Day: February 1, 2024

Imagine this scenario - your computer is connected to a Wi-Fi network but you do not remember the password that you previously used to connect to this particular WiFi network. Maybe you forgot the password over time, or perhaps the network administrator setup the Wi-Fi connection for you without providing the actual password to you.

Now, you need to connect a second device, such as your mobile phone, to the same Wi-Fi network, but you’re unsure how to retrieve the password.

Recover the Forgotten Wi-Fi Password

In this situation, you may either request the password from the Wi-Fi owner or, a simpler alternative, open the command prompt on your computer to retrieve the saved Wi-Fi password in one easy step. This technique works on both Mac and Windows computers.

Find the WiFi Password on Windows

Open the command prompt in administrator mode. Type cmd in the Run box, right-click the command prompt icon and choose Run as Administrator). Now enter the following command and hit enter to see the WiFi password.

netsh wlan show profile name=labnol key=clear

Remember to replace labnol with the name of your Wireless SSID (this is the name of the Wi-Fi network that you connect your computer to). The password will show up under the Security Setting section (see screenshot).

If you would only like to see the password and not the other information, use the findstr command:

netsh wlan show profile name=labnol key=clear | findstr Key

If you do not see the password, probably you’ve not opened the command prompt window as administrator

Show the WiFi Password on Mac OS

If you’re using a Mac and have previously connected to a Wi-Fi network but can’t remember the password, you can easily retrieve the password from Keychain. macOS uses Keychain to store various login credentials, including Wi-Fi network passwords.

Open Spotlight (Cmd+Space) and type terminal to open the Terminal window on your Mac. Now type the following command and hit enter (replace labnol with your WiFi name).

security find-generic-password -wa labnol

You’ll be prompted to enter your admin username and password to access the OS Keychain and the Wi-FI network password would be displayed on the screen in plain text.

Reveal the WiFi Password on Linux

The process of retrieving the Wi-Fi password on Linux is similar to the method used on macOS. Open the Terminal window and type the following command to view the Wi-Fi password.

sudo cat /etc/NetworkManager/system-connections/labnol | grep psk=

Remember to substitute labnol with the wireless name (SSID) of your current Wi-Fi network. The value of the field psk is your WiFi password.

If you don’t know the exact name of the Wi-Fi network, you can use the following command to list all the stored network configurations and their corresponding passwords:

sudo grep psk= /etc/NetworkManager/system-connections/*

Start WLAN AutoConfig (Wlansvc Service)

If you are using this technique to retrieve the WiFi password on a Windows computer but getting an error that says - “The Wireless AutoConfig Service (wlansvc) is not running” - here’s a simple fix:

Click the Windows Start button and type services.msc in the Run box to access Windows Services. Here go to the WLAN Autoconfig service and make sure that the status is Running. Else right-click the WLAN AutoConfig service, select Properties and go to Dependencies. Check all the dependencies to make sure they are all running.

Looking at the two tables below, which format do you find more intuitive and easier to read?

For years, people have been using spreadsheet software to create cross-tabulated (or contingency, multi-dimensional) reports or fill forms. These reports neatly organize categories, dates, and other data points into levels of rows and columns, making them easy to read and analyze. 

Columnar storage is a commonly used storage technique. Often, it implies high performance and has basically become a standard configuration for today’s analytical databases.

The basic principle of columnar storage is reducing the amount of data retrieved from the hard disk.  A data table can have a lot of columns, but the computation may use only a very small number of them. With columnar storage, useless columns do not need to be retrieved, while with row-wise storage, all columns need to be scanned. When the retrieved columns only take up a very small part of the total, columnar storage has a big advantage in terms of IO time, and computation seems to get much faster.

Last year, I wrote a post on OpenTelemetry Tracing to understand more about the subject. I also created a demo around it, which featured the following components:

• The Apache APISIX API Gateway
• A Kotlin/Spring Boot service
• A Python/Flask service
• And a Rust/Axum service

I've recently improved the demo to deepen my understanding and want to share my learning.

The history of DevOps is definitely worth reading in a few good books about it. On that topic, “The Phoenix Project,” self-characterized as “a novel of IT and DevOps,”  is often mentioned as a must-read. Yet for practitioners like myself a more hands-on one is “The DevOps Handbook” (which shares Kim as author in addition to Debois, Willis, and Humble) that recounts some of the watershed moments around the evolution of software engineering and provides good references around implementation. This book actually describes how to replicate the transformation explained in the Phoenix Project and provides case studies.

In this brief article, I will use my notes on this great book to regurgitate a concise history of DevOps, add my personal experience and opinion, and establish a link to Cloud Development Environments (CDEs), i.e., the practice of providing access to and running, development environments online as a service for developers.

It is often said that software developers should create simple solutions to the problems that they are presented with. However, coming up with a simple solution is not always easy, as it requires time, experience, and a good approach. And to make matters worse, a simple solution in many ways will not impress your co-workers or give your resume a boost.

Ironically, the quest for simplicity in software development is often a complex journey. A developer must navigate through a labyrinth of technical constraints, user requirements, and evolving technological landscapes. The catch-22 is palpable: while a simple solution is desirable, it is not easily attained nor universally appreciated. In the competitiveness of software development, where complexity often disguises itself as sophistication, simple solutions may not always resonate with the awe and admiration they deserve. They may go unnoticed in a culture that frequently equates complexity with competence.

This article explores the implementation of predictive maintenance models in industrial IoT settings and their significant impact on ensuring equipment reliability.

The Need for Predictive Maintenance in Industrial IoT

Traditionally, industrial facilities have relied on reactive maintenance strategies, addressing equipment failures after they occur. This approach often leads to unplanned downtime, increased maintenance costs, and potential safety risks. With the integration of IoT sensors and AI capabilities, predictive maintenance shifts the paradigm towards a proactive and data-driven strategy. While sized at 4.5 billion U.S. dollars in 2020, the market is projected to reach a size of 64.3 billion U.S. dollars by 2030.

Dynatrace welcomed thousands of in-person and virtual attendees to its annual Perform conference in Las Vegas this week. The overarching theme was “Make Waves,” – conveying both the tectonic shifts happening across industries and opportunities for organizations to drive transformational impact.

True to the cutting-edge nature of the company, Dynatrace had several major announcements that will allow enterprises to tackle some of today’s most pressing challenges around cloud complexity, AI adoption, security threats, and sustainability commitments. Let’s dive into the key developments.

In the ever-evolving landscape of software development, GitHub Copilot stands as a beacon of innovation, revolutionizing the coding experience. As developers navigate this AI-powered coding assistant, a comprehensive set of metrics has emerged to gauge productivity and efficiency. Let's delve into the top 25 key metrics, uncovering their rationale, formulas, and real-time applications.

1. Total Lines of Code Written (TLOC)

Rationale: Measures the aggregate lines of code, encompassing both manual and Copilot-generated contributions.

Get ready for a special journey with "Code Wisdom 2024," a guide for developers who want to shine in the world of software development.

Enlightenment in development is realising that every bug is a lesson, every error is an opportunity, and every line of code is a chance to create something meaningful.