Continuing from my Project Euler Problem 1, here are the next two Euler problems and some reflection!

Project Euler Problem 2

The second problem asks to find the sum of even Fibonacci numbers numbers below four million. For procedural languages, this is quite walk in the park, but for me a concise method to produce the series in Clojure proved a bit of a brain nugget!

Python 3

For Python, I decided to make a simple generator that yields the Fibonacci numbers below a given treshold N. Rest is just list comprehension:

def fib(N):
    i, j = 0, 1
    while j < N:
        yield(j)
        i, j = j, i+j

print(sum(i for i in fib(4e6) if i%2==0))

C++

In C++ it's natural to just loop with a conditional addition. This time I decided to drop using namespace std; to save a few characters. Without i, j = j, i+j syntax, the temporary variable ij is a bit ugly.

#include <iostream>

int main() {
    int i=0, j=1, s=0;

    while(j<4000000) {
        if(j%2==0) s += j;
        int ij = i + j; i = j; j = ij;
    }
    std::cout << s << std::endl;
}
}

Read post

I've long thought about learning Lisp. Some time ago I ran across Clojure and the map, set and vector implementations felt like a modern addition. It's running on top of JVM which is nice in a sense, but a major pain in the ass when installing. Well, I swallowed my wow to never install JDK again and got Clojure running quite painlessly.

To learn the ropes, I thought it would be a fun idea to rewrite Project Euler problems 1-10 with Clojure to see how it would compare against my language of choice for these types of algorithmic problems, Python.

Just to make it more fun, I decided to implement them in my second favorite language, Javascript as well. Since ECMAScript 9, it's actually a pretty powerful and concise language, and coupling it with Node.js unleashes some serious usage options outside just web pages. Npm dependency hell sucks, but that's a topic for another post. For more bare metal, I'm also doing C++ (basically sticking to C functionality until maps and sets make themselves useful). Let's see how the languages stack up!

Project Euler Problem 1

The first problem in Project Euler is simple, add up numbers below 1000 that are divisible with either 3 or 5 (or both). Modulo operator % can be used to get the remainder of a number when divided by 3 and 5, and basically we add all numbers that have either modulo as zero.

Python 3

With Python, the most concise implementation is to use the sum(iterable) and a list comprehension, netting us a nice one-liner:

print(sum(i for i in range(1000) if i%3==0 or i%5==0))

Read post

Having just spent 4 hours trying to get a Python pseudocode version of PBKDF2 to match with hashlib.pbkdf2_hmac() output, I thought I'll post Yet Another Example how to do it. I thought I could just use hashlib.sha256 to calculate the steps, but turns out HMAC is not just a concatenation of password, salt and counter.

So, without further ado, here's a 256 bit key generation with password and salt:

import hashlib, hmac

def pbkdf2(pwd, salt, iter):
    h = hmac.new(pwd, digestmod=hashlib.sha256) # create HMAC using SHA256
    m = h.copy() # calculate PRF(Password, Salt+INT_32_BE(1))
    m.update(salt)
    m.update(b'\x00\x00\x00\x01')
    U = m.digest()
    T = bytes(U) # copy
    for _ in range(1, iter):
        m = h.copy() # new instance of hmac(key)
        m.update(U) # PRF(Password, U-1)
        U = m.digest()
        T = bytes(a^b for a,b in zip(U,T))
    return T

pwd = b'password'
salt = b'salt'

# both should print 120fb6cffcf8b32c43e7225256c4f837a86548c92ccc35480805987cb70be17b
print(pbkdf2(pwd, salt, 1).hex())
print(hashlib.pbkdf2_hmac('sha256', pwd, salt, 1).hex())

# both should print c5e478d59288c841aa530db6845c4c8d962893a001ce4e11a4963873aa98134a
print(pbkdf2(pwd, salt, 4096).hex())
print(hashlib.pbkdf2_hmac('sha256', pwd, salt, 4096).hex())

Getting from pseudocode to actual working example was surprisingly hard, especially since most implementations on the web are on lower level languages, and Python results are mostly just using a library.

Simplifying the pseudo code further

If you want to avoid the new...update...digest and skip the hmac library altogether, the code becomes even simpler. HMAC is quite simple to implement with Python. Here's gethmac function hard-coded to SHA256 and an even shorter pbkdf2:

Read post

WebSocket is a protocol that allows for real-time, bidirectional communication between a client and a server. It is often used in web applications to enable features such as chat, live updates, and multiplayer games.

In this tutorial, I will show you how to create a minimalistic WebSocket server using Go and the nhooyr websocket library, and a JavaScript client to test it out. You will learn how to handle WebSocket connections, send and receive messages, and close the connection when necessary.

By the end of this tutorial, you will have a working WebSocket server and client that you can use as a starting point for your own WebSocket-based applications.

Setting up the project

You should first set up a simple "Hello world" go project, something along the lines of this tutorial. After you have a project going, let's install nhooyr.io/websocket WebSocket library (Go's own seems deprecated and Gorilla development has ceased some years ago):

$ go get nhooyr.io/websocket

The whole system will consist of main.go that will contain a simple net/http server that will:

1. Serve a simple WebSocket echo server at /echo
2. Serve static files from static subfolder – essentially other addresses including / will try content from there. We'll put index.html under that subfolder.

Basic webserver stuff:

func main() {
	address := "localhost:1234"
	http.HandleFunc("/echo", echoHandler)
	log.Printf("Starting server, go to http://%s/ to try it out!", address)
	http.Handle("/", http.FileServer(http.Dir("static")))
	err := http.ListenAndServe(address, nil)
	log.Fatal(err)
}

Now the echoHandler will do a few essential items:

1. Upgrade the connection into a WebSocket one with websocket.Accept
2. Log errors and defer connection close in case of errors
3. Loop forever (or actually 10 minutes in this sample), reading messages from the socket and writing them back.

Note that I've used InsecureSkipVerify to accept connections from any origin, you might want to modify the code for a tighter policy:

Read post

Continuing the awesome and not so unique stream of ideas on what to do with ChatGPT, here's a bit modified take to my previous post on self-running ChatGPT generated Python code. This time, let's do a shell script that takes a description of what you want as a shell command, and returns just that command. Here's how it will work:

$ shai find latest 3 files
46 total tokens
ls -lt | head -n 3
$ ls -lt | head -n 3

total 1233
-rwxrwxrwx 1 root root  5505 huhti   4  2023 python-chatgpt-ai.md
-rwxrwxrwx 1 root root 10416 maalis 26  2023 golang-sveltekit-chatgpt.md

As seen, this time I'm not running the command automatically, but just returning the command. This is a bit safer, as you can inspect the command before running it. The script is quite simple:

#!/usr/bin/env python

import sys, os
from openai import OpenAI
from configparser import ConfigParser

# Read the config file openai.ini from same directory as this script
path = os.path.dirname(os.path.realpath(__file__))
config = ConfigParser()
config.read(path + '/openai.ini')

client = OpenAI(api_key=config['openai']['api_key'])
prompt = ' '.join(sys.argv[1:])
role = ('You are Ubuntu linux shell helper. Given a question, '
        'answer with just a shell command, nothing else.')

chat_completion = client.chat.completions.create(
    messages=[ { "role": "system", "content": role },
              { "role": "user", "content": prompt } ],
    model = config['shai']['model']
)

print(chat_completion.usage.total_tokens, 'tokens:')
print(chat_completion.choices[0].message.content)

I decided GPT 3.5 Turbo is a good model for this, as it should be able to handle shell commands quite well. You also need to have a openai.ini file in the same directory as the script, with the following content:

[openai]
api_key = sk-xxx

[shai]
model = gpt-3.5-turbo

To use it, just install the OpenAI Python package with pip install openai, and then you can use the script like this:

$ chmod +x shai
$ ./shai find latest 3 files

And putting the script in your path, you can use it like any other shell command. Enjoy!

Read post

I was cleaning up my code folder today and came across my JUnzip library. I realized that I added zipping to the "popular" (40 stars makes it my second most starred repo at the moment) library.

It supports "deflate" method is zlib is present, and "store" if not. You can take a look at the zipping demo code to take a deeper dive.

Additional thanks to Björn Samuelsson for his compact CRC32 routines that were used in case there's no zlib.

Read post