Each element in tensioned structural networks -- such as tensegrity, architectural fabrics, or medical braces/meshes -- requires a specific tension level to achieve and maintain the desired shape, stability, and compliance. These structures are challenging to manufacture, 3D print, or assemble because flattening the network during fabrication introduces multiplicative inaccuracies in the network's final tension gradients. This study overcomes this challenge by offering a fabrication algorithm for direct 3D printing of such networks with programmed tension gradients, an approach analogous to the spinning of spiderwebs. The algorithm: (i) defines the desired network and prescribes its tension gradients using the force density method; (ii) converts the network into an unstretched counterpart by numerically optimizing vertex locations toward target element lengths and converting straight elements into arcs to resolve any remaining error; and (iii) decomposes the network into printable toolpaths; Optional additional steps are: (iv) flattening curved 2D networks or 3D networks to ensure 3D printing compatibility; and (v) automatically resolving any unwanted crossings introduced by the flattening process. The proposed method is experimentally validated using 2D unit cells of viscoelastic filaments, where accurate tension gradients are achieved with an average element strain error of less than 1.0\%. The method remains effective for networks with element minimum length and maximum stress of 5.8 mm and 7.3 MPa, respectively. The method is used to demonstrate the fabrication of three complex cases: a flat spiderweb, a curved mesh, and a tensegrity system. The programmable tension gradient algorithm can be utilized to produce compact, integrated cable networks, enabling novel applications such as moment-exerting structures in medical braces and splints.

Electricity is the energy that drives modern society but trying to figure out where cables are located continues to provide engineers with major challenges when considering that these cables were installed decades ago. Now, Google is using AI to automatically generate maps of where all cables are so that future engineers know precisely how to make upgrades and repairs. Why are cable networks providing engineers with significant challenges, what will Google's new project do, and should this idea of digitised cables be brought into homes? If there is one fact of life that I am truly grateful is that electricity comes out of my sockets whenever I require it. Just like the Armstrong and Miller Time Traveller sketch, the number of people in society who are entirely oblivious to the effort and resources needed to correctly operate a nationwide grid is astonishing (electricity is that thing that comes out of the wall).

When MGA Entertainment, the world's largest private toy company, premiered its newest kids' show about a teen-girl team of super spies, it skipped Saturday morning TV and staged a splashy premiere on Netflix -- replete with a matching toy line, a few dozen dolls and play sets like the "lip balm lab activity kit." And Netflix, the world's largest streaming service, was more than happy to fold the show, "Project Mc2," into its exploding empire of kids' entertainment. Much of the 5 billion Netflix is spending this year on movies and TV shows will be spent on fare for the playground set. The big-business battle for kids' distracted attention spans has never been more competitive -- or eye-poppingly lucrative -- and Netflix has aggressively angled to use its data-driven insights to make programs kids don't want to turn off. About half of Netflix's 75 million members regularly watch kids' movies or TV shows, executives say, but the potential for long-term profits runs much deeper. If the site is able to win over viewers when they're young, executives said, they may be able to secure their loyalty for life.