Andrew Hundt ahundt

Get the old VSCode icon back!! 🔥 🔥

First download the new old icon: https://cl.ly/mzTc (based on this)

You can also use the icon you want, but you need to convert it to .icns. You can use this service to convert PNG to ICNS.

Go to Applications and find VSCode, right click there and choose Get Info. Drag 'n drop the new icon.

CUDA7.5

Now, CUDA did not compatible Apple clang 7.0.0 also Xcode7.0.

e.g,

> cd /Developer/NVIDIA/CUDA-7.5/samples/0_Simple/asyncAPI

> clang -v
Apple LLVM version 7.0.0 (clang-700.1.75)

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

	# Project Policy

	This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

	# 1. Introduction

	> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

	## 1.1 Actors

	# prior - likelihood conflict

	library(rethinking)

	yobs <- 0

	mtt <- ulam(
	alist(
	y ~ dstudent(2,mu,1),
	mu ~ dstudent(2,10,1)

	class weighted_categorical_crossentropy:
	def __init__(self, weights):
	self.weights = weights
	self.__name__ = 'wcentroid_loss'

	def __call__(self, y_true, y_pred):
	class0 = K.ones_like(y_pred)[:, :, :, 0] * self.weights[0]
	class1 = K.ones_like(y_pred)[:, :, :, 0] * self.weights[1]
	x = K.tf.where(y_true[:, :, :, 0] > 0, class0, class1)
	result = x * K.categorical_crossentropy(y_pred, y_true)

	import datetime as dt

	import tensorflow as tf
	import tensorflow.contrib.slim as slim
	from tensorflow.contrib.slim.nets import resnet_v1
	import threading

	from PoseDataset import PoseDataset
	from TrainParams import TrainParams

	import tensorflow as tf
	from tensorflow.python.framework import ops
	import numpy as np

	# Define custom py_func which takes also a grad op as argument:
	def py_func(func, inp, Tout, stateful=True, name=None, grad=None):

	# Need to generate a unique name to avoid duplicates:
	rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))

	#!/bin/bash

	CUDA_LIB_DIR=/usr/local/cuda/lib
	CUDA_VERSION=7.5
	CUDA_LIBS="cublas cudart curand"

	CUDNN_LIB_DIR=/usr/local/cuda/cudnn-3/lib
	CUDNN_VERSION=7.0
	CUDNN_LIBS="cudnn"