts to screw up the results at scale. That's why usually there's some type of log(n) formula involved or an algorithm - and quite complex one. Things become more complicated with dynamic data (authenticated user id, UX, time of day, position and significance of a particular content item, etc). I'm facing this problem with an ad display system that I've built and it works almost like your recommendation table and the results are not that great in terms of UX. So what I thought would be a super useful tutorial is to take the logic of your example #2 to the next level: how to connect OpenAI, feed it dynamic data from various tables from Supabase and ask it to spit out a ranking of recommendations based on a multitude of dynamic factors and UX preferences, and then pick up the IDs of those rankings in FlutterFlow and display them as content accordingly. Basically, making the AI handle the backend recommendations engine based on Supabase data.
👍VIEW/CLONE ALL MY NOCODE APPS + support my work: www.patreon.com/jamesnocode
👍GET MY NEW TRAINING - MASTERING FLUTTERFLOW: masteringflutterflow.com
👍GET MY NEW TRAINING - MASTERING SUPABASE: masteringsupabase.com
Hey James! Any way you could make a video on making edge functions for webhooks in supabase?? Thank you so much for bringing so much value to the no code space. Been subscribed for a couple years. Thank you 🙏
Would love much more videos on flutterflow and supabase integration.
Hi James! I liked your 2nd example - the Recommendations. However, having been involved with many projects, what I found is that attributing flat points to various interaction types, starts to screw up the results at scale. That's why usually there's some type of log(n) formula involved or an algorithm - and quite complex one. Things become more complicated with dynamic data (authenticated user id, UX, time of day, position and significance of a particular content item, etc). I'm facing this problem with an ad display system that I've built and it works almost like your recommendation table and the results are not that great in terms of UX.
So what I thought would be a super useful tutorial is to take the logic of your example #2 to the next level: how to connect OpenAI, feed it dynamic data from various tables from Supabase and ask it to spit out a ranking of recommendations based on a multitude of dynamic factors and UX preferences, and then pick up the IDs of those rankings in FlutterFlow and display them as content accordingly.
Basically, making the AI handle the backend recommendations engine based on Supabase data.
pure gold. I love the mapping content. for me just that alone is worth joining the patreon :)
🎉thank you once again for this, James
Should I use Firebase or supabase??
is this habit tracker available on your patreon?
Yes it is
@@jamesnocode Great!