Makita DHP 487 SF1X9 Akku Schlagbohrschrauber 18 V 40 Nm Brushless + 1x Akku 3,0 Ah + Ladegerät + 96 tlg. Zubehör Set + Koffer
SKU: 80985844180

Makita DHP 487 SF1X9 Akku Schlagbohrschrauber 18 V 40 Nm Brushless + 1x Akku 3,0 Ah + Ladegerät + 96 tlg. Zubehör Set + Koffer

$119.11 $132.35
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Description

Makita DHP 487 SF1X9 Akku Schlagbohrschrauber 18 V 40 Nm Brushless + 1x Akku 3,0 Ah + Ladegerät + 96 tlg. Zubehör Set + KofferLieferumfang: 1x Makita DHP 487 Akku Schlagbohrschrauber 1x Makita BL1830B 18 V 3,0 Ah Akku 1x Makita DC18SD Ladegert 2x 37 tlg. Bit Set B 28606 bestehend aus jeweils: 36x Bit 25 mm ( PZ0 PZ1 2x PZ2 PZ3 PH0 PH1 2x PH2 PH3 H3 H4 H5 H6 SL4 SL4,5 SL5,5 SL6,5 T10 2x T15 2x T20 2x T25 T27 2x T30 T40 TH10 TH15 TH20 TH25 TH27 TH30 TH40 ) 1x magnet Bithalter 1x 18 tlg. Bohrer Set D 46202 bestehend aus: 6x Holzbohrer ( 3,0 4,0 5,0 6,0 8,0 10,0 mm ) 6x

Lieferumfang:

- 1x Makita DHP 487 Akku Schlagbohrschrauber
- 1x Makita BL1830B 18 V 3,0 Ah Akku
- 1x Makita DC18SD Ladegerät
- 2x 37 tlg. Bit Set B-28606 bestehend aus jeweils:
    - 36x Bit 25 mm ( PZ0 / PZ1 / 2x PZ2 / PZ3 / PH0 / PH1 / 2x PH2 / PH3 / H3 / H4 / H5 / H6 / SL4 / SL4,5 / SL5,5 / SL6,5 / T10 / 2x T15 / 2x T20 / 2x T25 / T27 / 2x T30 / T40 / TH10 / TH15 / TH20 / TH25 / TH27 / TH30 / TH40 )
    - 1x magnet Bithalter
- 1x 18 tlg. Bohrer Set D-46202 bestehend aus:
    - 6x Holzbohrer ( 3,0 / 4,0 / 5,0 / 6,0 / 8,0 / 10,0 mm )
    - 6x Steinbohrer ( 3,0 / 4,0 / 5,0 / 6,0 / 8,0 / 10,0 mm )
    - 6x Metallbohrer ( 3,0 / 4,0 / 5,0 / 6,0 / 8,0 / 10,0 mm )
- 1x Maßband 3m
- 1x Bit Schraubendreher
- 1x Ratschen Schraubendreher
- 1x Cuttermesser
- 1x Alu Schubladen Koffer

Produktbeschreibung:

Der Makita DHP 487 ist ein sehr kompakter und vielseitig einsetzbarer Akku Schlagbohrschrauber. Durch seine kompakte Bauweise ist er die ideale Wahl um auch in schwer zugänglichen Bereichen arbeiten zu können. Der Schlagbohrschrauber ist mit einem 2-Gang Planetengetriebe mit einer robusten Alu Getriebegehäuseabdeckung ausgestattet, welche ein einfaches Schrauben, sowie Bohren gewährleistet. Darüber hinaus verfügt der DHP 487 über einen Bürstenlosen Motor ( Brushless ), eine Motorbremse, einen Rechts-Links-Lauf und ein 13mm Schnellspannbohrfutter. Das zuschaltbare Schlagwerk ermöglicht zusätzlich zum Bohren in Holz und Stahl, auch das Bohren in Mauerwerk. Als weiteres Ausstattungsmerkmal bietet die verbaute doppel LED, mit Nachglimmfunktion, gute Lichtverhältnisse in schlecht beleuchteten Räumen. Der ergonomisch neu designte Handgriff, soll gesundheitliche Schäden minimieren, somit sind lange Arbeitstage ohne nennenswerte Ermüdungserscheinungen möglich. Makita ist ein führender Hersteller von Elektrowerkzeugen, eine hohe Verarbeitungsqualität und ein riesiges 18 V Programm gehören zu den Markenzeichen des Unternehmens.  

Technische Daten:

Hersteller: Makita
Herstellerbezeichnung: DHP 487
Akkuspannung: 18 V
Akkusystem: LXT
Akkuschutzsystem: Ja
Leerlaufdrehzahl: 0 - 500 / 1700 min⁻¹
Drehmoment hart/weich: 40 / 25 Nm
Drehmomenteinstellungen: 20
Bohrleistung in Holz: 36 mm
Bohrleistung in Stahl: 13 mm
Aufnahme: 1/2"-20UNF "
Bohrfutterspannweite: 1,5 - 13 mm
Produktgewicht: 1,6 kg
Produktabmessung (L x B x H): 150 x 81 x 232 mm
Akkutyp (Ni-Cd / Li-Ion): Li-ion


Bei gewerblicher Nutzung beachten Sie bitte die Bauvorschriften!

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SKU: 80985844180

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4.3 ★★★★★
Based on 1623 reviews
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Product Reviews
O
Om S
Fort Morgan, 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
Houston, 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
Waukegan, 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
Lowell, 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
Massapequa, 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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