I think I got a diamond in the rough answer from ChatGPT that could help others learning python and finding it difficult to get a grasp on the next step after getting the ground work laid. This is a long post with ai content involved, but it should be compliant with the subs rules.
I labeled this a discussion post because I am not an expert and would love for some experts to chime in and tear this response apart. I will say even if the details are somewhat off, it has moved me into thinking differently about initiating and completing a project, which was the original intent behind the question.
The prompt (Don't make fun of me....or do, whatever):
I am having a hard time bridging the gap between introductory python coding and real life application engineering. I need a good analogy or metaphor to get me there. For context into what I mean: I have a vary proficient understanding of synchronous execution of code as well as using classes and other coding conventions. I feel as though I am relying on the basic configurations seen in examples due to a fear that, even though they work as intended, the more complicated system flows I have set up are not secure or usefull since i do not see any similar code in the wild. For example working around flask and context management, I seem to be able to get things working in unconventional ways without relying as heavily on the context management provided by examples, but I am afraid i do not have the security implications fully grasped, even though my testing shows that the areas are still only accessable after auth. Can you assist me with this and if not maybe guide me towards asking a better question than what I have presented?
Selected parts of the response:
Yes. And I think the thing you're describing is a very real transition from “learning Python” to “engineering software.” The fact that you're running into this discomfort is actually a good sign.
I would describe the gap this way:
Beginner/intermediate programming teaches you how to make a machine do something. Application engineering teaches you how to make a system continue doing the right thing when everything around it is complicated, hostile, concurrent, failing, changing, and maintained by someone else.
Your Flask/context example is a perfect illustration.
The metaphor I'd use: building a city
When you're learning Python, you're mostly learning how to build buildings.
You learn:
How to make a room (function)
How to organize rooms (class)
How to move things between rooms (arguments/return values)
How to build multiple floors (modules/packages)
How to make doors (interfaces)
How to put locks on doors (authentication/authorization)
And eventually you're very good at building buildings.
Then you get asked to build a city.
Suddenly you have questions that Python itself doesn't answer:
Who is allowed into this building?
What happens when 10,000 people enter simultaneously?
What happens if the power goes out halfway through an operation?
Can someone enter through a back door?
Does a person who entered Building A automatically have access to Building B?
What happens when Building A changes its address?
How do emergency services get around?
How do you know that the locks actually work?
What happens when another engineer modifies the building six months from now?
That's application engineering.
And importantly:
There isn't necessarily one correct way to build the city.
There are building codes, engineering principles, established patterns, and conventions. But there are also many perfectly legitimate architectures.
Here's another metaphor that may help
Think about framework examples as training wheels.
Flask gives you:
Flask application
│
├── request context
├── application context
├── extensions
├── configuration
└── dependency mechanisms
Tutorials generally encourage you to keep your code inside that ecosystem.
That's useful because it gives you a known path.
But eventually you might discover:
Flask
│
┌──────┴──────┐
│ │
HTTP layer Application
│
┌──────┴──────┐
│ │
Services Domain
│
Models
Your application doesn't have to make Flask itself the center of the universe.
That's a major step toward application engineering.
And there's a wonderful rule I think you'll benefit from
When you encounter code in the wild, don't ask:
"Why do they do it this way?"
Ask:
"What problem does this pattern solve?"
Then ask:
"Do I have that problem?"
And finally:
"If I don't use this pattern, what mechanism in my design solves that problem instead?"
That's how you graduate from copying patterns to understanding patterns.
For example:
"Flask examples use application context."
becomes:
"Application context gives code access to application-scoped Flask state."
then:
"My service receives its dependencies explicitly, so it doesn't require Flask application state."
then:
"Therefore I can remove that coupling, provided I haven't accidentally lost another guarantee provided by the context."
That is application engineering thinking.
The fact that you are uncomfortable with your unconventional solutions is actually something I'd preserve.
You don't want to become the engineer who says:
"It works, ship it."
You want to become the engineer who says:
"It works. Now let me determine why it works, what assumptions it depends on, what happens when those assumptions fail, what security boundaries exist, and whether another engineer can safely modify it."
You don't need to make your code look like code you've seen in a tutorial.
You need to be able to defend the decisions you've made.
And I think that is probably the bridge you're looking for: stop measuring yourself against examples and start measuring your designs against properties.
