buy military night vision goggles ARGUS PVS-31 NNVT NVT4 Gen2+ Night Vision Goggles
SKU: 91711330781
buy military night vision goggles

buy military night vision goggles ARGUS PVS-31 NNVT NVT4 Gen2+ Night Vision Goggles

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Description

buy military night vision goggles ARGUS PVS-31 NNVT NVT4 Gen2+ Night Vision GogglesOverview PVS31 is a high performance binocular night vision device. It is featured by high resolution optics, compact housing and ultra light weight. It can be installed with Gen2+ multi alkali cathode or Gen3 gallium arsenide cathode intensifier tubes. The system image resolution can be as high as 72 lp mm, and FOM as high as 3000, depending on tubes installed. Unlike similar legacy systems, the PVS31 lenses are designed with quick disassembly

Overview

PVS31 is a high performance binocular night vision device. It is featured by high resolution optics, compact housing and ultra light weight. It can be installed with Gen2+ multi-alkali cathode or Gen3 gallium arsenide cathode intensifier tubes. The system image resolution can be as high as 72 lp/mm, and FOM as high as 3000, depending on tubes installed. Unlike similar legacy systems, the PVS31 lenses are designed with quick disassembly features. This innovation allows users to field-stripe the lenses and tubes for maintenance and trouble shooting. With optimized structure and patented material, the system weight is below 450g (w/ tubes and w/o battery). With the high image quality, ultra low weight and robust structure, the PVS31 allow its users to train and operate under complex scenarios and harsh conditions. It is especially helpful in improving situation awareness and combat effectiveness of special force units.

How to Operate

1)Flip Up Power Off When the device is installed on a helmet or head mount, it will automatically turn off after it is completely flipped up over head. When the device is flipped down, it will automatically turn on. It will only turn off when it the flipping angle is greater than the predefined threshold, therefore, raising head will not turn off the device.

2)Dormancy The gravity sensor checks its posture all the time. If the device stay still for more than 100 seconds , it will automatically go dormant to save power and protect the tube from long-time burning damage until the device is waken up by new movements.

3)Manual Gain Control After correctly installing different 37mm manual gain tubes (i.e. Photonis and NNVT three-pin tubes, US MX-11769 tubes and tubes with flying wires) according to our tube installation manual, the user can rotate the power switch clockwise and anti-clockwise to manually control the intensifier’s gain features.

Specification

Shell Material: Matte black, corrosion-resistant, fibre reinforced nylon material

Weight: ≤450g

Dimensions:≤105*120*87mm（L*W*H）

Mount:Wilcox standard Dovetail

Image Intensifier Tube:Gen2+/Gen3 18mm MX10160/MX11769 tube (P20 Green/ P43 Green/P45 White)

Diopter:Replaceable diopter lenses

FOV:40°± 2°

Focus Length: 30cm to infinity

Magnification: 1.0x

Waterproof:IP67 (IP68 upgrade is available on user’s demand)

Pivot Angle of Pods:≥130°

Environmental Adaptation: Working temperature: -40℃ ~60℃; Storage temperature: -55℃ ~70℃

Battery Life: Single AA≥15 Hours, or External Battery Pack≥50 Hours (normal temperatures) Universal Strobe Battery Pack: Fischer cable, 4 AA batteries

Smart Functions: Automatic sleep and wake-up Automatic flip-up power-off and flip-down power-on Personalizing default brightness of manual gain tubes Fast cancellation of function settings

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Exchange/Return Notes
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SKU: 91711330781

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4.6 ★★★★★
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Product Reviews
N
Nader
Omaha, US
★★★★★ 1
Light on substance and heavy on flaws
Format: Paperback
The book has a great list of topics, but fails to provide much substance any of them. Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!) Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples). Seems like the book was rushed, and the author has limited hands on experience (if any). At least we know based on the amount of flaws that it was not written by an LLM
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 31, 2025
N
noam barkay
Grantham, US
★★★★★ 5
Excellent book to truly understand LLM design patterns
Format: Paperback
I just finished reviewing Ken Huang's pocket book on LLM Design Patterns, and WOW what an amazing resource! This book is excellent if you want to truly understand how to create and enhance intelligent AI language models, all that in your pocket! Ken makes the difficult things seem surprisingly easy, and that's the real MAGIC. - How to prepare your data for training by making it extremely clean. Developing the brains: the practical aspects of training, optimizing, and maintaining your models. - Learn amazing prompting techniques (such as Chain-of-Thought and Tree-of-Thoughts) to improve your AI's reasoning and problem-solving abilities. Learn everything there is to know about RAGs so that your LLM can incorporate outside expertise. - It also delves into creating "agentic" AI that is capable of action and planning (not only simple plan and execute but also enhanced techniques like ReWoo!) Really, this feels like a useful toolkit, so Ken thank you for that resource Thanks, Idan Habler
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 9, 2025
R
Ryan Meyer
Carnegie, US
★★★★★ 3
A Broad Overview, But Light on Modern Fine-Tuning
Format: Paperback
I'm currently really interested in fine-tuning LLMs and recently completed my first LoRA-based fine-tuning on a quantized model. I came to this book looking for more detail on fine-tuning. While it touches on the topic, I found the content didn’t quite align with the current state of the field in 2025. Techniques like LoRA, QLoRA, and PEFT weren’t really covered, and the material leaned more toward what I think are older or lower level approaches. That made it harder to connect with what I’m actually working on. That said, when I shifted to other chapters — like the sections on prompt engineering techniques such as Chain of Thought (CoT) and Tree of Thought (ToT) — I found more value. These sections were clearer, and I picked up a few practical insights, like using few-shot examples that walk through the CoT reasoning process. That’s not something I’ve tried before, and I can see how it might help smaller models that struggle with any type of reasoning tasks. Overall, the book feels more like a broad overview of all LLM concepts. For someone exploring many topics across the LLM ecosystem, it offers a wide-ranging introduction. But for readers like me who are actively trying to learn and apply techniques like fine-tuning and quantization, it may leave you wanting up-to-date guidance.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 10, 2025
V
Vineeth Sai
Pawtucket, US
★★★★★ 5
Great foundation read for security!
Format: Paperback
This book is a great read! It builds a strong foundation and I would highly recommend it for builders who are interetsed in building on LLMs and ensuring everything is secure. Security is super important and this book does it justice!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 27, 2025
C
Verified Purchase
CL
Grantham, US
★★★★★ 5
Loved it
Format: Paperback
I’ve easily read dozens of tech books. I liked this one a lot. Sure, there were boring parts, but most of it was engaging, especially on dry subjects. I previously read “How AI Works” and found this more informative and way more enjoyable. I got through the 700 pages in about 5 weeks while also learning about probability and linear algebra from other books and online sources. I’d love to read something more advanced by the author, maybe getting into more modern applications. I feel more comfortable with the subject and feel I am now ready to conquer more advanced texts. I initially picked this up to give me some background before reading “How to Build a LLM (from scratch)”. I’ve ordered an intermediary Deep Learning with Python book as well, but wouldn’t mind a more advanced theory book to accompany these books. I’ll definitely be rereading sections of this book to further familiarize myself with topics like backpropagation. Highly recommend if you’re looking for a gentle, but broad introduction to the topic.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 14, 2025

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