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Description
Reflet 3-Functions Thermostatic Valve Trim - Brushed Gold TRF23BG RIOBEL> Dimensions: > Origin: > Finish: > Elegant and beautifully designed shower control creates a stunning, thoughtful shower design Solid brass construction delivers the utmost in beauty, durability and long lasting performance Innovative engineering perfectly balances temperature, pressure and flowin both high and low pressure environmentsdelivering a predictable, optimal and sensorial shower experience Must order compatible House of Rohl R23
- Elegant and beautifully-designed shower control creates a stunning, thoughtful shower design
- Solid brass construction delivers the utmost in beauty, durability and long-lasting performance
- Innovative engineering perfectly balances temperature, pressure and flowin both high- and low-pressure environmentsdelivering a predictable, optimal and sensorial shower experience
- Must order compatible House of Rohl® R23 thermostatic and pressure balance rough-in valve separately
- No need to plumb in a separate shut-off or diverter valve, allowing for a more streamlined shower design with less clutter on the wall
- Cartridge is included with trim
- Supports up to 3 functions allowing you to run 2 independent functions or both at the same time
- Compatible House of Rohl® R23 rough-in valve supports a flow rate of 6.3 GPM at 60 PSI for an immersive shower experience
- All necessary mounting hardware included
- Can run showerhead or handshower independently; or showerhead and handshower at the same time
- Purchase with confidence knowing that this product is backed by limited lifetime warranty
- Set it and forget it temperature memory
- Combines both thermostatic and pressure balance technologies in one valve
- Manufacturer supplied links
- Specification Sheet
- Installation Manual
- Parts Breakdown
- Manufacturer Product Page
- Manufacturer Web Site
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4.3 ★★★★★
Based on 17 reviews
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Product Reviews
★★★★★ 5
Your Blueprint for Building Smarter AI!
Format: Paperback
If you're building AI and sometimes feel a bit lost, "LLM Design Patterns" by Ken Huang is like finding the secret map you've been searching for.
Ken Huang, who clearly knows his stuff (he's a renowned AI expert and works with big names like OWASP and NIST), writes in a way that just clicks, without getting bogged down in super-dense tech talk. The author even acknowledges using AI to make the language clearer for a smooth reading experience!
This book covers everything you need, from getting your data squeaky clean to making AI agents that can actually think and act autonomously. For me, the parts on Retrieval-Augmented Generation (RAG) and advanced ways to 'talk' to your AI (prompting) were particularly eye-opening and immediately useful for my projects.
Plus, it has handy code snippets that really help you grasp the ideas. While they're not ready for direct production copy-pasting, they illustrate the concepts perfectly for learning. It's not for absolute beginners – you'll want some basic Python and machine learning smarts to get the most out of it – but the effort is totally worth it. It even delves into making sure your AI is fair and unbiased, which was a real lightbulb moment for me.
This book is crammed with actionable advice; it's less about abstract theory and more about real-world solutions you can actually use. If you're serious about building impressive AI systems professionally, this is a must-read.
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Reviewed in the United States on August 7, 2025
★★★★★ 5
Great Resource when Integrating AI
Format: Kindle
This is a great resource when building systems that integrate with AI. It manages to cover the entire lifecycle and even tips for corporate environments!
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Reviewed in the United States on August 26, 2025
★★★★★ 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.
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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.
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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
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Reviewed in the United States on December 31, 2025