3D Printing’s Even More Affordable Now Thanks to SparkMaker
Currently being dubbed as the world’s most affordable desktop SLA 3D printer, SparkMaker has certainly caused a stir on Kickstarter already. After just a few days of being on the investment seeking platform, the company succeeds at reaching its modest $20,000 goals with pledges…
Currently being dubbed as the world’s most affordable desktop SLA 3D printer, SparkMaker has certainly caused a stir on Kickstarter already. After just a few days of being on the investment seeking platform, the company succeeds at reaching its modest $20,000 goals with pledges now topping the $370,000 mark in total with a staggering 25 days left to run. So far more than 1,600 backers have jumped on board with this printer, so there must be something good in it.
SparkMaker is an LCD based SLA 3D printer that’s affordable yet effective. It prints in high-resolution and can give most other desktop 3D printers a run for their money. Not only is SparkMaker aesthetically pleasing, it’s compact too, neatly fitting into any studio, office, or workshop. But don’t let the size put you off. This little printer packs a punch and has a uniform maximum build area of 4 x 2.2 inches (10.2 x 5.6 cm) and a maximum build height of 5 inches (12.5 cm). And just because it’s small doesn’t mean the quality is compromised in the slightest. In fact, the SparkMaker prints at a higher resolution than DLP 3D printers.
This printer is ideal for scale photocopying and general 3D printing and is so easy to use you just won’t believe your eyes. It also supports offline printing, unlike many other SLA 3D printers. Simply pop in an SD card that’s got your required design loaded on, hit the print button, and off it goes. In no time at all, you’ll see your very own creation come to life in front of you. So, are you ready to get yours?
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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