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Mountain Living Model Kit

Save 41% Save 41%
SKU TPX00118 Barcode 5391525340739 Show more
Original price €73,34
Original price €73,34 - Original price €73,34
Original price €73,34
Current price €43,02
€43,02 - €43,02
Current price €43,02
VAT included

Explore mountain landscapes and freely build with the Arckit Mountain Living kit. Create your ultimate collection of colourful and modern alpine house style designs reflecting the challenging lifestyle in mountainous terrain.

Overview

The Arckit Mountain Living scale model building kits lets you build atop cliffs of imagination from the comfort of your home! (Scale 1:50 / 1/4)

Ideal for Budding architects from age 7 years old to create and learn about life in the mountains, from casting to camping, bunnies to bears. 

The Arckit Mountain Living building kit comes with:

  • 87 x Arckit pcs. (Architectural building blocks).
  • 22 x Arckit Mountain cardboard pop-outs (including figurines & vegetation).
    Add more cardboard pop-outs.
  • Introductory booklet with fun and educational facts about the mountainous regions, sketching & planning page and step by step instructions for 1 design. 
  • Features a pitched roof design set. 
  • A further 3+ alternative building design instructions are available online as well as lots more printable Arckitexture decals and Arckit inspiration. 
  • Arckit Digital components are also available to build via SketchUp and Revit platforms.     
  • Reusable storage box for components and accessories. 
  • Access to Arckit Universe. 
  • Package Dimensions: 300x50x220mm 
  • Recommended for age 7+ 
     

And remember that all Arckit construction sets are compatible with one another! 


Get Inspired

PROJECT HUB
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Project Tutorial by fullmakeralchemist

An intelligent device to track moves with responses during an interactive space with mapping, backlight, music and smart sculptures. This project makes use of a machine learning algorithm capable of tracking and detecting moves to identify associated gesture recognition through a microcontroller. Smart sculptures, lighting, music and video projection to trigger with each assigned gesture, creating a powerful AV experience highlighting the incredible potential of TinyML for the performing arts. This allows the corresponding media set Tiny ML in interactive to play when the right move was made because all these elements interact to create a new experience. This allows us to create Interactive installations, these sculptures use a combination of motors, sensors, and other electronics to create an immersive and interactive experience for the viewer. They may include projections, sound, and other sensory elements to create a complete experience.

read more
BLOG
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
May 22, 2023

With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where

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