Key Facts
- ✓ The NotePin S replaces the squeeze-to-record mechanism with a recessed button.
- ✓ It features a new 'press to highlight' function to flag important conversation moments.
- ✓ The device weighs 0.6 ounces and has an advertised recording range of 9.8 feet.
- ✓ It is available now for $179 on Plaud's website and Amazon.
Quick Summary
Plaud has released the NotePin S, an updated iteration of its AI-powered conversation recording device. The new model introduces a physical button interface, replacing the squeeze-to-record mechanism found on the 2024 original. This change addresses user feedback and adds new functionality for capturing important moments.
The device features a recessed recording button that starts recording with a long press. A key addition is the 'press to highlight' feature, which flags significant parts of a conversation. This data helps the integrated AI learn to prioritize specific content when creating summaries. The NotePin S maintains the capsule-sized form factor and core hardware specifications of the first generation, including dual microphones and a 9.8-foot recording range. It is designed for versatility with included hardware for pin, lanyard, wristband, or clip attachment. The device is available now for $179 via Plaud's website and Amazon.
Design and Interface Changes
The NotePin S represents a functional evolution in Plaud's wearable lineup. While the overall design philosophy remains consistent with the original, the input method has been completely overhauled. The previous squeeze-to-record mechanism has been removed in favor of a dedicated physical button.
This recessed button serves two primary functions:
- Recording Control: A single long press initiates audio recording.
- Highlighting: A specific press sequence activates the new 'press to highlight' feature.
The 'press to highlight' capability is the device's most significant software addition. By flagging key moments, users provide direct feedback to the device's AI algorithms. This allows the system to better identify and emphasize critical information during the summary generation process, ensuring that the most relevant parts of a discussion are not overlooked.
Technical Specifications
Aside from the interface update, the NotePin S retains the hardware profile of the original model. The device remains a compact, capsule-sized unit intended for discreet wear. It weighs 0.6 ounces, making it lightweight enough for all-day wear without significant burden.
Audio capture is handled by an integrated dual-microphone array. Plaud has advertised a reliable recording range of 9.8 feet. This specification is particularly relevant for users recording meetings or lectures, as audio quality may degrade if the speaker is located beyond this distance. The device is equipped with the necessary hardware to support multiple wearing styles, offering flexibility for various professional and personal use cases.
Availability and Pricing
The NotePin S is available for purchase immediately. Plaud has set the retail price at $179, positioning the device in the premium wearable technology market.
Consumers can purchase the device through two primary channels:
The simultaneous release on both platforms ensures broad accessibility for potential customers looking to acquire the latest iteration of the AI recording wearable.
Conclusion
The launch of the NotePin S signals Plaud's commitment to refining the user experience of their AI wearables. By moving from a gesture-based control to a tactile button interface, the company addresses practical usability while introducing a software feature designed to enhance AI summarization accuracy. The device maintains its competitive hardware specifications, including the dual-microphone setup and versatile wearing options. At a price point of $179, the NotePin S offers a specialized tool for professionals and students seeking to automate the capture and summarization of spoken interactions. As AI integration in consumer hardware continues to grow, Plaud's focus on specific interaction modes like 'highlighting' suggests a trend toward more intentional data collection for machine learning models.
