SKU: 89377687561

Formatt Hitech 77mm Firecrest Graduated ND 1.5 Filter

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Description

Formatt Hitech 77mm Firecrest Graduated ND 1.5 Filter5 Stop Graduated ND Filter Reduce Exposure in Selective Areas Soft Edge Graduation Neutral Results Across All Spectrums 15 Layer Coating on Outside of Filter Hydrophobic and Scratch Resistant Rotating and Stackable 7. 5mm Thick Ring Schott Superwite Glass Helping to selectively reduce exposure in certain areas, while allowing for a normal, unaffected exposure in other regions, the Formatt Hitech 77mm Firecrest Graduated ND 1. 5 Filter is ideally

  • 5 Stop Graduated ND Filter
  • Reduce Exposure in Selective Areas
  • Soft-Edge Graduation
  • Neutral Results Across All Spectrums
  • 15-Layer Coating on Outside of Filter
  • Hydrophobic and Scratch-Resistant
  • Rotating and Stackable 7.5mm-Thick Ring
  • Schott Superwite Glass

Helping to selectively reduce exposure in certain areas, while allowing for a normal, unaffected exposure in other regions, the Formatt-Hitech 77mm Firecrest Graduated ND 1.5 Filter is ideally suited to controlling bright skies without underexposing the foreground. The filter is densest at the edge and tapers to clear by the middle, with a soft-edged line of transition between the density and clear areas. The filter is mounted within a rotating frame, and allows for greater control over the orientation of the density to match certain subject types. Neutral density filters do not affect the coloration of the image and the 1.5 density provides a 5 stop reduction in light from entering the lens.

The design of this filter has been optimized for use with digital sensors and promote nearly flat attenuation of visible, UV, and infrared light. Due to imaging sensors' greater susceptibility to infrared light, compared to traditional film, color casts can occur when photographing darker subjects that require increased exposure times. This filter provides a high level of neutrality across all three spectrums in order to eliminate color casts and ensure cleaner, truer blacks.

Applied to the outside of the Schott Superwite glass construction is a 15-layer Firecrest multi-coating, which helps to minimize reflections and flare in order maintain truer colors and contrast. The multi-coating is also scratch-resistant and hydrophobic to benefit the overall durability of the filter. The filter is set within a 7.5mm-thick SuperSlim aluminum filter ring, which features front threads for stacking additional filters or attaching a lens cap.

Graduated ND 1.5 filter for darkening skies and other bright areas of the image.
Provides a 5 stop reduction in light in selective areas.
Soft-edge transition is ideal for circumstances there is not a distinct line of separation between the bright and dark zones of the image.
Rotating mount enables control over the orientation of the density for matching to specific subject shapes and compositions.
Attenuates infrared light, as well as visible and UV wavelengths, to maintain color neutrality and suppress color casts due to increased exposure lengths.
The 15-layer Firecrest coating has been applied directly to the outside of the filter glass by way of a vacuum-formed, hard-coated, electrolytic process. The multi-coating is water- and scratch-resistance for greater overall durability.
Schott Superwite glass promotes maintained image clarity and is set within a 7.5mm-thick SuperSlim aluminum filter ring.
UPC: 886235191191
Type Graduated neutral density with IR attenuation
Size 77 mm (0.3" / 7.5 mm-thick)
Density 1.5 (5 stop)
Effect Reduces exposure in selective areas and helps to attenuate IR contamination
Construction Schott Superwite glass
Aluminum alloy filter ring
Front Filter Thread Size 77 mm
Packaging Info
Package Weight 0.15 lb
Box Dimensions (LxWxH) 4.4 x 4.4 x 0.8"
All product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.
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SKU: 89377687561

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4.9 ★★★★★
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N
Nader
Bozeman, 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
Carnegie, 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
Bozeman, 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
Alexandria, 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
Port Orchard, 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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