VictorTaelin

Optlam.js - 3.4 million IPS

// ~~~~~~~~~~~~~~~~~~~~~~ Optlam.js ~~~~~~~~~~~~~~~~~~~~~~
// An optimal λ-calculus normalizer written in JavaScript.
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// Optlam.js is a simple, optimal (in Levy's sense) λ-calculus evaluator using
// interaction nets. It is, currently, as far as I know, the fastest
// implementation of functions in the world. It uses Lamping's Abstract
// Algorithm - that is, the so called (and problematic) "oracle" is avoided

VictorTaelin

async/await is just the do-notation of the Promise monad

CertSimple just wrote a blog post arguing ES2017's async/await was the best thing to happen with JavaScript. I wholeheartedly agree.

In short, one of the (few?) good things about JavaScript used to be how well it handled asynchronous requests. This was mostly thanks to its Scheme-inherited implementation of functions and closures. That, though, was also one of its worst faults, because it led to the "callback hell", an seemingly unavoidable pattern that made highly asynchronous JS code almost unreadable. Many solutions attempted to solve that, but most failed. Promises almost did it, but failed too. Finally, async/await is here and, combined with Promises, it solves the problem for good. On this post, I'll explain why that is the case and trace a link between promises, async/await, the do-notation and monads.

First, let's illustrate the 3 styles by implementing

VictorTaelin

#!/bin/bash

# Find all hvm processes and extract their PIDs
pids=$(ps aux | grep hvm | grep -v grep | awk '{print $2}')

# Check if any hvm processes were found
if [ -z "$pids" ]; then
  echo "No hvm processes found."
else
  # Forcefully kill each hvm process

VictorTaelin

CHALLENGE

Develop an AI prompt that solves random 12-token instances of the A::B problem (defined here), with 90%+ success rate.

RULES

1. The AI will be given a <problem/> to solve.

We'll use your prompt as the SYSTEM PROMPT, and a specific instance of problem as the PROMPT, inside XML tags. Example:

VictorTaelin

Solving SAT via interaction net superpositions

I've recently been amazed, if not mind-blown, by how a very simple, "one-line" SAT solver on Interaction Nets can outperform brute-force by orders of magnitude by exploiting "superposed booleans" and optimal evaluation of λ-expressions. In this brief note, I'll provide some background for you to understand how this works, and then I'll present a simple code you can run in your own computer to observe and replicate this effect. Note this is a new observation, so I know little about how this algorithm behaves asymptotically, but I find it quite

VictorTaelin

Implementing complex numbers and FFT with just datatypes (no floats)

In this article, I'll explain why implementing numbers with just algebraic datatypes is desirable. I'll then talk about common implementations of FFT (Fast Fourier Transform) and why they hide inherent inefficiencies. I'll then show how to implement integers and complex numbers with just algebraic datatypes, in a way that is extremely simple and elegant. I'll conclude by deriving a pure functional implementation of complex FFT with just datatypes, no floats.

VictorTaelin

#include <stdarg.h>
#include <stdbool.h>
#include <stdint.h>
#include <stdio.h>
#include <string.h>
#include <unistd.h>

typedef uint8_t u8;
typedef uint16_t u16;
typedef uint32_t u32;

VictorTaelin

Notes on this tweet.

• The screenshots were taken on different sessions.

• The entire sessions are included on the screenshots.

• I lost the original prompts, so I had to reconstruct them, and still managed to reproduce.

• The "compressed" version is actually longer! Emojis and abbreviations use more tokens than common words.

VictorTaelin

// Fast block-local prefix-sum on CUDA, using warp-syncs.
// The input is an array of u32. It is mutated in place. Example:
// arr = [1,1,1,1,...]
// Becomes:
// arr = [1,2,3,4,...]
// The number of elements must be equal to threads per block (TPB).

#include <stdio.h>
#include <cuda_runtime.h>

VictorTaelin

A::B is a system with 4 tokens: `A#`, `#A`, `B#` and `#B`.

An A::B program is a sequence of tokens. Example:

    B# A# #B #A B#

To *compute* a program, we must rewrite neighbor tokens, using the rules:

    A# #A ... becomes ... nothing
    A# #B ... becomes ... #B A#