arianvp

Native Secure Enclave backed ssh keys on MacOS

It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive

There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!

Chillee

import torch
from torch.nn.attention.flex_attention import create_block_mask, flex_attention
torch.set_default_device('cuda')

q, k, v = [torch.randn(8, 8, 1024, 64, requires_grad=True) for _ in range(3)]

causal_mask = create_block_mask(lambda b, h, q_idx, kv_idx: q_idx >= kv_idx, None, None, 1024, 1024)
uncausal_mask = create_block_mask(lambda b, h, q_idx, kv_idx: q_idx < kv_idx, None, None, 1024, 1024)
ref_out = flex_attention(q, k, v)
causal_out, causal_lse = flex_attention(q, k, v, block_mask=causal_mask, return_lse=True)

cloneofsimo

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import numpy as np
import math


def compute_activation_std(model, dataset, device='cpu', batch_size=32, num_workers=0, layer_names=None):
    activations = {}

AndreVallestero

# https://youtu.be/DNKAvDeqH_Y
# https://google.github.io/mediapipe/solutions/iris.html#ml-pipeline
# https://google.github.io/mediapipe/solutions/face_mesh.html#python-solution-api

import cv2 as cv
import numpy as np
from mediapipe import solutions

LEFT_IRIS = [pair[0] for pair in solutions.face_mesh.FACEMESH_LEFT_IRIS]
RIGHT_IRIS = [pair[0] for pair in solutions.face_mesh.FACEMESH_RIGHT_IRIS]

deepanshululla

#include <exception>
#include <memory>
#include <mutex>
#include <queue>

struct EmptyQueueException : std::exception {
    const char *  what() const throw() {
        return "Empty queue";
    }
};

silvesthu

GPU Optimization for GameDev

Graphics Pipeline / GPU Architecture Overview

• 2011 - A trip through the Graphics Pipeline 2011
• 2013 - Performance Optimization Guidelines and the GPU Architecture behind them
• 2015 - Life of a triangle - NVIDIA's logical pipeline
• 2015 - Render Hell 2.0
• 2016 - How bad are small triangles on GPU and why?
• 2017 - GPU Performance for Game Artists
• 2019 - Understanding the anatomy of GPUs using Pokémon

xezpeleta

Ubiquiti EdgeRouter X: custom dynamic DNS

ssh ubnt@<your-ip>
configure

Obtain your public IP address behind a NAT: using ipinfo.io

stormraiser

Danbooru Faces v0.1

Discription

This dataset contains ~443k anime face images of size 256x256 drawn by ~7,000 artists, obtained from Danbooru

Collection

We first downloaded JSON files of all existing posts numbered from 1 to 2,800,000 using their API. We filtered the posts by the following criteria:

spro

import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, n_layers=1):
        super(RNN, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

ruoyu0088