mem-isolate is a Rust crate designed to execute potentially unsafe code within isolated memory compartments. It leverages Linux's memfd_create system call to create anonymous memory mappings, allowing developers to run untrusted code within these confined regions, limiting the potential damage from vulnerabilities or exploits. This sandboxing approach helps mitigate security risks by restricting access to the main process's memory, effectively preventing malicious code from affecting the wider system. The crate offers a simple API for setting up and managing these isolated execution environments, providing a more secure way to interact with external or potentially compromised code.
C Plus Prolog is a project that embeds a Prolog interpreter within C++ code, allowing for logic programming within a C++ application. It aims to provide a seamless integration where Prolog predicates can be called directly from C++ and vice-versa, enabling the combination of Prolog's declarative power with C++'s performance and imperative features. The project leverages a modified version of SWI-Prolog, a popular open-source Prolog implementation, and offers a bidirectional interface for data exchange between the two languages. This facilitates the development of applications that benefit from both efficient procedural code and the logical reasoning capabilities of Prolog.
Hacker News users discussed the practicality and niche appeal of C Plus Prolog. Some expressed interest in its potential for specific applications like implementing rule engines or program analysis tools, while others questioned the performance implications of embedding Prolog within C++. One commenter suggested that a cleaner approach might involve interfacing Prolog with a language like Rust. Several pointed out the project's age and apparent inactivity, raising concerns about maintainability and documentation. The potential for improved tooling using C++-based IDEs was mentioned as a possible benefit. Overall, the discussion centered around the specialized nature of the project and the trade-offs involved in its approach.
This post explores optimizing Ruby's Foreign Function Interface (FFI) performance by using tiny Just-In-Time (JIT) compilers. The author demonstrates how generating specialized machine code for specific FFI calls can drastically reduce overhead compared to the generic FFI invocation process. They present a proof-of-concept implementation using Rust and inline assembly, showcasing significant speed improvements, especially for repeated calls with the same argument types. While acknowledging limitations and areas for future development, like handling different calling conventions and more complex types, the post concludes that tiny JITs offer a promising path toward a much faster Ruby FFI.
The Hacker News comments on "Tiny JITs for a Faster FFI" express skepticism about the practicality of tiny JITs in real-world scenarios. Several commenters question the performance gains, citing the overhead of the JIT itself and the potential for optimization by the host language's runtime. They argue that a well-optimized native library, or even careful use of the host language's FFI, could often outperform a tiny JIT. One commenter notes the difficulties of debugging and maintaining such a system, and another raises security concerns related to executing untrusted code. The overall sentiment leans towards established optimization techniques rather than introducing a new layer of complexity with a tiny JIT.
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.
Summary of Comments ( 5 )
https://news.ycombinator.com/item?id=43601301
Hacker News users discussed the practicality and security implications of the
mem-isolatecrate. Several commenters expressed skepticism about its ability to truly isolate unsafe code, particularly in complex scenarios involving system calls and shared resources. Concerns were raised about the performance overhead and the potential for subtle bugs in the isolation mechanism itself. The discussion also touched on the challenges of securely managing memory in Rust and the trade-offs between safety and performance. Some users suggested alternative approaches, such as using WebAssembly or language-level sandboxing. Overall, the comments reflected a cautious optimism about the project but acknowledged the difficulty of achieving complete isolation in a practical and efficient manner.The Hacker News post "Show HN: I built a Rust crate for running unsafe code safely" (linking to the
mem-isolatecrate) generated a moderate amount of discussion, mostly focused on the complexities and nuances of memory safety in Rust, and whether the crate truly offers a "safe" solution for running unsafe code.Several commenters express skepticism about the claim of "safely" running unsafe code. One points out the inherent contradiction, suggesting the term is an oxymoron. Another argues that true safety requires formal verification, and anything short of that is merely reducing the attack surface rather than eliminating it. This sentiment is echoed by another commenter who highlights the difficulty in proving the soundness of the approach and the potential for subtle bugs to undermine the isolation.
A few comments delve into the specifics of
mem-isolate's implementation. One user questions its practicality for real-world scenarios, suggesting that the overhead of serialization and deserialization, coupled with the limitations on system call access, could severely limit its usefulness. They also mention the potential performance impact and the challenge of managing data dependencies between isolated processes.The discussion also touches upon alternative approaches to isolating unsafe code, such as WebAssembly. One commenter mentions Wasmtime as a more mature and robust solution, although they acknowledge that Wasmtime might not be suitable for all use cases. Another suggests using language-level sandboxing features provided by some languages.
Some users discuss the trade-offs between security and performance. One commenter notes that while complete memory safety is desirable, it often comes at a cost to performance. They suggest that in certain situations, a calculated risk with less strict isolation might be acceptable if performance is a critical factor.
Finally, a few comments express general interest in the project and commend the author for tackling a challenging problem. They acknowledge the difficulty of achieving true memory safety in systems programming and appreciate the effort to improve the security of Rust code. However, even these positive comments maintain a cautious tone, reflecting the overall skepticism towards the claim of absolute safety.