kristofer / PolymorphismInheritance.java
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| 1 | class A |
| 2 | { |
| 3 | public void methodA() { |
| 4 | System.out.println("method of Class A"); |
| 5 | } |
| 6 | } |
| 7 | class B extends A{ |
| 8 | public void methodB(){ |
| 9 | System.out.println("method of Class B"); |
| 10 | } |
kristofer / Inheritance.java
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| 1 | class A |
| 2 | { |
| 3 | public void methodA() { |
| 4 | System.out.println("method of Class A"); |
| 5 | } |
| 6 | } |
| 7 | class B extends A{ |
| 8 | public void methodB(){ |
| 9 | System.out.println("method of Class B"); |
| 10 | } |
kristofer / HeapSort.java
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| 1 | import java.util.*; |
| 2 | public class HeapSort { |
| 3 | public void sort(int arr[]) |
| 4 | { |
| 5 | int N = arr.length; |
| 6 | |
| 7 | for (int i = N / 2 - 1; i >= 0; i--) |
| 8 | heapify(arr, N, i); |
| 9 | |
| 10 | for (int i = N - 1; i > 0; i--) { |
kristofer / WebsiteStatusChecker.java
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| 1 | import java.io.IOException; |
| 2 | import java.net.HttpURLConnection; |
| 3 | import java.net.URL; |
| 4 | import java.util.Scanner; |
| 5 | |
| 6 | public class WebsiteStatusChecker { |
| 7 | public static void main(String[] args) { |
| 8 | Scanner scanner = new Scanner(System.in); |
| 9 | System.out.print("Enter the website URL to check: "); |
| 10 | String urlToCheck = scanner.nextLine(); |
kristofer / FastAPI_auth_example.py
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| 1 | from fastapi import FastAPI, Depends, HTTPException, status, Header |
| 2 | from sqlalchemy import create_engine, Column, String, Integer |
| 3 | from sqlalchemy.ext.declarative import declarative_base |
| 4 | from sqlalchemy.orm import sessionmaker |
| 5 | from keycove import encrypt, decrypt, hash, generate_token |
| 6 | from sqlalchemy.orm import Session |
| 7 | |
| 8 | app = FastAPI() |
| 9 | |
| 10 | SQLALCHEMY_DATABASE_URL = "sqlite:///db.sqlite3" |
kristofer / AsyncWithFASTAPI.md
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Useful, from FastAPI, description of Async and Await within concurrency in Python
kristofer / O'Reilly Research
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the items traced by reading the 3-part series.
The original Three articles.
- https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/
- https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-ii/
- https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-iii-strategy/
And then the Rabbit Holes.
- https://www.microsoft.com/en-us/research/blog/the-power-of-prompting/ Microsoft's ideas on prompt engr rather than fine-tunning.
- https://www.linkedin.com/blog/engineering/generative-ai/musings-on-building-a-generative-ai-product LinkedIn's App work description.
kristofer / guidance-example.py
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| 1 | import guidance |
| 2 | gpt35 = guidance.models.OpenAI("gpt-3.5-turbo") |
| 3 | |
| 4 | import re |
| 5 | from guidance import gen, select, system, user, assistant |
| 6 | |
| 7 | @guidance |
| 8 | def plan_for_goal(lm, goal: str): |
| 9 | |
| 10 | # This is a helper function which we will use below |
kristofer / PyObjects.py
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| 1 | print("1 Hello World!") |
| 2 | |
| 3 | class HelloWorld: |
| 4 | def main(self): |
| 5 | print("2 Hello World!") |
| 6 | |
| 7 | obj = HelloWorld() |
| 8 | obj.main() |
| 9 | |
| 10 | # What do you MEAN Python is an Object Oriented Language? |
kristofer / symboltable2.md
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describe how you would use python's dict data structure to build a symbol table for a small language compiler
TinyLlama
In a small language compiler like Python, the goal is to convert an input program into a bytecode that can be executed by an interpreter. This bytecode is then translated into machine code (CPU instructions) and executed by a CPU, or run directly in a virtual machine (VM). The intermediate representation for this process is called a symbol table,