Kyle Kingsbury’s Post

Hello LinkedIn!! PSA:🚨Rachele Simms, gem barton and myself (a total dream team if I do say so…) are pitching a session at SXSW London next year, bringing together a cross-functional group to collaboratively explore how we should rethink the questions we are asking of #architecture and the built environment to prepare more effectively for the future 🌇 We’ll be looking at the interdependencies between objective changes such as New Technologies, the Climate Crisis and Societal Shifts, and subjective evolutions in How, Where and Why humans, Live, Work and Play – provoking on their long-term impact on the physical spaces we inhabit. If that sounds like something you’d like to join in, or you just think needs to happen please vote for us using the link below on Rachele’s post… FYI: you’ll need to sign up and sign in first! To find us, once you’re in, the title is: Building Tomorrow: 2050 - Nine questions to reshape how we think about the future built environment ID Code is: 3189 2050

Run Daft Distributed in 60 seconds! In this first part, you’ll see in 30 seconds how to install daft-launcher, configure a cluster and also spin up a cluster. Watch the full video: https://lnkd.in/d_NTftJY Try it yourself: https://lnkd.in/g4tbpWDB

📢 New Blog Post Alert! 📢 I’m excited to share my latest blog post: “Big O Notation: The Secret Sauce of Algorithms”. If you’ve ever wondered how computer scientists measure the efficiency of algorithms, this post is for you! In this blog, I dive into: * What Big O Notation is and why it’s important * The basics of time complexity and space complexity * The ultimate showdown between time complexity and space complexity * Real-world examples to help you understand these concepts better No matter your skill level, grasping Big O Notation is essential for optimizing your code and making well-informed decisions. Dive into the blog to discover why it’s important and how to master it. Happy reading! 📖

#day62 of #365daysofcodechallenge Solved simple DSA problem today and React Revising going on.... Problem: Length of longest consecutive ones Below Image describes : Problem Description, Problem Constraints, solution Time Complexity: O(n2) Space Complexity: O(1)

Part 1 of blog post about my FAIR library out on medium. This one dives deep on the core algorithm and optimizations. I want to do at least one more on the practical usage tips in the future. https://lnkd.in/g5UjA-zv

#day65 of #365daysofcodechallenge Solved simple DSA problem today and React Revising going on.... Problem: Multiple left rotations of the array Below Image describes : Problem Description, Problem Constraints, solution Time Complexity: O(n2) Space Complexity: O(n)

When diving into code, especially in performance-sensitive applications, understanding how memory is managed can be a game-changer! 🧠✨ Two crucial concepts to grasp are stack and heap memory allocation. Think of the stack as your fast, organized workspace for handling short-lived, temporary data. 📚🚀 On the flip side, the heap is your flexible storage area for dynamic, long-term data—perfect for objects that need to stick around. 🏗️💾 In this post, we’ll explore how stack and heap memory work, their key differences, and why choosing the right type for your data can make a big difference in your code’s performance and efficiency. Let's decode the mysteries of memory management together! 🔍💡

Awesome example of how reproducible dev environments with Jetify Devbox speeds up developer onboarding: "Linus demonstrated how he used Devbox from Jetify to significantly reduce the onboarding time for his students – from days to just a few hours – by setting up his computing environment in a re-runnable way."

How to score a tech role in five steps https://buff.ly/3yiRGoU

Check out my blog post https://wix.to/c3G7pw2 #newblogpost