SKU: 86445007460

Kiloview N40 UHD HDMI / NDI Bidirectional Converter

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Description

Kiloview N40 UHD HDMI / NDI Bidirectional ConverterThe Kiloview N40 (often cataloged as KIL N40 or N40 Plus) is a professional, pocket sized 4K UHD HDMI to NDI bidirectional hardware converter. Driven by an internal FPGA chip, it functions as a switchable, half duplex transceiver. This means it can either act as an Encoder (compressing a raw 4K HDMI camera signal into a low latency IP network stream) or a Decoder (pulling an IP stream from the network and outputting it via physical HDMI to a display,

The Kiloview N40 (often cataloged as KIL-N40 or N40 Plus) is a professional, pocket-sized 4K UHD HDMI to NDI bidirectional hardware converter. Driven by an internal FPGA chip, it functions as a switchable, half-duplex transceiver. This means it can either act as an Encoder (compressing a raw 4K HDMI camera signal into a low-latency IP network stream) or a Decoder (pulling an IP stream from the network and outputting it via physical HDMI to a display, project, or legacy switcher).

Unlike Kiloview's multi-codec Gen 2 transceivers, the N40 focuses heavily on processing premium, high-bandwidth Full NDI (SpeedHQ) at up to 4K UHD at 60 frames per second, delivering visually lossless images with sub-frame latency.

Core Engineering and Operational Features

  • True Bidirectional Versatility: The unit provides two distinct operational workflows chosen through its local menu or web interface. In Encoding Mode, it ingests a 4K60 HDMI feed and broadcasts it onto the NDI network. In Decoding Mode, it pulls any designated NDI source off the network and outputs it cleanly to a physical HDMI destination.

  • Prinstine Image Performance: Equipped with an HDMI 2.0 pipeline, the N40 natively handles resolutions up to 3840 × 2160 pixels at 60fps. It processes professional YCbCr 4:2:2 color profiles, ensuring highly accurate color tracking and clean, artifact-free edges—critical for live green-screen keying or graphics rendering.

  • Lag-Free Local Loop-Through: When configured as an encoder, the second HDMI port acts as a physical, zero-latency loop-out. This allows camera operators to feed a local on-camera confidence monitor while sending the compressed NDI packet down the network line.

  • Lagless Decoder Stream Switching: When acting as a decoder, the N40 uses Kiloview’s specialized frame-buffer management technology. Operators can preset up to 9 network NDI stream addresses. Switching between these channels via the dashboard triggers instantly without black frames, signal lag, or frozen macroblocks.

  • Flexible Wide-Voltage Power Array: The unit is highly adaptable in the field. It can run on standard Power over Ethernet (PoE) via its Gigabit port, or draw power across a wide 5V to 18V DC spectrum through its USB-C terminal. This allows it to be powered by a basic phone power bank, a USB port on a laptop, or an on-camera D-Tap battery link.

  • Oversized Production Tally Light: Built straight into the face of the rugged casing is a large, highly visible LED Tally block. It communicates bi-directionally with NDI-compatible production switchers (like NewTek TriCaster, vMix, or OBS), glowing Red for Program (PGM) and Green for Preview (PVW) to guide on-camera talent.

  • PTZ Control and Voice Intercom: The N40 supports bi-directional 3.5mm analog audio loops for voice intercom connections when coupled with a USB headset and the Kiloview Intercom Server (KIS). It also natively routes remote PTZ camera commands over IP or via a USB-to-serial expansion cable using standard VISCA, Pelco-D, and Pelco-P telemetry.

Hardware Technical Specifications Matrix

Parameter Kiloview N40 System Specifications
Video Input Interface 1 × HDMI 2.0 Type-A (Up to 4Kp60, 4:2:2 processing)
Video Output Interface 1 × HDMI 2.0 Type-A (Active loop-out or decoded output)
Network Interface 1 × RJ45 1000Mbps Ethernet port (with PoE support)
NDI Codec Flavor High-Bandwidth Full NDI (SpeedHQ compression profile)
Target NDI Bandwidths 4Kp60: ~250Mbps | 4Kp30: ~200Mbps | 1080p60: ~125Mbps
Audio Line Ports 1 × 3.5mm Headphone/Microphone TRRS Analog Jack
USB Interface Ports 1 × USB-C (Power delivery input), 1 × USB expansion port
End-to-End Processing Latency Optimized hardware mode yielding under 100ms total latency
Power Consumption Highly efficient 6 Watts maximum power draw
Physical Enclosure Profile 100 mm × 80 mm × 24 mm | Weight: ~235 grams

💡 Operational System Tips for the N40 Converter

The Full NDI Multi-Cam Switch Calculus: Because the N40 operates exclusively in high-bandwidth Full NDI, it places immediate data demands on your network switch gear. Since a single 4K60 stream requires 250 Megabits per second of continuous throughput, running just three N40 units at full resolution will hover around 750 Mbps. This safely approaches the real-world threshold of a common 1GbE copper switch bottleneck. If you plan to scale a multi-cam live venue beyond three 4K lines, always backhaul your edge switches to a core switch network utilizing high-capacity 10GbE SFP+ fiber optic links to protect against dropped frames or packet losses.

  • Deploying the USB Keypad Source Preset Switcher: If you are using the N40 in decoding mode to drive a projector or a large display wall at a live event, you can connect a standard, inexpensive USB numeric keypad into the converter's USB expansion slot. Map your favorite network NDI camera streams to the 0–9 keys inside the web UI. Once configured, you can shift your physical display between ten completely different production cameras with a single keypress, creating a localized, lightweight hardware switching terminal.

  • Physical Mechanical Rigging: The lightweight aluminum chassis features standard internal 1/4"-20 threaded mounting options alongside lateral grooves on the frame body. This allows you to attach a cold-shoe mount to fix the converter directly on top of an on-field camera handle, or lock up to four N40 units side-by-side inside Kiloview's specialized 1RU passive rack enclosure inside a central control room.

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SKU: 86445007460

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4.7 ★★★★★
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B
Brahmananda Reddy
Grantham, US
★★★★★ 5
Practical AI Engineering Beyond Prompts — One of the Better Books on Agentic Coding
Format: Paperback
This book is not another “AI coding hype” book. A lot of books talk about agents at a very high level. This one actually explains how things work when you try to use them inside real development workflows. That was the biggest difference for me. What I liked most was the focus on context engineering, memory, MCP, hooks, subagents, and workflow orchestration instead of just “prompt better.” The author spends time explaining why long-running agent systems fail, how context grows over time, and why most AI coding setups become messy without structure. The examples also feel practical — The HookHub project, Next.js setup, GitHub workflows, Claude memory files, and MCP integrations make it easier to connect theory with actual implementation. From my retail domain experience perspective, I could immediately connect this to forecasting and pricing workflows. For example: * agents helping analysts generate specs before model development * automated code review for promo forecasting pipelines * isolated subagents for pricing, promotions, assortment * persistent memory for business rules across teams * MCP integrations to pull context from internal systems safely The section around context isolation and subagents especially stood out because that is very similar to how enterprise forecasting teams already operate in reality. Different teams own different decision spaces. One thing I appreciated: the author does not oversell AI. There is a strong focus on constraints, context pollution, hallucinations, performance degradation, and workflow reliability. That makes the book feel grounded instead of marketing-heavy. This is not for complete beginners though. If someone has never worked with Git, APIs, coding agents, or LLM workflows, parts of the book may feel overwhelming early on. The author clearly says this is not beginner-level content. Overall, probably one of the more practical books I have read recently on agentic coding systems. Good for: * software engineers * AI engineers * enterprise architecture teams * technical product teams * analytics leaders trying to operationalize AI development workflows Especially useful if your organization is trying to move from “AI demos” into actual production workflows.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 20, 2026
U
UA
Boise, US
★★★★★ 5
A Good Reality Check on How AI Agents Actually Work in Enterprise Systems
Format: Paperback
Most AI books stop at prompts. This one goes deeper into how agent systems actually behave once you try to use them inside large workflows with memory, tools, permissions, automation, and multiple agents working together. That part felt very relevant for healthcare and enterprise environments. The book does a good job explaining why context engineering matters and how poor context handling creates hallucinations, inconsistent outputs, and degraded performance over time. Honestly, that is one of the biggest problems organizations underestimate right now. In healthcare workflows, context matters a lot: * prior interactions * business rules * auditability * escalation logic * safety constraints * tool permissions * workflow boundaries The sections on persistent memory, scoped context, subagents, and structured workflows connected strongly to that reality. I work in enterprise analytics, and while reading this book I kept thinking about use cases like: * pharmacy workflow automation * prior authorization support systems * coding assistants for healthcare engineering teams * AI copilots for operational analytics * agent-based escalation systems * claims and workflow orchestration The MCP chapters were also useful because they explain integration challenges clearly instead of treating tooling as magic. What made this book stand out for me was the balance between implementation and architecture. The author explains: * why long contexts fail * how context poisoning happens * why isolation matters * when parallel agents help * when they actually create more complexity That level of honesty is missing in many AI books right now. Another thing: the examples are not overly academic — The Next.js project setup, GitHub automation, Claude desktop workflows, memory systems, hooks, and subagents make the learning process feel practical and hands-on. One limitation: this book assumes technical background. Someone completely new to coding agents, LLMs, Git, or development workflows may struggle in the first few chapters. But for engineers, AI teams, enterprise architects, and technical leaders trying to understand where agentic coding is actually going, this book is worth reading. Especially for organizations trying to operationalize AI safely instead of just experimenting with chatbots.
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Reviewed in the United States on May 20, 2026
C
Christopher West
West Palm Beach, US
★★★★★ 5
Great book! Practical and for developers that already use AI!
Format: Paperback
I purchased "Agentic Coding" by Claude Code due to my desire for an alternative to generic "Prompt Template" type resources related to AI-based development. This book accomplishes just that. As opposed to merely viewing Claude Code as a "magic box", the author has explained how to utilize it in conjunction with other actual development processes. The authors' emphasis on "context engineering" (i.e., structuring data/information; managing knowledge in a project; guiding an AI agent to produce consistent results vs. producing random/unknown results) represents the strongest component of the book. It should be noted that the book appears to be intended primarily for experienced developers with prior experience in software development and/or familiarity with AI-based development tools. Should you be familiar with Git, the command-line interface, and/or modern development processes, you may find this resource very helpful. Conversely, I did appreciate the fact that there were no novice-oriented descriptions provided throughout the book. The aspect of the book that I found most valuable, however, is the extremely pragmatic nature of the material contained within. The examples illustrated through developing/maintaining CLAUDE.md files; utilizing Claude Code in combination with GitHub Workflows; employing MCP Servers; and creating multi-agent or sub-agent workflows all seemed to reflect a clear focus on "real world usage" rather than theoretical constructs. In addition, each chapter builds upon previous chapters in such a manner as to provide a logical progression through which the reader can easily understand and ultimately implement the concepts learned. I also appreciated that the author included guidance on responsible utilization of the tool(s), as well as maintaining control over what changes are made by the agent. While numerous books regarding AI focus solely on what AI tools can accomplish, this book addresses both how to utilize these tools effectively in a real codebase, as well as responsibility and safety considerations. In summary, this is not a book for individuals completely inexperienced in either programming or generative AI. However, if you are currently experimenting with tools such as Claude, Cursor, GitHub Actions, or MCP, this is likely one of the more useful and practical books available on the subject. Recommended for software engineers seeking to transition from simply "prompting an AI" into establishing a repeatable/professional workflow process surrounding agentic coding.
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Reviewed in the United States on April 11, 2026
P
Paul Pollock
Massapequa, US
★★★★★ 4
⭐⭐⭐⭐ (so far)
Format: Paperback
I'm maybe a third of the way through this and already rethinking how I talk to coding agents. The reframe from "prompt engineering" to "context engineering" sounds like semantics until Marco walks you through why context poisoning, context clash, the Goldilocks zone for system prompts. That chapter alone reorganized something in my head. I keep going back to the line about garbage in, garbage out being the real reason agentic systems underperform. The hands-on stuff lands well too. Building the HookHub project from scratch, wiring up Playwright MCP, watching Claude generate a CLAUDE.md file and then not automatically loading a memory file you just created — that moment where you expect magic and get silence instead? That's the kind of honest teaching I appreciate. It made the "why" behind memory hierarchies click.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 12, 2026
J
Jonathan Reeves
Waukegan, US
★★★★★ 5
Essential Reading for Developers Serious About Agentic AI Workflows with Claude Code
Format: Paperback
Agentic Coding with Claude Code is easily one of the most practical and forward-thinking AI development books I’ve read. Instead of treating Claude Code like a simple chatbot, this book shows how to turn it into a true agentic development platform capable of handling real-world engineering workflows. What I appreciated most was how actionable the content is. The explanations around slash commands, hooks, persistent memory files, and MCP servers are incredibly clear and immediately useful. The author does an excellent job balancing foundational concepts with hands-on implementation, making advanced topics like multi-agent orchestration and hierarchical delegation approachable for experienced developers. The chapters on MCP and context engineering were especially valuable. Most AI books stay at the surface level, but this one dives deep into structured context sharing, workflow automation, and scalable AI-assisted development practices that actually matter in production environments. I also liked that the book focuses heavily on maintainability and control. It doesn’t just show flashy demos—it teaches how to safely integrate AI agents into existing terminal and IDE workflows while enforcing coding standards and keeping projects organized. The examples using Claude Code with Next.js projects were practical and helped connect the concepts to real software engineering scenarios. The sections on subagents, planning workflows, and reusable automation patterns opened my eyes to entirely new ways of approaching AI pair programming and development productivity. If you are a developer, AI engineer, or technical lead looking to move beyond basic prompt engineering and build reliable, scalable AI-assisted workflows, this book is absolutely worth reading. Highly recommended for anyone serious about modern agentic coding and AI-powered software development.
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Reviewed in the United States on May 9, 2026

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