i have created my first github project for my college mini project.
your review will help us a lot.

Apex Driver Lab

Qubit-007/Apex-Driver-Lab
A procedural driving simulator and reinforcement-learning sandbox built with Python and PyTorch. Train a policy against generated roads and traffic, inspect its behavior in a live GUI, and resume from versioned local checkpoints.

This project is an educational simulation. It is not designed, validated, or safe for controlling real vehicles.

Capabilities

Hey everyone! I’ve been working on a project that organizes AI tutoring prompts into a structured learning path. I think AI has a lot of potential for education, and I wanted to explore how much more useful it could be with a clear curriculum and specific teaching instructions.

The current catalog covers Python in 28 learning blocks, from installing Python and writing your first program to testing, databases, APIs, and deployment. Each block has learning objectives and a prompt you can use with an AI assistant.

The prompts ask the AI to explain concepts, check your understanding through open-ended questions, give you practical exercises, and review your work. Based on your answers, it should revisit anything you’re struggling with and adapt to your pace. For more complex exercises, the AI guides you through the problem without solving it for you.

My goal is to make a thorough Python learning path available for free. I believe AI tutoring could offer learning experiences comparable to paid courses, and I’d like to test how well this approach works in practice.

If you’re learning Python, whether you’re starting from scratch or filling gaps in your knowledge, I’d love for you to give it a try. I’m looking for people to use it and help me find out how well it works in practice.

If you try it, I’d appreciate hearing how it goes: what helped, what was confusing, or what could be better. But you don’t have to give feedback if you don’t want to; you’re welcome to just use it to learn.

Web: Click here and start learning Python with your AI tutor

I made a Ratio class in Python. It wasn't too hard, in fact this is very easy and can be done by anyone with a basic understanding of OOP.

import math


class Ratio:
    def __init__(self, numerator: float, denominator: float):
        self.numerator: float = numerator
        self.denominator: float = denominator

    def __str__(self) -> str:
        return f"The numerator is {self.numerator}, the denominator is {self.denominator}\nIts result is {self.calculate} \nThe lowest form is {self.lowest_form}"

    def __repr__(self) -> str:
        return f"Ratio(numerator = {self.numerator}, denominator = {self.denominator})"

    def __add__(self, ratio2: Ratio) -> Ratio:
        """Adds two ratios together, handles both same
        denominator and different denominators"""

        if self.denominator == ratio2.denominator:
            sum_numerator = self.numerator + ratio2.numerator
            return Ratio(sum_numerator, self.denominator)
        else:
            # Gets LCM of numerator and denominator
            main_lcm = self.lcm(self.denominator, ratio2.denominator)

            new_ratio1_numerator = self.numerator * (
                main_lcm / self.denominator
            )  # Gets the new numerator, e.g 1:2 + 1:3 now u get 3:6

            new_ratio2_numerator = (
                ratio2.numerator * (main_lcm / ratio2.denominator)
            )  # Gets the new numerator of thee second ratio, e.g(Following the above example) 2:6

            # Sums the two numerators
            sum_numerator = new_ratio1_numerator + new_ratio2_numerator

            return Ratio(sum_numerator, main_lcm)

    def __sub__(self, ratio2) -> Ratio:
        """Subtracts two ratios, handles both same
        denominator and different denominators"""

        if self.denominator == ratio2.denominator:
            diff_numerator = self.numerator - ratio2.numerator
            return Ratio(diff_numerator, self.denominator)
        else:
            # Gets the denominator LCM of both fractions
            main_lcm = self.lcm(self.denominator, ratio2.denominator)

            # New numerator, same logic here
            new_ratio1_numerator = self.numerator * (
                main_lcm / self.denominator
            )

            new_ratio2_numerator = ratio2.numerator * (
                main_lcm / ratio2.denominator
            )

            diff_numerator = new_ratio1_numerator - new_ratio2_numerator

            return Ratio(diff_numerator, main_lcm)

    def __mul__(self, ratio2) -> Ratio:
        """Very self explanatory"""
        prod_numerator = self.numerator * ratio2.numerator
        prod_denominator = self.denominator * ratio2.denominator
        return Ratio(prod_numerator, prod_denominator)

    def __truediv__(self, ratio2) -> Ratio:
        """For the division of two ratios, e.g 3:4/5:6"""
        # Fractional divison is very simple, (x / y) / (z / a) = xa / yz
        # The same principle has been applied here

        quo_numerator = self.numerator * ratio2.denominator
        quo_denominator = self.denominator * ratio2.numerator

        return Ratio(quo_numerator, quo_denominator)

    def __eq__(self, ratio2) -> bool:
        """Checks if two ratios are equal(in proportion)"""

        if not isinstance(ratio2, Ratio):
            return False

        return (
            self.numerator * ratio2.denominator
            == self.numerator * ratio2.numerator
        )


    def calculate(self) -> float:
        # Just returns the value of the ratio e.g 3:5 = 0.6
        return round(self.numerator / self.denominator, 12)


    def lowest_form(self) -> str:
        # Returns the lowest form, e.g 10:15 = 2.0, doesn't return Ratio to avoid recursion
        hcf = self.hcf(self.numerator, self.denominator)

        lowest_numerator = self.numerator / hcf
        lowest_denominator = self.denominator / hcf

        # If-else block so that 10:15 doesn't output 2.0:3.0
        if lowest_numerator.is_integer() and lowest_denominator.is_integer():
            return f"{int(lowest_numerator)} : {int(lowest_denominator)}"
        else:
            return f"{lowest_numerator} : {lowest_denominator}"

    def get_multiplier(self, a: str, b: str) -> tuple[int, int]:
        from_index_a = (
            a.index(".") + 1
        )  # Finds out how much you need to 10 ** to make a an int
        mul1 = 10 ** len(
            a[from_index_a:]
        )  # Figures out the 10 **, e.g for 0.05 this equals 10 ** 2 or 100

        from_index_b = b.index(".") + 1  # Same as "a"
        mul2 = 10 ** len(b[from_index_b:])

        mul_tuple = (mul1, mul2)  # Returns a tuple for easier unpacking
        return mul_tuple

    def lcm(self, a: float, b: float):
        # If two numbers are co-prime, i.e HCF(a, b) = 1, then their lcm is a * b
        if self.hcf(a, b) == 1:
            return a * b
        # Fun fact: a * b = hcf(a, b) * lcm(a, b) so lcm(a, b) = ab/hcf(a, b)
        else:
            return a * b / self.hcf(a, b)

    def hcf(self, a: float, b: float):
        # If both are ints, why go through the trouble?
        if isinstance(a, int) and isinstance(b, int):
            return math.gcd(a, b)
        elif a.is_integer() and b.is_integer():
            return math.gcd(int(a), int(b))
        # A bit tricky but easy enough
        else:
            # What to multiply with to make both floats ints
            greatest_mul = max(self.get_multiplier(str(a), str(b)))

            # Making the floats into ints
            big_a = int(a * greatest_mul)
            big_b = int(b * greatest_mul)

            # Dividing to return it back as a float and give an accurate hcf
            return math.gcd(big_a, big_b) / greatest_mul

Thoughts? Anyways no AI was used. You are free to use this code however you want.

Thanks!

Edit: Yes I do know that the Fraction class exists, this is just a project I made for fun.

Hey everyone,

Most process anomaly detection systems either trigger overwhelming alert fatigue or rely on black-box AI models that don't explain why an alert fired.

To study process-behavior detection from first principles, I built KERNEL SENTINEL — a Python-first research and detection platform.

Core Architecture & Engineering Highlights:

Since native eBPF requires a Linux kernel, I designed this environment-agnostic Python MVP to validate detection mathematics, streaming telemetry, and process correlation cleanly on Windows/Cross-platform first, while maintaining a strict interface for future native Linux sensor integration.