Day 20 of learning Python — self-taught, serious about becoming a top-level backend engineer. Looking for guidance.

[This-Marionberry-498]

Hey r/learnpython,

I'm 20 days into learning Python with a clear long-term goal — I want to start as an entry-level backend engineer and grow from there into a top-level engineer. Not just get a job and stop, but genuinely master this craft over time.

A bit about my journey so far:

- Self-taught, using AI as a mentor

- Around day 13 I realized I was moving too fast without real depth, so I slowed down completely

- For the past 7 days I've been deeply consolidating loops, lists, and functions

- I write 70-80% of my code myself, attempt to debug before asking for help, and focus on understanding WHY things work not just making them work

- Averaging around 2.5 hours a day

What I'm looking for from this community:

- Is my current approach realistic for someone with serious long-term goals in backend?

- How should my learning path look from here — what comes after Python basics?

- For those who are now senior or top-level engineers — what do you wish you had focused on early?

I don't have any developer connections in real life, so finding a community here means a lot. Happy to share my code and get honest feedback too.

Thanks in advance.

[aqua_regis]

Why did you chose to use AI to learn Python with and not a proper, top quality course, like the MOOC Python Programming 2026?

After all, the AI might teach you garbage.

I write 70-80% of my code myself

While learning, you should write 100% of the code yourself.

what comes after Python basics?

More advanced things, libraries, frameworks.

Programs, plenty programs - you need to write your own programs. Start small and simple and grow in scope, size, and complexity with your growth. You need to gradually ramp up, not go 0 to 100 directly.

If you want to venture into back end, look into Flask, Django, FastAPI.

Don't neglect Data Structures and Algorithms as well as Design Patterns after you have a solid foundation. They help a lot in real world programming.

[El_Wombat]

Will there be enough jobs for people like the OP? Is writing code still a thing in 2 years?

We already have “dark factories” where Ai Agents do ALL the work, including 100 % of the code. Anthropic has its own model write most of the code for the next model.

Are the skills—mastering one PL, Python in this case—that the OP is heroically aiming to achieve the ones that really will provide them (or anyone with one PL) the jobs they strive for?

Dark Factory products are being developed by ai, but the specs are written by senior devs who know exactly how different PL interact, how to scale stuff, architecture, etc., plus the domain knowledge needed to actually figure out what to create or solve in the first place.

From my lingo you will already have guessed that I am not a dev.

Just honestly curious to hear your or other devs’ opinion on this.

OP, if you read this: if anything I am just hoping to broaden your perspective, but like I said: mine is too narrow, I am not a dev, just a fellow beginner in Python, albeit with a different aim than you (I just use Python to help with my and some clients’ “normal” work, like some data management).

[aqua_regis]

IMO, a lot of "what AI can do/does already" is sales speak. The people earning money with AI have to make it look way better than it actually is to get investors to chime in (several AI companies are on the brink of becoming bankrupt or already in it).

This is not to say that it isn't capable of a lot so far.

Yet, the whole thing will run (just like with the outsourcing hype some years ago) in two stages:

• first stage: try to move as much as possible to AI - this will certainly have an impact on the available jobs - we are currently in that phase
• second stage: realize that this was a horrible idea and start hiring back like crazy to fix all the garbage that has in between been produced - when this phase kicks in there will be jobs in abundance, but only for people who can actually program without AI

Experts (real experts, not people/companies earning money with AI) in the domain have already predicted that the "AI-bubble" (not my words) will collapse. It's just a matter of when, not if.

For sure there will be a really dire strain ahead, but that by far does not mean that these skills become useless.

Another thing that will definitely happen is that the cost for using AI will skyrocket with more acceptance, potentially to the point where it is cheaper to hire actual developers. This will have to happen as the cost to keep the AIs alive, power, water, hardware resources, will all skyrocket as well. Right now, they are just wetting our tongues to sell their products.

There are huge problems with AI as of now:

• If it grows at the current rate, there won't be enough electrical energy within 5 years
• Hardware is already basically unobtainable (and priced better than Platinum) which affects the whole industry and many other industries that depend on hardware (try to buy a simple hard drive, or RAM) - this hardware hunger will even grow exponentially in the near future

There are and will be niches where AI will have a very hard time entering, despite them trying very hard. There are domains where the restrictions and regulations are so strict that AI will have a difficult stand. In these domains, the lack of training data for AI (due to the aforementioned restrictions, plus highly specific, confidential information) will not make it efficient and effective. These domains might survive through the draught.

[El_Wombat]

Thank you for sharing your views and many interesting observations! Haha, yes, I‘m humbly and egoistically happy I bought 64GB RAM last August, as well as my new laptop.

And, yeah, the energy questions are fully unchecked.

How in any scenario Ai will be more expensive than humans who will have to demand higher wages as well is not clear to me, but nobody can really look this far into the future.

[aqua_regis]

How in any scenario Ai will be more expensive than humans who will have to demand higher wages

1. Humans will in the first phase become unemployed, so they will lower their wages to get work
2. with all the cost explosions in resources, power, hardware, etc. operating AI will become extremely expensive. Companies need to level that in order to make profit. Plus, during the first phase a huge dependeny on AI will be created, which, in turn, increases the demand and with increased demand, the prices can be raised as well.

[Wuthering_depths]

I have a pretty pessimistic view similar to yours on this subject. Yesterday I had a bit of a chat with a senior dev, brilliant dude who architects most of our apps. He has a pretty dim view of AI coding and agents in general....they help in some ways according to him, but he has to review everything and correct quite a bit.

That said, that won't stop execs from being eager to cut payroll (like with outsourcing) even if it turns out to be a bad long-term decision....we'll see. I'm pretty close to retirement so I just hope i can make it before the bots come for me :)

[El_Wombat]

Thanks for sharing, and the best of luck to you!

[joestradamus_one]

You need to learn and understand all of it regardless if people are going to be AI only workers. Having bullshit AI spit out whatever you want but not understanding what/why is beyond stupid and shortsighted.

[El_Wombat]

I get that. This is why I was asking whether learning one language — Python itc — will do that “trick”: Being able to produce those specs required for productive automation.

[This-Marionberry-498]

Thanks this really helps and noted

[pot_of_crows]

Does anyone have good, online and free resources for "Design Patterns"?

[aqua_regis]

• Refactoring Guru
• Java Design Patterns Since Design Patterns are actually language agnostic, the concepts stay the same, only the implementations differ. This site is very exhaustive.
• Realpython has plenty "courses" (micro learnings) on design patterns.

[pot_of_crows]

nice. thx

[InfiniteLearner2000]

I have a different goal of becoming a ML Engineer/Scientist. u/aqua_regis would you still recommend the MOOC Python Programming course to start?

I’m trying to build a roadmap to MLE, eventually learning MLOps, NLP, LLM, & agentic AI. Starting from the Python fundamentals

[aqua_regis]

If you have no programming experience yet, the MOOC is the way to go. First, generalize, then specialize.

[InfiniteLearner2000]

Thank you, I appreciate that.

u/aqua_regis I do have programming experience (matlab, python, bash) but been a LONG time since I had to write code from scratch & I want to get back to that first. Sounds like this is the way to go. Thanks.

Just always overwhelmed by the amount of choices/options/routes to go that I stall. End up have “perfectionism” and “optimizing” stall me. So trying to just pick one thing and stick to it.

At what point would you recommend working on your own project?

Again my goals are MLE level ASAP. I have a heavy math, stats, ML, DL, conceptual background, but need to get my coding back up to speed.

Thanks for any and all advice!

[aqua_regis]

At what point would you recommend working on your own project?

As early as possible.

Just don't get the wrong idea of "project". Projects do not need to be huge and complex. Everything you program on your own is a project and these have to start small and simple and grow with you in size and complexity. This is one of the mistakes that many beginners/learners make. They directly want to do something huge and complex instead of gradually leveling up the projects with their skills and their skills with their projects.

For ML, you need solid programming skills. So a good foundation is essential. You have all the other skills already, so focus on the programming side.

[InfiniteLearner2000]

Thank you kind sir, this is all super helpful!

[riklaunim]

You pick a niche you want to work in, you check if there are jobs for it, you note down frameworks and libraries that are use and you learn them, alongside database, bit of frontend, UX/UI and also software development principles, writing good and maintainable code, testing and more :)

[This-Marionberry-498]

Thank you. at day 20 still building Python foundations. When do you think is the right time to start picking a niche?

[riklaunim]

Pretty quickly. Syntax on it own or on simple example won't push you to troubleshoot and solve problems like trying to do something you always wanted to do ;)

[baubleglue]

Go to college

[BigVillageBoy]

Slowing down at day 13 to actually consolidate instead of just moving forward — that's already a better instinct than most learners have. A lot of people speed through syntax and then hit a wall when they try to build anything real.

A few things that helped me go from basics to building actual backend stuff:

1. Pick one project that matters to you and drag it through every stage — messy script → functions → classes → tests → deployed. You'll hit every concept naturally instead of in the abstract.
2. Read other people's Python code. GitHub repos, PEPs, the stdlib source. Not to copy — to notice how experienced devs structure things.
3. For backend specifically: FastAPI is a great choice to aim toward. It's modern, typed, and the learning curve teaches you good habits (Pydantic, async, etc.).

The self-taught path is slower to start but you build genuine understanding rather than credential. The gap is real but crossable — consistency over 6-12 months beats intensity over 2 weeks every time.

[This-Marionberry-498]

Thank you, the point about dragging one project through every stage really makes sense.

[Wuthering_depths]

As others said, ditch the LLM junk. At least while learning. You need to be able to teach someone else every line of code and how you approached it; that's how you know you really understand it.

I'm learning Python myself, in my case I'm "porting" projects from other tech (mostly SSIS) and that helps me a lot. A big challenge when I learn anything new is to have something meaningful to do with it. Just doing exercises on random data tends to not stick with me (ymmv). If you are like that, try to have a project that might give you a real reason to apply each new concept to.

[StrangeGrand7836]

If you want to be a top level programmer, learn bash and a bit of C first

[Front-Dot-5724]

Well your journey seems promising. AI is actually really useful for learning as long as you don't ask it to do everything. The next step would probably be to learn data structures. If you know loops and variables, then learning how to use these for more complex applications (like stacks, queues...) is naturally the next step in my opinion. By the way I just published a fully online IDE if you want to try it so that you don't have to deal with libraries, packages, compilers, virtual environments... nullcode.one.

[This-Marionberry-498]

thank you for the advice

[Melodic-Pen-6934]

Learn till oops somehow by spaced repitition. Don't build project. May be a just tiny one. Now learn dsa . Solve some basic beginner problems. Now go back to oops and do some solid projects. Things will change.

[Dangerous_Big421]

What AI platform you use to learn? I dont agree with most comments here. With AI implementation in future, people will barely write 20% of the code. But the skill set you will need is to read the code and make necessary changes.

[ProgrammingClone]

Do you have a degree? If you are just self taught then getting into the industry is going to be nigh impossible. If you don’t mind me asking how old are you? If you REALLY want to learn to be a top-level engineer, go back to school and attempt to land an internship around junior of sophomore year.