eli_yumnik

:O

I think I can make it work. I'm gonna have to reach out to their owner and see if we can get the ball rolling. Lol.

No. CC: the last issue I'm mentioning. The extraction cadence is incredibly convoluted and irregular - normalizing the data collection into 1 readable normalized format is the hardest part. I'm wondering if anyone else has faced an issue like this, and weather coverage or fresh data is of priority.

Thanks for this! I'm going to review that right now. But glad to get a validation that it was true when they were open :D

This looks like a REALLY cool tool for tech-focused startups. I like that its not just resolving the stripe bloat but also the code. You can setup a full production-ready payments processor without worrying about the code.

I'm wondering - is there some overhead required to keep operations up? Or Flowglad takes care of everything between code and compliance

Also, sick sweater I have the same one XD

I built a food delivery price optimization platform that algorithmically stacks discounts across multiple layers (store promos, credit card rewards, gift cards, etc). Initially, I found a lot of discounts were hiding behind the massive marketing budgets of delivery corporations. I originally architected cross-platform price comparison, but delivery platforms' terms of service explicitly prohibit displaying "priced items from multiple retailers on the same screen." This forced a complete pivot from comparison to single-platform optimization. Basically they don't want me comparing their prices and promos to competitors. Makes sense.

So my adapted approach is: Instead of price comparison, the system takes your list/search terms and builds optimized platform-specific carts.

- Real-time promotion discovery - Credit card reward optimization - Smart cart building so it can qualify for all discounts - Uses ML to suggest product swaps that maintain savings while improving nutritional value - Predictive rebate matching based on shopping patterns

In early testing I'm seeing around 35% higher cart values on average. So I really think the idea has legs and I don't want to give up, but navigating these partnership dynamics as a solo founder is proving to be challenging.

Has anyone else had to completely restructure their product architecture due to partnership requirements? How do you balance technical capabilities with business partnership limitations?

I built a food delivery price optimization platform that algorithmically stacks discounts across multiple layers (store promos, credit card rewards, gift cards, etc).

Initially, I found a lot of discounts were hiding behind the massive marketing budgets of delivery corporations. I originally architected cross-platform price comparison, but delivery platforms' terms of service explicitly prohibit displaying "priced items from multiple retailers on the same screen." This forced a complete pivot from comparison to single-platform optimization. Basically they don't want me comparing their prices and promos to competitors. Makes sense.

So my adapted approach is: Instead of price comparison, the system takes your list/search terms and builds optimized platform-specific carts.

- Real-time promotion discovery - Credit card reward optimization - Smart cart building so it can qualify for all discounts - Uses ML to suggest product swaps that maintain savings while improving nutritional value - Predictive rebate matching based on shopping patterns

In early testing I'm seeing around 35% higher cart values on average. So I really think the idea has legs and I don't want to give up, but navigating these partnership dynamics as a solo founder is proving to be challenging.

Has anyone else had to completely restructure their product architecture due to partnership requirements? How do you balance technical capabilities with business partnership limitations?