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Description
D16 Antresol 플랜저 타입 이펙터D16 Group Antresol classic Mistress stomp box . 1970 flanger type effect stomp box . D16 Group . impulse solid state analog delay line (Bucket Brigade Device) . BBD delay line . . Key features: True emulation of BBD based analog delay line. Optional independent parameter control for each stereo channel. Selectable L R or M S stereo processing modes. Mistress mode allows the whole unit to work the same as the classic flanger. LFO can be synchronized
D16 Group 에서 Antresol 라는 classic Mistress stomp box 플러그인을 발표하였습니다.
이 제품은 1970년대 중반에 나온 flanger-type effect 로서 독특하면서도 풍부한 사운드 캐릭터를 가지고 있는 오리지널 stomp box 제품이었습니다. 이것을 D16 Group 에서 정밀하게 에뮬레이션하여 플러그인화 하였습니다.
impulse 방식으로 사운드 반응을 정밀하게 모델링하였으며 solid-state analog delay line (Bucket Brigade Device)느낌을 확실하게 구현하였습니다. 그리하여 BBD-기반 delay line 느낌을 완벽하게 컨트롤할 수 있게 되었습니다.
이 제품은 아래와 같은 실제 장비와 주파수 반응이 완벽하게 똑같이 반응하도록 모델링 되었다고 합니다.
Key features:
- True emulation of BBD based analog delay line.
- Optional independent parameter control for each stereo channel.
- Selectable L/R or M/S stereo processing modes.
- Mistress mode allows the whole unit to work the same as the classic flanger.
- LFO can be synchronized with your host application.
- 3 different LFO-to-BBD clock curves.
- Ultra-low aliasing output.
- Highly musical and pleasant sound.
- Tag-based browser.
- MIDI-learn functionality throughout.
- 64-bit internal processing.
데모 사운드
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4.5 ★★★★★
Based on 1931 reviews
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Product Reviews
★★★★★ 4
Title: Really Good Book for Learning LLMs
Format: Paperback, Format: Paperback
I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon.
The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice.
Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening.
Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development.
Recommended for anyone building AI systems professionally.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 25, 2025
★★★★★ 5
a comprehensive guide at the intersection of generative AI and cybersecurity
Format: Kindle
This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity.
What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike.
Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 2, 2025
★★★★★ 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
★★★★★ 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
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Reviewed in the United States on June 9, 2025
★★★★★ 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.
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Reviewed in the United States on August 10, 2025
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