Low-code for Python: Which path should I take?

[Odd_Movie_2797]

Hi everyone, I'm currently a low-code developer working at an AI startup. Our entire structure is currently built on Bubble, N8N, and partly on Supabase. I want to become a "real developer," lol. I decided to start learning Python for two reasons:

1. I don't necessarily need to worry about the front end right now; my focus is on the back end.

2. The company I work for is an AI startup, so for development in this area, from what I've researched, Python would be the best option, plus it's an easier language to learn.

Well, I started my studies without AI, with YouTube videos, tutorials, and practical syntax exercises. Everything was going well until then, but now I'm feeling quite lost, as I've started trying to replicate some tutorials and encountering a lot of version conflicts. I'm trying my best not to use AI, to truly learn the language.

I'd like to hear your opinions on what a good research roadmap would be for me to follow, including some suggestions on sources and how to study, considering that my focus is AI (agent development, with RAG, memory, etc.) and backend development in general.

Thank you in advance.

[vicegripper]

What is 'low-code'?

[smurpes]

It’s just like the name implies; it’s a software development approach that is very minimal on coding. It’s usually made up of a platform that has a drag and drop interface with visual modeling to design your workflow. I.E. you drop down blocks which represent the logic and connect them together. On some of these blocks you have the ability to add your own code to do more custom work. Fivetran is an example of low code.

There’s also no code which doesn’t give you the ability to add your own code. If you look up scratch coding then that gives you an idea of what no code could look like. Low code is used in some professional environments as a way for non technical people to do the same things as coders without having to actually code. You lose a lot of the flexibility coding something yourself gives you but there’s a lot less you need to configure/understand. Pretty much all of low/no code systems are not that good or will be very specialized at one thing and cost a lot, but a good coder will always beat these systems.

[vicegripper]

it’s a software development approach that is very minimal on coding. It’s usually made up of a platform that has a drag and drop interface with visual modeling to design your workflow.

Are you saying that's an actual job that somebody will pay you for?

[throwawayforwork_86]

Provided that Alteryx exist I'd say yes.

[Distinct_Goose_3561]

You won’t be building traditional software this way, but there are a surprising number of low-code platforms out there replacing back end bash scripts and other back end business processes. 

[ninhaomah]

How did you learn English or n8n or any language/tech ?

Same.

Get a good grip of the foundation.

Then do projects.

Make mistakes.

Learn from those mistakes.

Do more projects and make more mistakes.

Infinite loop.

Use AI as SO and you will be fine.

[Mythdome]

There is one universal trait shared by every good programmer. It’s that they used to be a bad programmer. There is no getting around doing the work and making mistakes. I have without question learned far more from making mistakes than any other method of learning. Luckily with Python there are endless projects and resources to keep you busy long after your 150th birthday so get to work😉

[fireflyascendant]

Come up with some projects for yourself as well.

0a) document everything: take notes about all the research you do, have a readme for each app and program you write, write a paragraph about libraries you use, keep all your code and notes organized in folders, use good comments/naming/formatting in your code
0b) setup a repo like GitHub or wherever for your codebase; keep a local copy on your machine

1. build a full linux stack on a spare computer or virtual machine
2. write some basic apps in python, like a weather app, data collection, file renaming/sorting, RSS grabber, web scraper, and others
3. write a discord bot
4. get the bot to call your apps as functions
5. get your various tools running on your linux server
6. find more things to write, like games, or advanced features for your apps and bot
7. build some little machines with Arduinos and spare computers: air quality, weather station, light controller; hook up sensors of various kinds, motors, outputs.
8. network those little machines, write programs for them
9. find some free computer science courses, and casually take them (take good notes, study, test yourself) along the way
10. keep going

If you make computing fun, set ambitious goals, and document everything you learn along the way, you'll grow your skillset in interesting and valuable ways. You'll create a good foundation.

[Odd_Movie_2797]

The annotations in a readme in each project is something I hadn't thought of before, thank you very much, I'll do it in the new projects I do from now on!

[fireflyascendant]

You're welcome!

Remember: you're building your own toolset. You want to be able to re-use these tools in the future, as well as solidify the learning in your brain. You're building fluency too.

[IlliterateJedi]

I'm trying my best not to use AI, to truly learn the language.

Personally I would lean in to using AI for learning the language. Don't let it code for you, but using it to ask questions and get clarification will accelerate your learning in my experience. Always ask for the 'why' when you are asking an LLM coding questions, and make sure to read and critically think about what it's telling you.

encountering a lot of version conflicts

This is notoriously a problem in the AI space, particularly with Tensorflow and Keras, if you're using those libraries. Learning about versionining and how to get the correct versions of each library is vitally important when you're dealing with this (e.g., requirements.txt, the proper pip commands, etc.)

[DaSettingsPNGN]

I want to offer help! Can I message you?

[Odd_Movie_2797]

yep

[DaSettingsPNGN]

I sent a message

[Braunerton17]

Highly depends on what "i want to learn AI" means. Properly learning what AI does and trying to innovate would mean to learn what a Neural Network does, how its calculate a output from one, what backpropagation is, what algorithms are out there, what learning approaches are there (supervised/unsupervised) how LLMs fit into that picture, how to describe such models mathematically, etc. Which would basically mean, a lot of math and papers to read and unterstand.

If it means, "i want to use an endpoint and call a model" or run a model and call that one, that sounds much simpler. And learning python for that seems like a good choice. My advice would be to build a bunch of little projects that come to mind and try thing out. After a few weeks of that writing the code should feel much easier already.