kristofer / HeapSort.java
Naposledy aktivní
| 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
Naposledy aktivní
| 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
Naposledy aktivní
| 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
Naposledy aktivní
Useful, from FastAPI, description of Async and Await within concurrency in Python
kristofer / O'Reilly Research
Naposledy aktivní
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
Naposledy aktivní
| 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
Naposledy aktivní
| 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
Naposledy aktivní
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,
kristofer / symboltable1.md
Naposledy aktivní
describe how you would use python's dict data structure to build a symbol table for a small language compiler
Phi3
Building a symbol table using Python's dictionary data structure for a small language compiler involves several steps. The symbol table will be used to keep track of variables, their types, and scopes during the compilation process. Here's how you can implement it:
- Define basic classes