sudo useradd -m -s /bin/bash alice
sudo passwd alice
Add user to wheel (sudo) group
(function (document) { | |
var checkCount = 0, | |
formatFound = false; | |
function setHTMLClass(height, className) { | |
checkCount++; | |
if (height == 2) { | |
formatFound = true; | |
document.documentElement.className += " " + className; | |
} else { |
(function (document) { | |
var checkCount = 0, | |
formatFound = false; | |
function setHTMLClass(height, className) { | |
checkCount++; | |
if (height == 2) { | |
formatFound = true; | |
document.documentElement.className += " " + className; | |
} else { |
#!/usr/bin/env python | |
# encoding: utf-8 | |
""" | |
linkedin-query.py | |
Created by Thomas Cabrol on 2012-12-03. | |
Customised by Rik Van Bruggen | |
Copyright (c) 2012 dataiku. All rights reserved. | |
Building the LinkedIn Graph |
If you don't want to deal with styling a mostly text-based HTML document, plop these lines in and it'll look good:
html {
font-family: 'Helvetica Neue', 'Helvetica', 'Arial', sans-serif;
font-size: 1.3em;
max-width: 40rem;
padding: 2rem;
margin: auto;
line-height: 1.5rem;
This page provides a full overview of PHP's SessionHandler
life-cycle - this was generated by a set of test-scripts, in order to provide an exact overview of when and
what you can expect will be called in your custom SessionHandler
implementation.
Each example is a separate script being run by a client with cookies enabled.
To the left, you can see the function being called in your script, and to the right, you can see the resulting calls being made to a custom session-handler registed using session_set_save_handler().
// Dump all NSApplication’s class methods | |
let dump = NSApplication.perform(NSSelectorFromString("fp_methodDescription")).takeUnretainedValue() as? String | |
// Dump all NSApplication’s instance methods | |
let dump = NSApp.perform(NSSelectorFromString("fp_methodDescription")).takeUnretainedValue() as? String | |
// or | |
print(NSApplication.value(forKey: "fp_methodDescription")) | |
print(NSApp.value(forKey: "fp_methodDescription")) |
Not super comprehensive (yet), but I think having up to date documentation like this should be quite helpful for those out of the loop. Things change all the time in local AI circles, and it can be dizzying to catch up from an outsider's perspective, especially if you are new to the more technical aspects of language models in general (and not just locally hosted LLMs).
Everytime a large language model makes predictions, all of the thousands of tokens in the vocabulary are assigned some degree of probability, from almost 0%, to almost 100%. There are different ways you can decide to choose from those predictions. This process is known as "sampling", and there are various strategies you can use which I will cover here.
#Overview
The ESP8266 is a versatile chipset that provides client and access point wifi capabilities, on-chip SRAM and flash storage, a RISC processor, GPIO pins, and pin outs for memory and CPU extensability. Bundled as a series of modules with varying features focused around acting as either a wifi extension module to an existing microcontroller or as an self-contained solution for integrating wifi and internet functionality with GPIO control. From an application standpoint, the ESP weighs in as an inexpensive and compact alternative to AVR (arduino) based, wifi-driven IoT solutions.
AT+RST restart the module, received some strange data, and "ready"