SKU: 9086919661

(Drivers Side)- Molle Panel Side Storage Unit to suit BYD Shark 6

$134.55 $149.50
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Ships within 48 hours · Estimated delivery Aug 18 - Aug 23

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Description

(Drivers Side)- Molle Panel Side Storage Unit to suit BYD Shark 6KINETIQ 4X4 MOLLE Panel Side Storage System Driver Side FREE FREIGHT ON ALL MOLLE PANEL ORDERS AUSTRALIA WIDE! SHIPPING FROM OUR SYDNEY WAREHOUSE DAILY Installation partners now available contact us to arrange fitment for you! Australia NSW ACT VIC QLD New Zealand Auckland Suits BYD Shark 6 CONFIRMED FITMENT WITH KINETIQ ELECTRIC & MANUAL ROLLER SHUTTER CONFIRMED FITMENT WITH HAMER ROLLER SHUTTER CONFIRMED FITMENT WITH TRADESMAN ROLLER SHUTTER

🧰 KINETIQ 4X4 MOLLE Panel Side Storage System – Driver Side

FREE FREIGHT ON ALL MOLLE PANEL ORDERS AUSTRALIA WIDE!

SHIPPING FROM OUR SYDNEY WAREHOUSE DAILY

Installation partners now available - contact us to arrange fitment for you! 

Australia NSW - ACT- VIC - QLD 

New Zealand - Auckland 

 

Suits BYD Shark 6

✅CONFIRMED FITMENT WITH KINETIQ ELECTRIC & MANUAL ROLLER SHUTTER 

✅CONFIRMED FITMENT WITH HAMER ROLLER SHUTTER

✅CONFIRMED FITMENT WITH TRADESMAN ROLLER SHUTTER

✅CONFIRMED FITMENT WITH OCAM ROLLER SHUTTER

✅CONFIRMED FITMENT WITH TIGER X ROLLER SHUTTER

✅ CONFIRMED FITMENT WITH BYD/IRONMAN SHUTTER

✅ CONFIRMED FITMENT WITH HSP SHUTTERS WITH REAR BRACKET MODIFICATION 

✅ CONFIRMED FITMENT WITH YE HUA TAILGATE SEAL KIT 



Product Installation Video: 
https://www.youtube.com/watch?v=p1E-qpeGJyM&t=125s

Turn wasted space into smart, secure storage with the KINETIQ 4X4 MOLLE Panel Side Storage System, purpose-built in Australia for the driver side of your BYD Shark 6. Whether you’re on the job site, camping off-grid, or just want your tub organized — this heavy-duty MOLLE panel has you covered.

💡 Also available for the Passenger Side.
Purchase both sides together in our Combo Deal and receive an exclusive discount!

⚙️ Key Specifications

  • 🇦🇺 Australian Designed – specifically for the BYD Shark 6

  • 🛠️ Material: Premium steel construction

  • 🎨 Finish: Durable black powder-coated surface

  • 📏 Thickness: 4 mm steel for exceptional strength

  • 🔩 Mounting: Uses factory OEM holes – no drilling required

  • 🧰 Compatibility: Fits with both roller shutters and bare tubs

  • 🧰 Retains Access to factory power outlets
  • 🧰Retains Access to all factory tie down points

  • ⏱️ Installation: Easy DIY setup, typically 10 minutes

  • 📦 Includes: All necessary mounting hardware 

🌟 Features & Benefits

✅ Perfect Driver-Side Fitment
Designed specifically for the driver side of the BYD Shark 6 tub for an exact, flush fit.

✅ Access to Factory Power Points Retained
Unlike other panels, this design maintains full access to the factory BYD power outlets, allowing you to conveniently power tools, fridges, or accessories while still using the MOLLE system.

✅ Strong & Durable Construction
Made from 4 mm thick steel, finished in a black powder coat for corrosion resistance and long-term strength.

✅ No-Drill Installation
Mounts securely using OEM tie-down and mounting points — no cutting or drilling required.

✅ Full Tie-Down Functionality
Still allows use of factory tie-down points, so you can strap down larger cargo with ease.

✅ Smart Storage Solution
Keep your tub neat and organized — ideal for:

  • First-aid kits

  • KINETIQ retractable ratchet straps

  • Recovery gear & tools

  • Camping & sports equipment

  • General loose cargo

✅ Accessory Ready
Easily mount accessories like fire extinguishers or shovels using aftermarket brackets (self modification and mounting required).

🏕️ Perfect For

  • Tradespeople, campers & weekend adventurers

  • Anyone looking to maximize storage without sacrificing utility

  • Ute owners who want both function and factory-style integration

🇦🇺 Australia-Wide Shipping

📦 Ships flat-packed for easy transport
💬 Contact KINETIQ 4X4 for a custom freight quote to your postcode

🔧 Professional Installation Available

Fitting service available with our trusted partner:
📍 Prestige Tyre & Auto – 44 Buckley St, Marrickville NSW 2204
💰 Installation Fee: $49

**PLEASE NOTE: If you have a tub liner installed in your vehicle you will be required to trim approximately 6-7cm off the TUB LINER to allow clearance for the bottom of the molly panel to sit in place against the bottom of your tub OR alternatively, squeeze the tub liner under the panel 

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 9086919661

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4.5 ★★★★★
Based on 7 reviews
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Product Reviews
P
Verified Purchase
Par
Lake Worth, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Dallas, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Lowell, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Fort Morgan, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Lowell, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

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