SKU: 78561498098

1 Piece Clear Hard Shell Protective Case for Nintendo 3DS XL, NDSL, PSP, GBA SP | Multi-Console Crystal Cover

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Description

1 Piece Clear Hard Shell Protective Case for Nintendo 3DS XL, NDSL, PSP, GBA SP | Multi-Console Crystal CoverThis clear hard shell case keeps your handheld console safe from scratches, dust, and everyday bumps while keeping its original look visible. It fits a wide range of classic gaming devices including the Nintendo 3DS XL, New 3DS LL, NDSL, DSi, DSi XL, GBA SP, PSP 1000, PSP 2000, PSP 3000, PSP Go, PSV 1000, and PSV 2000. The transparent crystal design lets your console's color and style show through without adding bulk. Made from durable plastic, this

This clear hard shell case keeps your handheld console safe from scratches, dust, and everyday bumps while keeping its original look visible. It fits a wide range of classic gaming devices including the Nintendo 3DS XL, New 3DS LL, NDSL, DSi, DSi XL, GBA SP, PSP 1000, PSP 2000, PSP 3000, PSP Go, PSV 1000, and PSV 2000. The transparent crystal design lets your console's color and style show through without adding bulk.

Made from durable plastic, this protective shell snaps onto your device quickly and stays secure during play or travel. It covers the back and sides of your console, shielding it from scuffs and light drops while keeping all buttons, ports, and screens fully accessible. You can play, charge, and insert game cartridges without removing the case.

This case is a practical choice for anyone who wants to preserve their handheld console's condition without hiding its design. It adds a slim layer of protection that fits easily into bags or pockets. Whether you are storing your device or taking it on the go, this shell helps keep it looking clean and new.

Key Features and Benefits

- Transparent crystal hard shell that protects your console from scratches and dust while keeping its original color and design visible

  • Snap-on installation that attaches quickly and securely without tools or adhesive
  • Precise cutouts that give full access to all buttons, ports, cameras, and screens without needing to remove the case
  • Lightweight and slim design that adds minimal bulk so your console stays easy to hold and carry
  • Durable plastic material that absorbs light impacts and helps prevent damage from everyday bumps and drops

Who Is This For

- Gamers who own multiple handheld consoles and want one protective case that fits different models

  • Collectors who want to keep their classic devices in good condition without hiding the original look
  • Travelers who need a lightweight protective shell for gaming on the go
  • Parents looking for an affordable way to protect their children's handheld consoles from scratches and wear

Usage Scenario

Mark pulled out his old Nintendo 3DS XL during a long train ride and noticed the back was already scratched from years of use. He slipped this clear hard shell onto the console in seconds. The snug fit protected the scratched area and kept the rest of the device safe in his bag. He played through his commute without the case slipping off or blocking any buttons. When he arrived, the console still looked clean and the case showed no signs of wear.

Micro Comparison

Unlike silicone skins that collect dust and feel sticky over time, this hard plastic shell stays smooth and easy to clean. It also offers better impact protection than a thin vinyl wrap. The transparent design gives you the look of a bare console while adding a protective layer that many soft cases cannot provide.

Specifications

Material: Hard plastic (crystal clear)

Compatibility: Nintendo 3DS XL, New 3DS LL, New 3DS XL, NDSL, DSi, DSi XL, GBA SP, PSP 1000, PSP 2000, PSP 3000, PSP Go, PSV 1000, PSV 2000

Color: Transparent

Type: Snap-on hard shell case

Package Includes

- 1 x Clear hard shell protective case

Why You Will Love It

You can protect your favorite handheld consoles without hiding their original design. This case snaps on quickly and stays secure during long gaming sessions. It gives you peace of mind that your device is safe from scratches and minor drops while remaining fully functional.

Questions and Answers

Question: Will this case fit my Nintendo New 3DS XL?

Answer: Yes, this case is compatible with the New 3DS XL and New 3DS LL models.

Question: Can I still use the charging port with the case on?

Answer: Yes, the case has precise cutouts that allow access to all ports, including the charging port.

Question: Does this case add a lot of extra weight to my console?

Answer: No, the case is made from lightweight plastic and adds very little weight or bulk to your device.

Question: Is the case easy to remove once it is on?

Answer: Yes, you can snap it off gently when you need to remove it. It stays secure during use but is not permanent.

Question: Will this case protect my PSP 1000 from scratches in my bag?

Answer: Yes, the hard shell covers the back and sides of the PSP 1000 and helps prevent scratches from keys or other items in your bag.

Shipping Notes
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SKU: 78561498098

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4.2 ★★★★★
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Product Reviews
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Verified Purchase
Par
Los Angeles, 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
Fort Morgan, 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
Lake Worth, 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
Omaha, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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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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