This post discusses a common problem in game physics: preventing jittering and instability in stacked rigid bodies. It introduces a technique called "speculative contacts," where potential collisions in the next physics step are predicted and pre-emptively resolved. This allows for stable stacking by ensuring the bodies are prepared for contact, rather than reacting impulsively after penetration occurs. The post emphasizes the improved stability and visual quality this method offers compared to traditional solutions like increasing solver iterations, which are computationally expensive. It also highlights the importance of efficiently identifying potential contacts to maintain performance.
Design pressure, the often-unacknowledged force exerted by tools, libraries, and existing code, significantly influences how software evolves. It subtly guides developers toward certain solutions and away from others, impacting code structure, readability, and maintainability. While design pressure can be a positive force, encouraging consistency and best practices, it can also lead to suboptimal choices and increased complexity when poorly managed. Understanding and consciously navigating design pressure is crucial for creating elegant, maintainable, and adaptable software systems.
HN commenters largely praised the talk and Hynek's overall point about "design pressure," the subtle forces influencing coding decisions. Several shared personal anecdotes of feeling this pressure, particularly regarding premature optimization or conforming to perceived community standards. Some discussed the pressure to adopt specific technologies (like Kubernetes) despite their complexity, simply because they're popular. A few commenters offered counterpoints, arguing that sometimes optimization is necessary upfront and that design pressures can stem from valid technical constraints. The idea of "design pressure" resonated, with many acknowledging its often-unseen influence on software development. A few users mentioned the pressure exerted by limited time and resources, leading to suboptimal choices.
Zig's comptime is powerful but has limitations. It's not a general-purpose Turing-complete language. It cannot perform arbitrary I/O operations like reading files or making network requests. Loop bounds and recursion depth must be known at compile time, preventing dynamic computations based on runtime data. While it can generate code, it can't introspect or modify existing code, meaning no macros in the traditional C/C++ sense. Finally, comptime doesn't fully eliminate runtime overhead; some checks and operations might still occur at runtime, especially when interacting with non-comptime code. Essentially, comptime excels at manipulating data and generating code based on compile-time constants, but it's not a substitute for a fully-fledged scripting language embedded within the compiler.
HN commenters largely agree with the author's points about the limitations of Zig's comptime, acknowledging that it's not a general-purpose Turing-complete language. Several discuss the tradeoffs involved in compile-time execution, citing debugging difficulty and compile times as potential downsides. Some suggest that aiming for Turing completeness at compile time is not necessarily desirable and praise Zig's pragmatic approach. One commenter points out that comptime is still very powerful, highlighting its ability to generate optimized code based on input parameters, which allows for things like custom allocators and specialized data structures. Others discuss alternative approaches, such as using build scripts, and how Zig's features complement those methods. A few commenters express interest in seeing how Zig evolves and whether future versions might address some of the current limitations.
Go's type parameters, introduced in 1.18, allow generic programming but lack the expressiveness of interface constraints found in other languages. Instead of directly specifying the required methods of a type parameter, Go uses interfaces that list concrete types satisfying the desired constraint. This approach, while functional, can be verbose, especially for common constraints like "any integer" or "any ordered type." The constraints package offers pre-defined interfaces for various common use cases, reducing boilerplate and improving code readability. However, creating custom constraints for more complex scenarios still involves defining interfaces with type lists, leading to potential maintenance issues as new types are introduced. The article explores these limitations and proposes potential future directions for Go's type constraints, including the possibility of supporting type sets defined by logical expressions over existing types and interfaces.
Hacker News users generally praised the article for its clear explanation of constraints in Go, particularly for newcomers. Several commenters appreciated the author's approach of starting with an intuitive example before diving into the technical details. Some pointed out the connection between Go's constraints and type classes in Haskell, while others discussed the potential downsides, such as increased compile times and the verbosity of constraint declarations. One commenter suggested exploring alternatives like Go's built-in sort.Interface for simpler cases, and another offered a more concise way to define constraints using type aliases. The practical applications of constraints were also highlighted, particularly in scenarios involving generic data structures and algorithms.
Summary of Comments ( 4 )
https://news.ycombinator.com/item?id=44127173
HN users discuss various aspects of rigid body simulation, focusing on the challenges of achieving stable "rest" states. Several commenters highlight the inherent difficulties with numerical methods, especially in stacked configurations where tiny inaccuracies accumulate and lead to instability. The "fix" proposed in the linked tweet, of directly zeroing velocities below a threshold, is deemed by some as a hack, while others appreciate its pragmatic value in specific scenarios. A more nuanced approach of damping velocities based on kinetic energy is suggested, as well as a pointer to Bullet Physics' strategy for handling resting contacts. The overall sentiment leans towards acknowledging the complexity of robust rigid body simulation and the need for a balance between physical accuracy and computational practicality.
The Hacker News post "Putting Rigid Bodies to Rest" links to a tweet showcasing a demo of a physics engine. The comments section is relatively short, with a primary focus on the specifics of the demo and some broader discussion about physics engines and game development.
One commenter points out that the demo is not actually putting rigid bodies to rest in the traditional physics engine sense. Instead, it's cleverly using joints to create the illusion of stability. They explain that true resting behavior usually involves detecting minimal movement and then freezing the object to prevent further computation. This commenter's observation sparks a small discussion about the practicality and efficiency of this approach versus true resting implementations.
Another commenter highlights the nostalgic aspect of the demo, comparing it to early 3D games and demoscene productions. They express appreciation for the visual simplicity and the focus on a single, well-executed effect.
A further comment dives a bit deeper into the technical details, speculating on how the demo might be handling collision detection and response, given the jointed nature of the construction. They posit that a specialized collision detection algorithm might be used to optimize performance.
The rest of the comments are brief, mostly expressing general interest in the demo or agreeing with previous points. One commenter simply states their appreciation for the "satisfying" nature of the simulation. There's no extensive debate or deeply technical analysis, likely due to the limited scope of the original tweet and the straightforward nature of the demo itself.