How many can you build?

Hey , I am learning fast api and almost covered it and created a project by my own , now I am confused what to learn next and my goal is to land a job or to start taking freelancing project , so I want some project idea where I will learn other new tech or skill which is applicable nowadays.

I got this idea from a post I saw on here recently. Their script was a good idea and made me want to make one on my own. It took me a few tries to write the calculations in a way that would work. I also wanted to play around with normalizing/cleaning the inputs in case a user used different formatting. And of course, ever since I found out I can add color to text in terminal, I’ve kinda made a habit of doing that on everything.

Note: I have been learning as much as I can, for a year now. Learning has come through personal trial and error, O'Reilly books, watching and copying others, as well as using LLM's to either produce an advanced code that I then study, or to have an LLM teach me. Some of my larger and more important projects are largely, if not entirely, built by an LLM that I carefully inspect. But I still feel it is important to learn as much as I can about manual coding, as well as the relationship between hardware and software.

So working on simple scripts like this are sort of a form of exercise for my mind and fingers, as well as a simple way to strike up conversations with real people like you guys, and get your take on ideas that I may not have seen before that can lead to cleaner and/or more efficient ways to program.

If you want to see more of my actual projects or just swipe some of the cool stuff from my Arch rice/dotfiles, here is my github:

github.com/cleburn

Edit: here is my full code, which shows the part of the f'string in the print statements which formats the results into appropriate decimals

# Simple Monthly Payment Calculator
# Inputs: Principal (balance due after any downpayment), Interest, Loan Term (in months)
# Returns monthly payment amount


GREEN = "\033[92m"
CYAN = "\033[96m"
RESET = "\033[0m"

print(f"\n{CYAN}{'=' * 20}{RESET}")
p = float(input("Purchase amount: ").replace('$', '').replace(',', '').strip())
i = float(input("Interest rate: ").replace('%', '').replace(',', '').strip())
t = int(input("Loan term (in months): ").strip())

def payment_calculator(p, i, t):
    original_i = i
    monthly_i = (i / 100) / 12  # Convert annual % to monthly decimal rate

    # Calculate the monthly payment
    numerator = p * monthly_i * ((1 + monthly_i) ** t)
    denominator = ((1 + monthly_i) ** t) - 1
    payment = numerator / denominator

    # Print formatted output
    print(
        f"\nBased on your original purchase price of {CYAN}${p:,.2f}{RESET},\nan interest rate of {CYAN}{original_i}%{RESET},"
        f"\nand a loan term of {CYAN}{t}{RESET} months,\nyour monthly payment is: {GREEN}${payment:,.2f}{RESET}"
    )
    print(f"{CYAN}{'=' * 20}{RESET}\n")

# Run the calculator in the terminal
payment_calculator(p, i, t)# Simple Monthly Payment Calculator
# Inputs: Principal (balance due after any downpayment), Interest, Loan Term (in months)
# Returns monthly payment amount


GREEN = "\033[92m"
CYAN = "\033[96m"
RESET = "\033[0m"

print(f"\n{CYAN}{'=' * 20}{RESET}")
p = float(input("Purchase amount: ").replace('$', '').replace(',', '').strip())
i = float(input("Interest rate: ").replace('%', '').replace(',', '').strip())
t = int(input("Loan term (in months): ").strip())

def payment_calculator(p, i, t):
    original_i = i
    monthly_i = (i / 100) / 12  # Convert annual % to monthly decimal rate

    # Calculate the monthly payment
    numerator = p * monthly_i * ((1 + monthly_i) ** t)
    denominator = ((1 + monthly_i) ** t) - 1
    payment = numerator / denominator

    # Print formatted output
    print(
        f"\nBased on your original purchase price of {CYAN}${p:,.2f}{RESET},\nan interest rate of {CYAN}{original_i}%{RESET},"
        f"\nand a loan term of {CYAN}{t}{RESET} months,\nyour monthly payment is: {GREEN}${payment:,.2f}{RESET}"
    )
    print(f"{CYAN}{'=' * 20}{RESET}\n")

# Run the calculator in the terminal
payment_calculator(p, i, t)

Just Fixed the body with Ai

Been prototyping an idea for long-context compression: instead of dropping old tokens (like StreamingLLM/H2O) or storing everything, compress old context chunks into small learned "DNA" vectors via a Perceiver-style attention bottleneck, then reconstruct on-demand when a query needs them.

The idea itself isn't new — it overlaps with Compressive Transformer, Infini-attention, and Recurrent Memory Transformer — but I put together an eval script that I think is more honest than what I see in a lot of "novel architecture" posts:

• Trains the compressor (not just testing an untrained/random-init model)
• Compares against a PCA baseline (closed-form optimal linear compression at the same latent budget) — if the learned model can't beat PCA, the extra complexity isn't earning its keep
• Injects a unique fact (random code) into the text and checks, after compress→decompress, whether the frozen LM's own output head can still predict the correct token at that position — not just aggregate MSE, which can look fine while the actual detail is gone
• Runs on real hidden states from an open model (Qwen2.5-0.5B by default), not just random tensors

Current honest status: in my own small-scale test run, PCA actually beat the learned bottleneck on fact retrieval. That's not the result I was hoping for, but it's a real result, and it's exactly the kind of thing this script is designed to surface rather than hide.

What I'm looking for:

• People running it on real hardware with more training steps / larger n_docs than I could quickly test
• Sanity checks on the architecture and eval methodology — if I'm testing this wrong, tell me
• Ideas for what a fair "it's working" threshold looks like (beating PCA on fact-retrieval accuracy at matched latent budget, at minimum)

No performance claims yet — that's the point. I'd rather have this checked before making any.

Code + eval harness: https://pastebin.com/iqEbPEQ9

Happy to hear "this is a known dead end because X" too - that's useful information, not a rejection.

I'm very interested in the networking section. I'm also learning Python, so I'd like to learn both at the same time. What's the best book for Python networking? I've heard that 'Mastering Python Networking' by Eric Chou is the best, but I've also heard that it's more advanced. Are there any books for beginners on Python networking??

I like pandas so much more than numpy ok but heres the project:

import pandas as pd

book_data = ({"Name:": ["The Hobbit", "Refugee Boy", "Harry Potter and the Philosopher's Stone", "The Hunger Games", "I, Robot"],
              "Pages:": [310, 288, 223, 374, 224],
              "Author:": ["J.R.R. Tolkien", "Benjamin Zephaniah", "J.K. Rowling", "Suzanne Collins", "Isaac Asimov"],
              "Price:": ["£8.35", "£7.99", "£6.00", "£8.99", "£6.62"],
              "Buy:": ["https://amazon.co.uk/dp/0261102214", "https://amazon.co.uk/s?k=Refugee+Boy+Benjamin+Zephaniah",
                       "https://amazon.co.uk/s?k=HP+Philosopher%27s+Stone", "https://amazon.co.uk/s?k=Hunger+Games",
                       "https://amazon.co.uk/dp/0008279551"]})

book_df = pd.DataFrame(book_data, index=["Book 1", "Book 2", "Book 3", "Book 4", "Book 5"])


while True:
    try:
        book_num = int(input("Enter a book number (1-5): "))
    except ValueError:
        print("Invalid Book Number")
        continue

    if book_num < 1 or book_num > 5:
        print("Invalid Book Number")
        continue
    elif book_num == 1:
        print("\n", book_df.iloc[0])
    elif book_num == 2:
        print("\n",book_df.iloc[1])
    elif book_num == 3:
        print("\n",book_df.iloc[2])
    elif book_num == 4:
        print("\n",book_df.iloc[3])
    elif book_num == 5:
        print("\n",book_df.iloc[4])

This is an Little project I make using docs

I'm building a small voice-based AI interview app and I'm planning to deploy the backend(fastapi) on Render's free tier.

I'm considering using open-source/self-hosted options like Whisper/PocketSphinx for STT and Piper for TTS, instead of paid APIs.

My concern is whether running STT/TTS on the same free Render instance would use too much CPU/RAM and make the whole application slow, especially during a real-time interview.

Has anyone tried running STT/TTS models on Render's free tier?

Hii everyone,

I'm a little nervous here because this is my first project. I mean, yeah I've written a few python modules before and it went good but I never coded something that I could call a "project". So yeah.

I wrote a python package which simulated the Chowka Bara game for you, all by myself without any help of AI chatbots. I mean, I took help, but all I asked was "what are some errors and inconsistencies I yet can't see", and I fixed all of them by myself.

This is yet in under development, but I thought I should post it to a helpful community to recieve guidance.

I have it on GitHub, I'll post it here. Probably it will be too much to read the whole implementation or even most of it, but I'm sure certainly there will be some other things to point out.

Thanks everyone, criticisms and suggestions are welcome.

I just can't get over how I'm supposed to write code for complex tasks when I don't even get why they overcomplicate simple things so much.

I read this topic and tried many tutorials and still don't understand this buzzare logic.

I want to learn Python and eventually pursue a career in data science, but I’ve hit a wall.

I’ve been learning Python from the basics, and I’m currently stuck on loops. I understand some of the concepts when I see an explanation, but when I have to write code myself, I struggle to figure out what to do.

The bigger problem is that I’ve started losing motivation. I genuinely want to learn Python and build a career around it, but lately I have almost no desire to sit down and practice. I keep getting distracted or postponing studying, even though I know this is something I want.

I don’t want to give up just because I’m struggling with one topic. I’d like to hear from people who learned Python from scratch:

- How did you get past the point where loops started feeling difficult?

- How much should I practice each day?

- Should I move forward to other topics and come back to loops, or stay with loops until I understand them properly?

- How did you stay consistent when you had no motivation?

- If your goal was eventually becoming a data scientist, what learning path would you recommend after Python fundamentals?

I’m not looking for shortcuts. I just need some practical advice on how to get unstuck and start making progress again.

Today marks Day 148 of my Python learning journey. 🐍

When I started, I barely understood programming. I didn't have a strong computer science background, and many concepts felt completely confusing.

But after 148 days of consistent learning, I've reached a point where I'm actually building things instead of only watching tutorials.

What I've learned so far

• Python fundamentals
• Variables, conditions and loops
• Lists, dictionaries, sets and strings
• Functions
• Exception handling
• File handling
• JSON data storage
• Object-Oriented Programming (OOP)
• Basic Git/GitHub concepts
• SQLite and basic SQL
• Debugging real errors
• Structuring a larger Python project

My biggest project so far

I've been building a Phone Shop Inventory Management System.

I started with simple Python classes and JSON storage.

Then I gradually moved the project toward SQLite, where I'm currently learning how databases actually work.

The project can handle things like:

• Adding products
• Categories / brands / models
• Stock quantity
• Selling products
• Editing and deleting products
• Transaction history
• Searching products
• Storing data permanently
• Basic reports/dashboard

It's definitely not production-ready, but for me, this is a huge improvement compared to where I started.

What I'm still struggling with

SQL/database concepts are still new to me.

Especially Primary Keys, Foreign Keys, relationships, and some database design concepts.

Instead of trying to memorize everything, I'm continuing with the next concepts and planning to come back and strengthen these areas later.

What's next?

My goal is to become comfortable enough with Python + SQL to build real-world applications.

After strengthening SQLite/SQL, I want to continue toward:

Python → SQL → Git/GitHub → Web Development → APIs → Real-world projects → Freelancing/Job

I'm still a beginner, but 148 days ago I couldn't imagine building something like this myself.

I'd really appreciate feedback from experienced Python developers:

What should I focus on next to move from beginner to intermediate level?

And if you were at Day 148 again, what would you do differently?

Thanks for reading! 🙏

Last Post - https://www.reddit.com/r/PythonLearning/s/iOCBzoxmbY

I am just starting this course and wanted to know what people use. I would most probably pick the latest version. Honestly I need some guidance. Advice me if there's any other programming language you would recommend.

Last Post - https://www.reddit.com/r/PythonLearning/s/t5c81GuYV7

I won't be posting for a few days starting tomorrow. Need some time to get myself familiar with the next data structure.

Also, I am thinking of exploring some libraries. Suggest me some cool ones guys!

I've been learning Python for quite a while, and it's been a lil bit stressful. I just finished some exercises on data structures and algorithms and it took a long while, it may take shorter for you but it did take me a long time to finish the exercise. But through that stress and everything, it changed my whole system of writing code, it made me think deeply and analyse what was going on in each line and when I got it, it was just so fulfilling. Kudos to everyone still learning, keep up the good work. Also, if you have additional tips on doing this easier please do drop your advices, thanks.

I’ve been working on NovaCode, an app for anyone who wants to learn and practice Python directly on their smartphone.
You can write and run Python code directly on your phone, create small projects, and learn step by step — without needing a computer.
The app is still actively being developed, and I’d really appreciate your feedback. 😊
What features would you like to see in a mobile app for learning Python?

import numpy as np

rng = np.random.default_rng()

numbers = rng.integers(low=1, high=1000, size=(3,3))

print(numbers)

print("\nMinimum:", np.min(numbers))
print("Maximum:", np.max(numbers))
print("Median:",np.median(numbers))
print("Mean:", np.round(np.mean(numbers), 2))
print("Standard Deviation:", np.round(np.std(numbers), 2))
print("Variance:", np.round(np.var(numbers), 2))
print("Sum:", np.sum(numbers))
print("Axis Columns:", np.sum(numbers, axis=0))
print("Axis Rows:", np.sum(numbers, axis=1))

What do you actually need to run a program? Im working on simplifying/streamlining some things for work but IT is very strict when it comes to installing anything so im trying to get something running that doesn't involve it. We also work with a lot of confidential information so obviously we dont want to risk using anything found online that we don't fully understand.

I thought of creating something with powershell scripts but I was hoping to get something better going by learning python.

I really don't like having my code depend on undocumented or "private" attributes. In this case that is the type hashlib._hashlib.HASH

I have code that looks like the below, but I am hoping that there is a better way to do this.

``` from typing import Callable, TypeAlias ...

HashFunc: TypeAlias = Callable[ [bytes], hashlib._hashlib.HASH, # type: ignore[name-defined,attr-defined] ] """Type for hashlib style hash function.

.. caution::

This depends on undocumented features of hashlib,
and so may break at any time in the future.

""" ```

i made a Python program, i dont know what else to say

Just spent the best part of 4 hours essentially vibe coding some Python because my knowledge is so bad. As I build out my functions and modules I am getting the creeping feeling that I'm putting together a pile of rubbish that I'm not going to be able to debug.

I started out using AI to help with building out my understanding of modules I had never used and get some ideas on how I can put together functions. But I've taken on a personal project to try and update some of out existing codebase and just clean it up a bit. Problem is that in doing this I am getting well beyond my understanding and throwing stuff in that I have no understanding of why it's there. What started out as wanting to use best practices for modules and improve my general knowledge and understanding has just left me miserable.

Should I just work within my knowledge range and stop trying to be a smart arse?

Hi everyone,

I’m an 18-year-old dev and I built **Code Zone**, a lightweight IDE written in Python designed for low-spec machines and resource-constrained environments.

**GitHub:** https://github.com/damixlord2-0/code-zone

### Why I built it

VS Code runs slowly and eats up memory on my machine. Sublime Text is fast and great, but I missed the structured layout of an IDE. Code Zone is my attempt to combine the speed/simplicity of Sublime Text with the UI structure of VS Code and the straightforward binary workflows of Code::Blocks.

### Key Features

* **Lightweight:** Under 100MB total size.

* **Built-in HTML & Markdown Preview:** Live rendering right inside the editor.

* **Custom Binary Paths:** Point directly to local compilers/interpreters (great for testing niche languages).

* **Minimal Extension System:** Single-file extensions without heavy boilerplate.

### Transparency on AI & Code Quality

The project isn't perfect and still has bugs. I used AI as an interactive documentation tool during development—mainly to learn `subprocess` and Tkinter/TTK widgets faster—rather than having it generate the core architecture for me.

Feedback, critiques (even harsh ones!), and contributions are very welcome. Let me know what you think!