yberreby

import functools, time, jax, jax.numpy as jnp

jax.config.update("jax_default_matmul_precision", "tensorfloat32")
SQRT2_OVER_PI = 0.7978845608028654

# ----------------------------------------------------------------------
def gelu_fast(x):
    u = SQRT2_OVER_PI * (x + 0.044715 * x * x * x)
    return 0.5 * x * (1. + jnp.tanh(u))

yberreby

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.12"
# dependencies = [
#   "jax==0.5.0",
#   "jaxopt>=0.8.5",
#   "optax>=0.2.4",
#   "matplotlib>=3.10.1",
#   "pyqt6>=6.9.0", # for matplotlib gui
# ]

yberreby

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.12"
# dependencies = [
#   "jax==0.5.0",
#   "matplotlib>=3.10.1",
#   "pyqt6>=6.9.0", # for matplotlib gui
# ]
# ///

yberreby

#!/usr/bin/env -S uv run --script --quiet
# /// script
# requires-python = ">=3.10"
# dependencies = [
#   "pyqt6",         # For matplotlib backend
#   "numpy",
#   "matplotlib",
#   "jax[cuda12]==0.5.2", # Change for CPU.
# ]
# ///

Const-me

#include <stdint.h>
#include <immintrin.h>
#include <intrin.h>
#include <stdio.h>

// Count of set bits in `plus` minus count of set bits in `minus`
// The result is in [ -32 .. +32 ] interval
inline int popCntDiff( uint32_t plus, uint32_t minus )
{
	plus = __popcnt( plus );

nateraw

"""
Built on top of this gist by @karpathy:
https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355

stable diffusion dreaming over text prompts
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:
$ python stable_diffusion_walk.py --prompts "['blueberry spaghetti', 'strawberry spaghetti']" --seeds 243,523 --name berry_good_spaghetti

codelynx

//
//	Swift UnsafePointer converter
//	Kaz Yoshikawa
//
//	These code demonstrates how to convert swift pointers to the other forms of pointers.
//
//	Source:
//	Swift の Array やら ArraySlice やらポインタの変換まとめ
//	https://qiita.com/Satachito/items/4c39c9b06304e4d86660
//

ChrisRackauckas

SciPy+Numba odeint vs Julia DifferentialEquations.jl vs NumbaLSODA Summary

All are solved at reltol=1e-3, abstol=1e-6 using the fastest ODE solver of the respective package for the given problem.

Absolute Performance Numbers:

• SciPy LSODA through odeint takes ~489μs
• SciPy LSODA through odeint with Numba takes ~257μs
• NumbaLSODA takes ~25μs
• DifferentialEquations.jl Rosenbrock23 takes ~9.2μs

chicobentojr

A Recipe for Training Neural Networks - Andrej Karpathy

DevonMorris

Add the following to a script in .config/i3/i3_shell.sh

#!/usr/bin/env bash
WHEREAMI=$(cat /tmp/whereami)
i3-sensible-terminal -cd $WHEREAMI

To call store the environment variable WHEREAMI put