SKU: 90448945263

Handcrafted Red Maple Lucet Knitting Fork

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Description

Handcrafted Red Maple Lucet Knitting ForkA Wooden Lucet for Cord Making with Yarn, Ribbon, and Thread This handcrafted red maple lucet is a wooden knitting fork for making strong decorative cords with yarn, ribbon, thread, or fiber. Inspired by Viking and medieval cord making traditions, a lucet creates a square braided cord that can be used for drawstrings, ties, handles, jewelry, trim, and small fiber arts projects. Handmade from start to finish by artisan John Ryan Polascik in Eastern

A Wooden Lucet for Cord Making with Yarn, Ribbon, and Thread

This handcrafted red maple lucet is a wooden knitting fork for making strong decorative cords with yarn, ribbon, thread, or fiber.

Inspired by Viking and medieval cord-making traditions, a lucet creates a square braided cord that can be used for drawstrings, ties, handles, jewelry, trim, and small fiber arts projects.

Handmade from start to finish by artisan John Ryan Polascik in Eastern Pennsylvania, each lucet is crafted from 100% natural red maple wood with no stains or dyes. The smooth finish helps yarn and ribbon glide easily as you work, while the 1-inch tine gap supports even tension and comfortable handling.

This full-sized lucet is well suited for adult fiber artists, homeschool handwork, historical craft study, and older children ready for traditional cord making. Use it with lace-weight yarn, worsted yarn, bulky yarn, ribbon, or other flexible fibers to create useful handmade cords for sewing, doll making, bags, garments, gifts, and decorative projects.

What Is a Lucet?

A lucet, also called a knitting fork or braiding fork, is a traditional handwork tool used to make sturdy braided cords. Yarn is looped around the two prongs and drawn over itself in a repeated motion, creating a strong cord without knitting needles or a loom.

Product Details

  • Handcrafted Red Maple Lucet
  • Made in: Eastern Pennsylvania, USA
  • Material: 100% natural red maple wood
  • Finish: No stains or dyes
  • Length: Approximately 6.5 inches
  • Tine Gap: Approximately 1 inch
  • Orifice: Approximately 3/8 inch
  • Use: Cord making with yarn, ribbon, thread, or fiber
  • Listing Includes: One handcrafted red maple wood lucet

Product Features

  • Smooth finish for easy yarn and ribbon movement
  • Works with lace-weight yarn through bulky yarn and ribbon
  • Creates strong braided cords for practical and decorative use
  • Useful for historical craft, Waldorf handwork, fiber arts, and homeschool projects

Project Ideas

Use the finished lucet cord for drawstring bags, necklace cords, bracelet ties, backpack pulls, gift wrap ties, doll clothing, garment trim, pouch handles, garlands, key ring loops, or decorative edging.

Looking for a Child-Sized Lucet?

For younger makers, pair this listing with the Waldorf Lucet Knitting Fork Kit for Children, which includes a smaller wooden lucet and plant-dyed wool yarn.

Suggested Pairing

Pair this red maple lucet with natural cotton, wool, or silk yarns for cord making, hand sewing, doll making, decorative trim, and fiber arts projects: Browse our natural yarn collection to find the perfect match for your next project!

 

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SKU: 90448945263

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4.2 ★★★★★
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Shannon
Boise, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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William P Ross
Lexington, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Dallas, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
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Amazon Customer
Waukegan, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
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mackster
Whiting, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018

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