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I would advise developers worried about these kind of tools to focus on the skill that really differentiates a developer who is basically effective from one who isn't, which is data modeling.

These things shine a spotlight on your high level data skills because you can't just code around bad database design decisions early in the project.

When the data is exported, your low level attention to detail and consistency come into focus. Did you encode Yes/No questions consistently? Were there free text fields where there should have been predefined options?

These low code/no code options are just revealing that coding was never the hard part of making software.

Could you please elaborate? What tools do you interact with? Is this mostly backend work, getting the data out of no code tools for further processing?

The other part is building custom apps that run alongside the no-code app, often backed by the API. I've implemented some fun things like transactions that lock so you don't have simultaneous write problems and full text search.

If you are in that business, the distinguishing features that I would look for is being able to use the relational model, an API or way to run code, and regulatory compliance (HIPAA, FERPA, 21 CFR Part 11). Native transactions would be a miracle (for instance update a record and associated child records and roll back if validation fails).

I'm thinking about picking up some freelance work in this area. Not really sure where to start

May seem simple at first, but you'll see it has more complicated features like:

- Tool searching/Filtering - Commenting - Complicated form to select tools from a database-generated list - Login/User System - Avatar Generation from API

Seems hard to scale, which isn't necessarily a problem, but I'm wondering:

1) How are you accessing audience data? Is this by scraping their insta or youtube or something or are you directly accessing accounts? Given an account is literally an influencer's livelihood, I can imagine that would be especially risky for them?

2) How do you relate audience products to data? Are you saying, for example, that if an audience is 50% metropolitan females aged 18-24 then it is likely they would purchase beauty products? Or do you go more in depth?

3) And then as a result...how do you intend to scale that? Machine learning?

Anyway...love it!

1) Influencers give us access to thei accounts directly so we can look at their audience data and create a product portfolio that works best for them! So far they have not had any concerns in giving us permissions to access that data.

2) We use both audience data (income level, age, etc) + market research (for pricing as an eg.) + previous product launches (that we did for other influencers in the cake category as an eg.) in a category to create an ideal product portfolio for a given influencer.

3) Yes, we are doing a lot of this manually at the moment - trying to understand the winning sauce. But AI/Machine Learning to scale this in the future!

Typically it’s ARR x 4

Who is downvoting this?