SKU: 61082736451
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rennrad licht hinten Fahrrad Rücklicht – Mit Bremslicht & Akku

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Description

rennrad licht hinten Fahrrad Rücklicht – Mit Bremslicht & AkkuPRAKTISCHE BREMSANZEIGE Das Stoptech Rcklicht fr Fahrrder verfgt ber den speziellen VELMIA Sensor, der Bremsungen erkennt und diese ber ein zustzliches grelles LED Licht anzeigt! INDIVIDUELLE NUTZBARKEIT Das Rcklicht verfgt ber zwei Modi: Tag Nachtsensor und manuelles Dauerlicht. Die Bremsanzeigefunktion ist bei beiden aktiv. So hast du die Mglichkeit, das Fahrradlicht nach deinen eigenen Bedrfnissen zu nutzen! EXTREME LEUCHTKRAFT Die VELMIA

  • PRAKTISCHE BREMSANZEIGE - Das Stoptech-Rücklicht für Fahrräder verfügt über den speziellen VELMIA-Sensor, der Bremsungen erkennt und diese über ein zusätzliches grelles LED-Licht anzeigt!

  • INDIVIDUELLE NUTZBARKEIT - Das Rücklicht verfügt über zwei Modi: Tag-/Nachtsensor und manuelles Dauerlicht. Die Bremsanzeigefunktion ist bei beiden aktiv. So hast du die Möglichkeit, das Fahrradlicht nach deinen eigenen Bedürfnissen zu nutzen!

  • EXTREME LEUCHTKRAFT - Die VELMIA Beleuchtung für Fahrräder leuchtet bis zu 500 m weit und sorgt so dafür, dass du für andere Verkehrsteilnehmer bereits aus großer Entfernung gut sichtbar bist!

  • WIEDERAUFLADBARER AKKU - Der Akku des Fahrradlichts ist per Micro-USB schnell & einfach wiederaufladbar und ermöglicht dir mit einer Leuchtdauer von bis zu 7 h spannende Fahrten zu später Stunde!

  • KUNDENORIENTIERTES START-UP - VELMIA ist immer eine Fahrradlänge voraus und bietet dir neben hochwertigen Fahrradprodukten auch einen zuverlässigen, deutschsprachigen Kundenservice!

 

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Befestigungsart

Montage durch Silikonband mit Klicksystem an jeder handelsüblichen Sattelstange

Leuchtdauer bis zu 7h

Leuchtstärke

bis zu 500 m weit

Leuchtmodi

Tag-/Nachtsensor + Bremsanzeige-Funktion, manuelles Dauerlicht + Bremsanzeige-Funktion

Material

Kunststoff

Wasserbeständigkeit 100 % wasserdicht

Stromquelle

Aufladbarer Akku (Lithium-Ionen-Akkus)

Lieferumfang Rücklicht inkl. Halterung, USB-Kabel, Gebrauchsanweisung

Zertifizierung

StVZO-konform

///Wie feinfühlig ist der Sensor für die Bremsfunktion?
Der Sensor ist sehr feinfühlig und reagiert auch bei sehr leichtem Bremsen.

Ist auch ein Reflektor mit eingebaut?
Das Rücklicht verfügt nicht über einen Reflektor, dieser wird noch zusätzlich benötigt.

Kann ich das Rücklicht auch neigen bzw. abwinkeln?
Ja, die Halterung lässt sich ungefähr in einem Winkel von 40-45 Grad abwinkeln.

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SKU: 61082736451

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4.0 ★★★★★
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Product Reviews
J
Jiewen Wang
Draper, US
★★★★★ 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
N
Nader
Lexington, 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
Cuba, 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
Natrona Heights, 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
Grantham, 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

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