Researchers have developed a new transistor that could significantly improve edge computing by enabling more efficient hardware implementations of fuzzy logic. This "ferroelectric FinFET" transistor can be reconfigured to perform various fuzzy logic operations, eliminating the need for complex digital circuits typically required. This simplification leads to smaller, faster, and more energy-efficient fuzzy logic hardware, ideal for edge devices with limited resources. The adaptable nature of the transistor allows it to handle the uncertainties and imprecise information common in real-world applications, making it well-suited for tasks like sensor processing, decision-making, and control systems in areas such as robotics and the Internet of Things.
Obsidian-textgrams is a plugin that allows users to create and embed ASCII diagrams directly within their Obsidian notes. It leverages code blocks and a custom renderer to display the diagrams, offering features like syntax highlighting and the ability to store diagram source code within the note itself. This provides a convenient way to visualize information using simple text-based graphics within the Obsidian environment, eliminating the need for external image files or complex drawing tools.
HN users generally expressed interest in the Obsidian Textgrams plugin, praising its lightweight approach compared to alternatives like Excalidraw or Mermaid. Some suggested improvements, including the ability to embed rendered diagrams as images for compatibility with other Markdown editors, and better text alignment within shapes. One commenter highlighted the usefulness for quickly mocking up system designs or diagrams, while another appreciated its simplicity for note-taking. The discussion also touched upon alternative tools like PlantUML and Graphviz, but the consensus leaned towards appreciating Textgrams' minimalist and fast rendering capabilities within Obsidian. A few users expressed interest in seeing support for more complex shapes and connections.
This blog post explores using Go's strengths for web service development while leveraging Python's rich machine learning ecosystem. The author details a "sidecar" approach, where a Go web service communicates with a separate Python process responsible for ML tasks. This allows the Go service to handle routing, request processing, and other web-related functionalities, while the Python sidecar focuses solely on model inference. Communication between the two is achieved via gRPC, chosen for its performance and cross-language compatibility. The article walks through the process of setting up the gRPC connection, preparing a simple ML model in Python using scikit-learn, and implementing the corresponding Go service. This architectural pattern isolates the complexity of the ML component and allows for independent scaling and development of both the Go and Python parts of the application.
HN commenters discuss the practicality and performance implications of the Python sidecar approach for ML in Go. Some express skepticism about the added complexity and overhead, suggesting gRPC or REST might be overkill for simple tasks and questioning the performance benefits compared to pure Python or using GoML libraries directly. Others appreciate the author's exploration of different approaches and the detailed benchmarks provided. The discussion also touches on alternative solutions like using shared memory or embedding Python in Go, as well as the broader topic of language interoperability for ML tasks. A few comments mention specific Go ML libraries like gorgonia/tensor as potential alternatives to the sidecar approach. Overall, the consensus seems to be that while interesting, the sidecar approach may not be the most efficient solution in many cases, but could be valuable in specific circumstances where existing Go ML libraries are insufficient.
Voyager 1, currently over 15 billion miles from Earth, successfully transmitted data using a backup thruster control system not activated since 1981. NASA engineers recently rediscovered the system's functionality and tested it, confirming Voyager 1 can still send scientific data back to Earth via this alternative route. This extends the spacecraft's operational lifespan, though using the backup system requires slightly higher power consumption. While the primary thruster control system remains functional for now, this rediscovery provides a valuable backup communication method for the aging probe.
Hacker News commenters generally expressed awe and excitement at Voyager 1's continued operation and the ingenuity of the engineers who designed and maintain it. Several commenters highlighted the remarkable longevity and durability of the spacecraft, given its age and the harsh environment of interstellar space. Some discussed the technical details of the trajectory correction maneuver and the specific hardware involved, including the attitude control thrusters and the now-resurrected TCM thruster. A few questioned the phrasing of "breaking its silence," pointing out that Voyager 1 continues to send scientific data. Others reflected on the historical significance of the Voyager missions and the small, but important, course correction that ensures continued communication with Earth for a few more years.
Summary of Comments ( 5 )
https://news.ycombinator.com/item?id=42118298
Hacker News commenters expressed skepticism about the practicality of the reconfigurable fuzzy logic transistor. Several questioned the claimed benefits, particularly regarding power efficiency. One commenter pointed out that fuzzy logic usually requires more transistors than traditional logic, potentially negating any power savings. Others doubted the applicability of fuzzy logic to edge computing tasks in the first place, citing the prevalence of well-established and efficient algorithms for those applications. Some expressed interest in the technology, but emphasized the need for more concrete results beyond simulations. The overall sentiment was cautious optimism tempered by a demand for further evidence to support the claims.
The Hacker News post "Transistor for fuzzy logic hardware: promise for better edge computing" linking to a TechXplore article about a new transistor design for fuzzy logic hardware, has generated a modest discussion with a few interesting points.
One commenter highlights the potential benefits of this technology for edge computing, particularly in situations with limited power and resources. They point out that traditional binary logic can be computationally expensive, while fuzzy logic, with its ability to handle uncertainty and imprecise data, might be more efficient for certain edge computing tasks. This comment emphasizes the potential power savings and improved performance that fuzzy logic hardware could offer in resource-constrained environments.
Another commenter expresses skepticism about the practical applications of fuzzy logic, questioning whether it truly offers advantages over other approaches. They seem to imply that while fuzzy logic might be conceptually interesting, its real-world usefulness remains to be proven, especially in the context of the specific transistor design discussed in the article. This comment serves as a counterpoint to the more optimistic views, injecting a note of caution about the technology's potential.
Further discussion revolves around the specific design of the transistor and its implications. One commenter questions the novelty of the approach, suggesting that similar concepts have been explored before. They ask for clarification on what distinguishes this particular transistor design from previous attempts at implementing fuzzy logic in hardware. This comment adds a layer of technical scrutiny, prompting further investigation into the actual innovation presented in the linked article.
Finally, a commenter raises the important point about the developmental stage of this technology. They acknowledge the potential of fuzzy logic hardware but emphasize that it's still in its early stages. They caution against overhyping the technology before its practical viability and scalability have been thoroughly demonstrated. This comment provides a grounded perspective, reminding readers that the transition from a promising concept to a widely adopted technology can be a long and challenging process.