SKU: 12771940774

Allant+ 6 Lowstep Galactic Grey 400Wh

$1642.05 $1824.50
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Description

Allant+ 6 Lowstep Galactic Grey 400WhDas Allant+ 6 Lowstep ist ein luxurises E Bike fr Pendelfahrten, aber auch fr Entdeckungstouren abseits viel befahrener Straen. Ausgestattet ist es mit dem neuen Bosch Smart System Motor, der mehr Konnektivitt bereitstellt als je zuvor. Koppele dein Smartphone mit deinem Bike und du kannst Aktivitten protokollieren, Routen planen und vieles mehr. Du kannst zwischen unterschiedlichen Akkus von 545 bis 725 Wh whlen, sodass du ber deine gesamte

 

Das Allant+ 6 Lowstep ist ein luxuriöses E-Bike für Pendelfahrten, aber auch für Entdeckungstouren abseits viel befahrener Straßen. Ausgestattet ist es mit dem neuen Bosch Smart System Motor, der mehr Konnektivität bereitstellt als je zuvor. Koppele dein Smartphone mit deinem Bike und du kannst Aktivitäten protokollieren, Routen planen und vieles mehr. Du kannst zwischen unterschiedlichen Akkus von 545 bis 725 Wh wählen, sodass du über deine gesamte Fahrstrecke komfortabel unterstützt wirst. Außerdem punktet das Bike an den entscheidenden Stellen mit höherwertigen Komponenten.

Trek bietet Dir einen eleganten Rahmen aus hydrogeformtem Aluminium mit Lowstep-Design für leichtes Auf- und Absteigen. Eine Auswahl aus zwei unterschiedlichen Akkuoptionen von 545 bis 725 Wh, um dein Bike auf deine Fahrstrecken abzustimmen. Hohen Fahrkomfort dank einer Luftfedergabel, die Bodenwellen und Schlaglöcher absorbiert. Einen leistungsstarken Bosch Smart System Performance Line CX Motor (250 Wh, 85 Nm), der dich bis zu einer Geschwindigkeit von 25 km/h unterstützt. Ein System, das sich vom Smartphone aus leicht auf deine Bedürfnisse abstimmen lässt. Innenverlegte Züge spendieren dem Bike eine aufgeräumte Optik, und die Beleuchtung sorgt fürs Sehen und Gesehenwerden.

 

  • Boschs neues Smart System gibt dir die volle Kontrolle und noch mehr Anpassungsmöglichkeiten – dank neuer, per Bluetooth verbundener LED Remote und eBike Flow App.
  • Die Luftfedergabel absorbiert Bodenwellen und Schlaglöcher und sorgt so für Laufruhe und Fahrkomfort.
  • Dank mitgeliefertem Zubehör wie Schutzblechen und akkugespeister Beleuchtung ist dieses Bike sofort einsatzbereit.
  • Das Lowstep-Design ermöglicht leichtes Auf- und Absteigen, ganz egal für welches Outfit du dich entscheidest.


Ausstattung: 

Rahmen: High-performance hydroformed alloy, internal cable routing, external battery mount, post-mount disc, 135x5mm QR
Gabel: SR Suntour Mobie 34, Luftfeder, verstellbare Zugstufe und Druckstufe, konischer Aluminiumgabelschaft, 15 x 100 mm Steckachse, 60 mm Federweg
Max. kompatibler Gabelfederweg: 63 mm
VR-Nabe: Bontrager, gedichtete Lager, 32-Loch, 15-mm-Aluminiumsteckachse
HR-Nabe: Formula CL-52, Aluminium, 135 x 5 mm Schnellspannachse
HR-Schnellspanner: 148 x 5 mm Schraubsteckachse
Felge: Bontrager Kovee, Hohlkammerfelge, Tubeless Ready, 28-Loch, 23 mm Innenweite, Presta-Ventil
Reifen: Schwalbe G-One, 650 x 57 mm
Max. Reifengröße: 27.5 x 2.40"
Schalthebel: Shimano Deore M5130, 10fach
Schaltwerk: Shimano Deore M5130, GS, Shadow Plus
*Kurbel Größe: S, M: ProWheel, Aluminium, 170 mm Kurbelarmlänge
Größe L, XL: ProWheel, Aluminium, 175 mm Kurbelarmlänge
Kettenblatt: ProWheel, 40 Z., Narrow/Wide, Stahl, mit Aluminiumschutz
Kassette: Shimano LG400, 11-43, 10fach
Kette: Shimano LG500, 9/10/11fach
Pedal: rutschfeste Pedale mit Reflektoren
Max. Kettenblattgröße: 1x: 48 Z.
Sattel: Bontrager Commuter Comp
*Sattelstütze Größe: S, M: Bontrager aus Aluminium, 31,6 mm, 12 mm Versatz, 330 mm Länge
Größe L, XL: Bontrager aus Aluminium, 31,6 mm, 12 mm Versatz, 400 mm Länge
Lenker: Aluminium-Lowriser, 31,8 mm, 25 mm Rise, 11 Grad Krümmung, 690 mm Breite
Griffe: Bontrager XR Endurance Elite
*Vorbau Größe: S, M: Bontrager Comp, 31 8 mm, Blendr-kompatibel, 7 Grad, 80 mm Länge
Größe L, XL: Bontrager Comp, 31 8 mm, Blendr-kompatibel, 7 Grad, 90 mm Länge
Bremse: Shimano MT401 / MT420 hydraulische Scheibenbremse, 180 mm Scheibendurchmesser
*Bremsscheibe Größe: S, M, L, XL: Shimano RT30, Center Lock, 203 mm
Größe S, M, L, XL: Shimano EM300, Centerlock-Scheibenaufnahme, 180 mm
Bremsscheibendurchmesser: Max. Bremsscheibendurchmesser: 180 mm vorne & hinten
Ladegerät: Bosch Standardladegerät 4 A, 230 V, Smart System
Motor: Bosch Performance Line CX, 25 km/h
*Leuchte Größe: S, M, L, XL: Spanninga SOLO für E-Bikes
Größe S, M, L, XL: Herrmans H-Black MR8, 180 Lumen, 60 Lux, LED, Scheinwerfer
*Computer Größe: S, M, L, XL: Bosch Kiox 300, Smart System
Größe S, M, L, XL: Bosch LED-Remote, Smart System
Seitenständer: Pletscher Comp Flex 18
Gepäckträger: MIK-kompatibler Heckgepäckträger aus Aluminium, max. Traglast 25 kg
*Schutzblech: Größe: S, M, L, XL: SKS Kunststoff, hinten
Größe S, M, L, XL: SKS Kunststoff, vorne
Gewicht: M – 23,10 kg (mit 545-Wh-Akku)
Max. Gewicht: Dieses Fahrrad hat eine maximale Gewichtsbeschränkung (Fahrrad, Fahrer:in und Beladung) von 136 kg.


*Bitte beachten - die Spezifikation gilt für alle Größen, sofern nicht gesondert aufgeführt

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SKU: 12771940774

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4.7 ★★★★★
Based on 10 reviews
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Product Reviews
O
Om S
San Leandro, US
★★★★★ 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
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
Massapequa, 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
West Palm Beach, 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
Houston, 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

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