This paper introduces Outcome-Based Reinforcement Learning (OBRL), a new RL paradigm that focuses on predicting future outcomes rather than learning policies directly. OBRL agents learn a world model that predicts the probability of achieving desired outcomes under different action sequences. Instead of optimizing a policy over actions, the agent selects actions by optimizing a policy over outcomes, effectively planning by imagining desired futures. This approach allows for more efficient exploration and generalization, especially in complex environments with sparse rewards or long horizons, as it decouples the policy from the low-level action space. The paper demonstrates OBRL's effectiveness in various simulated control tasks, showing improved performance over traditional RL methods in challenging scenarios.
These lecture notes provide a concise introduction to domain theory, focusing on its applications in computer science, particularly denotational semantics. They cover core concepts like partially ordered sets, complete partial orders (cpos), continuous functions, and the fixed-point theorem, explaining how these tools can be used to model computation and give meaning to recursive programs. The notes also touch on more advanced topics such as algebraic cpos and function spaces, providing a solid foundation for further exploration of the subject. The emphasis is on clear explanations and practical examples, making it accessible to those with a background in basic set theory and logic.
HN users generally praised the clarity and accessibility of the lecture notes, particularly for beginners. Several appreciated the focus on intuition and practicality over strict formalism, making the often-dense subject matter easier to grasp. One commenter pointed out the helpful use of diagrams and examples, while others highlighted the effective explanation of core concepts like directed sets and continuous functions. Some suggested additional topics or resources that could further enhance the notes, such as exploring the connection between domain theory and denotational semantics, or including more advanced topics like powerdomains. A few commenters with prior experience in the field expressed renewed appreciation for the foundational material presented in a refreshingly clear way.
In 1979, sixteen teams competed to design the best Ada compiler, judged on a combination of compiler efficiency, program efficiency, and self-documentation quality. The evaluated programs ranged from simple math problems to more complex tasks like a discrete event simulator and a text formatter. While no single compiler excelled in all areas, the NYU Ada/Ed compiler emerged as the overall winner due to its superior program execution speed, despite being slow to compile and generate larger executables. The competition highlighted the significant challenges in early Ada implementation, including the language's complexity and the limited hardware resources of the time. The diverse range of compilers and the variety of scoring metrics revealed trade-offs between compilation speed, execution speed, and code size, providing valuable insight into the practicalities of Ada development.
Hacker News users discuss the Ada competition, primarily focusing on its historical context. Several commenters highlight the political and military influences that shaped Ada's development, emphasizing the Department of Defense's desire for a standardized, reliable language for embedded systems. The perceived over-engineering and complexity of Ada are also mentioned, with some suggesting that these factors contributed to its limited adoption outside of its intended niche. The rigorous selection process for the "winning" language (eventually named Ada) is also a point of discussion, along with the eventual proliferation of C and C++, which largely supplanted Ada in many areas. The discussion touches upon the irony of Ada's intended role in simplifying software development for the military while simultaneously introducing its own complexities.
Professor Simon Schaffer's lecture, "Bits with Soul," explores the historical intersection of computing and the humanities, particularly focusing on the 18th and 19th centuries. He argues against the perceived divide between "cold" calculation and "warm" human experience, demonstrating how early computing devices like Charles Babbage's Difference Engine were deeply intertwined with social and cultural anxieties about industrialization, automation, and the nature of thought itself. The lecture highlights how these machines, designed for precise calculation, were simultaneously imbued with metaphors of life, soul, and even divine inspiration by their creators and contemporaries, revealing a complex and often contradictory understanding of the relationship between humans and machines.
Hacker News users discuss the implications of consciousness potentially being computable. Some express skepticism, arguing that subjective experience and qualia cannot be replicated by algorithms, emphasizing the "hard problem" of consciousness. Others entertain the possibility, suggesting that consciousness might emerge from sufficiently complex computation, drawing parallels with emergent properties in other physical systems. A few comments delve into the philosophical ramifications, pondering the definition of life and the potential ethical considerations of creating conscious machines. There's debate around the nature of free will in a deterministic computational framework, and some users question the adequacy of current computational models to capture the richness of biological systems. A recurring theme is the distinction between simulating consciousness and actually creating it.
Nordström, Petersson, and Smith's "Programming in Martin-Löf's Type Theory" provides a comprehensive introduction to Martin-Löf's constructive type theory, emphasizing its practical application as a programming language. The book covers the foundational concepts of type theory, including dependent types, inductive definitions, and universes, demonstrating how these powerful tools can be used to express mathematical proofs and develop correct-by-construction programs. It explores various programming paradigms within this framework, like functional programming and modular development, and provides numerous examples to illustrate the theory in action. The focus is on demonstrating the expressive power and rigor of type theory for program specification, verification, and development.
Hacker News users discuss the linked book, "Programming in Martin-Löf's Type Theory," primarily focusing on its historical significance and influence on functional programming and dependent types. Some commenters note its dense and challenging nature, even for those familiar with type theory, but acknowledge its importance as a foundational text. Others highlight the book's role in shaping languages like Agda and Idris, and its impact on the development of theorem provers. The practicality of dependent types in everyday programming is also debated, with some suggesting their benefits remain largely theoretical while others point to emerging use cases. Several users express interest in revisiting or finally tackling the book, prompted by the discussion.
This post emphasizes the importance of enumerative combinatorics for programmers, particularly in algorithm design and analysis. It focuses on counting problems, specifically exploring integer compositions (ways to express an integer as a sum of positive integers). The author breaks down the concepts with clear examples, including calculating the number of compositions, compositions with constraints like limited parts or specific part sizes, and generating these compositions programmatically. The post argues that understanding these combinatorial principles can lead to more efficient algorithms and better problem-solving skills, especially when dealing with scenarios involving combinations, permutations, and other counting tasks commonly encountered in programming.
Hacker News users generally praised the article for its clear explanation of a complex topic, with several highlighting the elegance and usefulness of generating functions. One commenter appreciated the connection drawn between combinatorics and dynamic programming, offering additional insights into optimizing code for calculating compositions. Another pointed out the historical context of the problem, referencing George Pólya's work and illustrating how seemingly simple combinatorial problems can have profound implications. A few users noted that while the concept of compositions is fundamental, its direct application in day-to-day programming might be limited. Some also discussed the value of exploring the mathematical underpinnings of computer science, even if not immediately applicable, for broadening problem-solving skills.
Bryan Cantrill laments the decline of the USENIX Annual Technical Conference (ATC), attributing it to a shift away from its core focus on systems research towards more mainstream, less technically rigorous topics. He argues that this broadening scope, driven by a desire for larger attendance and influenced by the "open source" movement, has diluted the conference's identity and diminished its value for hardcore systems researchers. Consequently, he suggests the "golden age" of USENIX ATC, characterized by deep dives into operating systems, filesystems, and networking, has likely passed.
Commenters on Hacker News largely echoed Bryan Cantrill's sentiments about the decline of Usenix ATC, lamenting the loss of its unique character and technical depth. Several attributed this shift to the increasing influence of corporate interests and the rise of "sanitized" presentations focused on product pitches rather than groundbreaking research. Some argued that the conference's prestige had waned, with top researchers opting for venues perceived as more impactful. A few commenters suggested potential remedies, such as stricter review processes prioritizing novel research and limiting corporate influence, but overall, the prevailing tone was one of nostalgia for a bygone era of more rigorous and academically focused technical conferences. The shift towards more general conferences was also mentioned, alongside the proliferation of specialized conferences that may now be better suited for specific research areas.
USENIX has announced the cancellation of the in-person component of the 2024 ATC conference in Boston due to escalating costs, primarily venue and hotel expenses exceeding initial projections. While disappointed about this change, USENIX remains committed to holding a high-quality virtual conference experience during the original dates of July 17-19, 2024. Accepted papers will still be published in the conference proceedings, and authors will have the opportunity to present their work virtually. USENIX is exploring ways to potentially organize smaller, in-person gatherings focused on specific technical tracks during the same timeframe, but details are yet to be finalized. They are actively seeking alternative solutions for future ATCs and look forward to returning to a hybrid format in subsequent years.
The Hacker News comments express disappointment and frustration with USENIX's decision to hold their Advanced Technical Conference (ATC) in Boston, citing high costs, difficult visa processes for international attendees, and Massachusetts' generally unfriendly political climate (particularly regarding abortion access). Some commenters suggest alternative, more accessible locations and question the conference organizers' rationale. Several point out the hypocrisy of USENIX's stated commitment to diversity and inclusion while choosing a location that presents barriers for many. There's a sense of betrayal among long-time attendees, with some vowing to boycott the event. A few commenters offer counterpoints, mentioning Boston's strong technical scene and suggesting that USENIX might have negotiated favorable rates. However, these comments are largely overshadowed by the negative sentiment.
The blog post argues that inheritance in object-oriented programming wasn't initially conceived as a way to model "is-a" relationships, but rather as a performance optimization to avoid code duplication in early Simula simulations. Limited memory and processing power necessitated a mechanism to share code between similar objects, like different types of ships in a harbor simulation. Inheritance efficiently achieved this by allowing new object types (subclasses) to inherit and extend the data and behavior of existing ones (superclasses), rather than replicating common code. This perspective challenges the common understanding of inheritance's primary purpose and suggests its later association with subtype polymorphism was a subsequent development.
Hacker News users discussed the claim that inheritance was created as a performance optimization. Several commenters pushed back, arguing that Simula introduced inheritance for code organization and modularity, not performance. They pointed to the lack of evidence supporting the performance hack theory and the historical context of Simula's development, which focused on simulation and required ways to represent complex systems. Some acknowledged that inheritance could offer performance benefits in specific scenarios (like avoiding virtual function calls), but that this was not the primary motivation for its invention. Others questioned the article's premise entirely and debated the true meaning of "performance hack" in this context. A few users found the article thought-provoking, even if they disagreed with its central thesis.
The post "Perfect Random Floating-Point Numbers" explores generating uniformly distributed random floating-point numbers within a specific range, addressing the subtle biases that can arise with naive approaches. It highlights how simply casting random integers to floats leads to uneven distribution and proposes a solution involving carefully constructing integers within a scaled representation of the desired floating-point range before converting them. This method ensures a true uniform distribution across the representable floating-point numbers within the specified bounds. The post also provides optimized implementations for specific floating-point formats, demonstrating a focus on efficiency.
Hacker News users discuss the practicality and nuances of generating "perfect" random floating-point numbers. Some question the value of such precision, arguing that typical applications don't require it and that the performance cost outweighs the benefits. Others delve into the mathematical intricacies, discussing the distribution of floating-point numbers and how to properly generate random values within a specific range. Several commenters highlight the importance of considering the underlying representation of floating-points and potential biases when striving for true randomness. The discussion also touches on the limitations of pseudorandom number generators and the desire for more robust solutions. One user even proposes using a library function that addresses many of these concerns.
The blog post recounts the author's experience using Lilith, a workstation specifically designed for the Modula-2 programming language in the 1980s. Fascinated by Niklaus Wirth's work, the author acquired a Lilith and found it to be a powerful and elegant machine, deeply integrated with Modula-2. The post highlights the impressive speed of the system, the innovative windowing system, and the seamless integration of the Modula-2 development environment. Despite its advantages, the Lilith's specialized nature and limited software library ultimately led to its decline, making it a fascinating footnote in computing history.
HN commenters discuss Modula-2's strengths, primarily its clarity and strong typing, which fostered maintainable code. Some fondly recall using it for various projects, including operating systems and embedded systems, praising its performance and modularity. Others compare it to Oberon and discuss Wirth's design philosophy. Several lament its lack of widespread adoption, attributing it to factors like Wirth's resistance to extensions and the rise of C++. The lack of garbage collection and the complexity of its module system are also mentioned as potential downsides. Several commenters mention Wirth's preference for simpler systems and his perceived disdain for object-oriented programming. Finally, there's some discussion of alternative historical paths and the influence Modula-2 had on later languages.
This website outlines the curriculum for a Numerical Linear Algebra course taught at the Technical University of Munich (TUM). The course focuses on practical applications of linear algebra in computer science and industrial engineering, using the Julia programming language. Topics covered include fundamental linear algebra concepts like matrix decompositions (LU, QR, SVD, Cholesky), eigenvalue problems, and least squares, alongside their computational aspects and stability analysis. The course emphasizes efficient implementation and the use of Julia packages, with a focus on large-scale problems and real-world datasets. Assignments and projects involve solving practical problems using Julia, providing hands-on experience with numerical algorithms and their performance characteristics.
Hacker News users discuss the linked Numerical Linear Algebra course taught in Julia, generally praising the choice of language. Several commenters highlight Julia's speed and readability as beneficial for teaching NLA, making the concepts easier to grasp without getting bogged down in performance optimization or complex syntax. Some appreciate the interactive nature of Julia and its ecosystem of packages like Plots.jl, making it suitable for demonstrations and visualizations. One user notes the rising adoption of Julia in scientific computing, suggesting this course reflects a broader trend. Others point out potential drawbacks, such as Julia's relative immaturity compared to established languages like MATLAB or Python, and the potential for instability in the language or its packages. However, the overall sentiment is positive, with several commenters expressing excitement about Julia's potential for education and research in numerical computation.
Stefan Karpinski's talk highlights Julia's multiple dispatch as a powerful paradigm for code organization and performance. He demonstrates how multiple dispatch allows functions to be defined for specific combinations of argument types, leading to elegant and extensible code. This allows generic algorithms to be written once and automatically applied to various data types, enabling performant specialized implementations without manual type checking. He emphasizes that this approach leads to better code readability, maintainability, and composability compared to single-dispatch or other approaches like visitor patterns, showcasing examples with various algorithms and data structures. Ultimately, Karpinski argues that multiple dispatch contributes significantly to Julia's effectiveness in scientific computing and general-purpose programming.
HN users largely praise Julia's multiple dispatch system, highlighting its elegance and power for code organization and performance. Several commenters share their positive experiences using it for tasks involving different data types or algorithms, contrasting it favorably with single-dispatch object-oriented approaches. Some discuss the potential learning curve, but emphasize the long-term benefits. A few comments delve into the technical details of how Julia implements multiple dispatch efficiently. The overall sentiment expresses appreciation for how multiple dispatch simplifies complex code and contributes to Julia's effectiveness in scientific computing.
The blog post explains closures in Tcl, highlighting their late binding behavior. Unlike languages with lexical scoping, Tcl's closures capture variable names, not values. When the closure is executed, it looks up the current value of those names in the calling context. This can lead to unexpected behavior if the environment has changed since the closure's creation. The post demonstrates this with examples and then introduces apply and lmap, which offer lexical scoping through argument binding, ensuring the closure receives the intended values regardless of the calling environment's state. Finally, it touches on using upvar and namespaces to manage variables within closures for more controlled behavior when explicit late binding is desired.
HN commenters discuss the surprising power and flexibility of Tcl's closures, despite its reputation for being simplistic. Several highlight the elegance of Tcl's approach, contrasting it with more complex implementations in other languages. Some commenters reminisce about past experiences using Tcl, while others express renewed interest in exploring its capabilities. The conciseness and expressiveness of the closure syntax, combined with Tcl's overall minimalist design, are frequently praised. A few comments also touch upon the broader topic of language design and the trade-offs between simplicity and feature richness.
Codd's cellular automaton is a self-replicating cellular automaton designed by Edgar F. Codd as a simplified version of von Neumann's universal constructor. Using an 8-state rule set on a square grid, it's capable of universal computation and self-replication, demonstrating that a relatively simple set of rules can give rise to complex behavior. The automaton's "organisms" consist of a looped instruction tape controlling a constructing arm, allowing it to copy its own tape and construct new offspring. While more complex than Conway's Game of Life, Codd's automaton is significantly simpler than von Neumann's original design, achieving self-replication with fewer states and a less intricate structure.
HN users discuss Codd's self-replicating cellular automaton, primarily focusing on its historical significance in the development of artificial life and its relationship to von Neumann's earlier, more complex self-replicating automaton. Several commenters highlight Codd's simplification of von Neumann's design, achieving self-replication with fewer states and a simpler rule set. Some discuss the implications for the origins of life and the potential for emergent complexity from simple rules. One commenter notes the connection to Conway's Game of Life, which further simplified these concepts and gained wider popularity. Others mention practical applications and the use of Codd's automaton in research. A few express interest in exploring implementations and variations of the automaton.
Performance optimization is difficult because it requires a deep understanding of the entire system, from hardware to software. It's not just about writing faster code; it's about understanding how different components interact, identifying bottlenecks, and carefully measuring the impact of changes. Optimization often involves trade-offs between various factors like speed, memory usage, code complexity, and maintainability. Furthermore, modern systems are incredibly complex, with multiple layers of abstraction and intricate dependencies, making pinpointing performance issues and crafting effective solutions a challenging and iterative process. This requires specialized tools, meticulous profiling, and a willingness to experiment and potentially rewrite significant portions of the codebase.
Hacker News users generally agreed with the article's premise that performance optimization is difficult. Several commenters highlighted the importance of profiling before optimizing, emphasizing that guesses are often wrong. The complexity of modern hardware and software, particularly caching and multi-threading, was cited as a major contributor to the difficulty. Some pointed out the value of simple code, which is often faster by default and easier to optimize if necessary. One commenter noted that focusing on algorithmic improvements usually yields better returns than micro-optimizations. Another suggested premature optimization can be detrimental to the overall project, emphasizing the importance of starting with simpler solutions. Finally, there's a short thread discussing whether certain languages are inherently faster or slower, suggesting performance ultimately depends more on the developer than the tools.
"Compiler Reminders" serves as a concise cheat sheet for compiler development, particularly focusing on parsing and lexing. It covers key concepts like regular expressions, context-free grammars, and popular parsing techniques including recursive descent, LL(1), LR(1), and operator precedence. The post briefly explains each concept and provides simple examples, offering a quick refresher or introduction to the core components of compiler construction. It also touches upon abstract syntax trees (ASTs) and their role in representing parsed code. The post is meant as a handy reference for common compiler-related terminology and techniques, not a comprehensive guide.
HN users largely praised the article for its clear and concise explanations of compiler optimizations. Several commenters shared anecdotes of encountering similar optimization-related bugs, highlighting the practical importance of understanding these concepts. Some discussed specific compiler behaviors and corner cases, including the impact of volatile keyword and undefined behavior. A few users mentioned related tools and resources, like Compiler Explorer and Matt Godbolt's talks. The overall sentiment was positive, with many finding the article a valuable refresher or introduction to compiler optimizations.
Learn-C.org offers a free, interactive C tutorial directly in your web browser. It provides a comprehensive learning path, starting with the basics of C syntax and progressing through more complex topics like pointers, memory management, and data structures. The platform features a built-in code editor and compiler, allowing users to write, run, and test their C code in real-time without needing to install any local development environment. This hands-on approach aims to make learning C more accessible and engaging for beginners.
HN users generally praised the interactive C tutorial for its accessibility and ease of use. Several commenters appreciated the browser-based nature, eliminating the need for local setup. Some highlighted the value of instant feedback and the clear explanations, making it beneficial for beginners. A few mentioned existing interactive C resources like "Programming in C" by Stephen Kochan and online compilers, comparing them to this new tutorial. One user suggested potential improvements, such as incorporating exercises and quizzes. Overall, the sentiment was positive, viewing it as a helpful tool for learning C.
The 2025 SIGBOVIK conference proceedings showcase a collection of humorous and technically creative papers exploring unconventional and often absurd aspects of computer science. Topics range from generating Shakespearean insults with machine learning to developing a self-destructing paper airplane protocol, and analyzing the computational complexity of stacking chairs. The papers, presented with a veneer of academic rigor, embrace playful exploration of impractical ideas, highlighting the lighter side of research and the joy of creative problem-solving. While the research itself is not meant to be taken seriously, the underlying technical skills and cleverness demonstrated throughout the proceedings are genuinely impressive.
HN users generally expressed amusement and appreciation for the SIGBOVIK conference and its tradition of humorous, yet technically interesting, papers. Several commenters highlighted specific papers that caught their attention, including one about generating cooking recipes from code and another exploring the potential of AI-generated sea shanties. The absurdity of a paper analyzing the "metadata" of cave paintings also drew positive remarks. Some users reflected on the conference's history and the consistent quality of its satirical contributions to computer science. There was also a brief discussion about the challenges of discerning genuine AI-generated text from human-written parody.
This blog post breaks down the typical architecture of a SQL database engine. It outlines the journey of a SQL query from initial parsing and validation, through query planning and optimization, to execution and finally, result retrieval. Key internal components discussed include the parser, validator, optimizer (utilizing cost-based optimization and heuristics), the execution engine (leveraging techniques like vectorized execution), and the storage engine responsible for data persistence and retrieval. The post emphasizes the complexity involved in processing SQL queries efficiently and the importance of each component in achieving optimal performance. It also highlights the role of indexes, transactions (including concurrency control mechanisms), and logging for data integrity and durability.
Hacker News users generally praised the DoltHub blog post for its clear and accessible explanation of SQL engine internals. Several commenters highlighted the value of the post for newcomers to databases, while others with more experience appreciated the refresher and the way it broke down complex concepts. Some discussion focused on the specific choices made in the example engine described, such as the use of a simple hash index and the lack of query optimization, with users pointing out potential improvements and alternative approaches. A few comments also touched on the broader database landscape, comparing the simplified engine to more sophisticated systems and discussing the tradeoffs involved in different design decisions.
The University of Waterloo is withholding the results of its annual Canadian Computing Competition (CCC) due to suspected widespread cheating using AI. Hundreds of students, primarily from outside Canada, are under investigation for potentially submitting solutions generated by artificial intelligence. The university is developing new detection methods and considering disciplinary actions, including disqualification and potential bans from future competitions. This incident underscores the growing challenge of academic integrity in the age of readily available AI coding tools.
Hacker News commenters discuss the implications of AI use in coding competitions, with many expressing concern about fairness and the future of such events. Some suggest that competition organizers need to adapt, proposing proctored environments or focusing on problem-solving skills harder for AI to replicate. Others debate the efficacy of current plagiarism detection methods and whether they can keep up with evolving AI capabilities. Several commenters note the irony of computer science students using AI, highlighting the difficulty in drawing the line between utilizing tools and outright cheating. Some dismiss the incident as unsurprising given the accessibility of AI tools, while others are more pessimistic about the integrity of competitive programming going forward. There's also discussion about the potential for AI to be a legitimate learning tool and how education might need to adapt to its increasing prevalence.
ArXiv, the preprint server that revolutionized scientific communication, faces challenges in maintaining its relevance and functionality amidst exponential growth. While its open-access model democratized knowledge sharing, it now grapples with scaling its infrastructure, managing the deluge of submissions, and ensuring quality control without stifling innovation. The article explores ArXiv's history, highlighting its humble beginnings and its current struggles with limited resources and a volunteer-driven moderation system. Ultimately, ArXiv must navigate the complexities of evolving scientific practices and adapt its systems to ensure it continues to serve as a vital tool for scientific progress.
Hacker News users discuss ArXiv's impact and challenges. Several commenters praise its role in democratizing scientific communication and accelerating research dissemination. Some express concern over the lack of peer review, leading to the spread of unverified or low-quality work, while acknowledging the tradeoff with speed and accessibility. The increasing volume of submissions is mentioned as a growing problem, making it harder to find relevant papers. A few users suggest potential improvements, such as enhanced search functionality and community-driven filtering or rating systems. Others highlight the importance of ArXiv's role as a preprint server, emphasizing that proper peer review still happens at the journal level. The lack of funding and the difficulty of maintaining such a crucial service are also discussed.
The Halting Problem is frequently cited as an example of an NP-hard problem, but this is misleading. While both are "hard" problems, the nature of their difficulty is fundamentally different. NP-hard problems deal with the difficulty of finding a solution among a vast number of possibilities, where verifying a given solution is relatively easy. The Halting Problem, however, is about the impossibility of determining whether a program will even finish, regardless of how long we wait. This undecidability is a stronger statement than NP-hardness, as it asserts that no algorithm can solve the problem for all inputs, not just that efficient algorithms are unknown. Using the Halting Problem to introduce NP-hardness confuses computational complexity (how long a problem takes to solve) with computability (whether a problem can even be solved). A better introductory example would be something like the Traveling Salesperson Problem, which highlights the search for an optimal solution within a large, but finite, search space.
HN commenters largely agree with the author's premise that the halting problem is a poor example for explaining NP-hardness. Many point out that the halting problem is about undecidability, a distinct concept from computational complexity which NP-hardness addresses. Some suggest better examples for illustrating NP-hardness, such as the traveling salesman problem or SAT. A few commenters argue that the halting problem is a valid, albeit confusing, example because all NP-hard problems can be reduced to it. However, this view is in the minority, with most agreeing that the difference between undecidability and intractability should be emphasized when teaching these concepts. One commenter clarifies the author's critique: it's not that the halting problem isn't NP-hard, but rather that its undecidability overshadows its NP-hardness, making it a pedagogically poor example. Another thread discusses the nuances of Turing completeness in relation to the discussion.
The BitNet b1.58 technical report details a novel approach to data transmission over existing twisted-pair cabling, aiming to significantly increase bandwidth while maintaining compatibility with legacy Ethernet. It introduces 2B4T line coding, which transmits two bits of data using four ternary symbols, enabling a theoretical bandwidth of 1.58 Gbps over Cat5e and 6a cabling. The report outlines the 2B4T encoding scheme, discusses the implementation details of the physical layer transceiver, including equalization and clock recovery, and presents experimental results validating the claimed performance improvements in terms of data rate and reach. The authors demonstrate successful transmission at the target 1.58 Gbps over 100 meters of Cat6a cable, concluding that BitNet b1.58 offers a compelling alternative to existing solutions for higher-bandwidth networking on installed infrastructure.
HN users discuss BitNet, a new Ethernet PHY aiming for 1.58 Tbps over existing cabling. Several express skepticism that it's achievable, citing potential issues with signal integrity, power consumption, and the complexity of DSP required. One commenter highlights the lack of information on FEC and its overhead. Others compare it to previous ambitious, ultimately unsuccessful, high-speed Ethernet projects. Some are cautiously optimistic, acknowledging the significant technical hurdles while expressing interest in seeing further development and independent verification. The limited real-world applicability with current switch ASIC capabilities is also noted. Overall, the sentiment leans towards cautious skepticism, tempered by curiosity about the technical details and potential future advancements.
"Hacktical C" is a free, online guide to the C programming language aimed at aspiring security researchers and exploit developers. It covers fundamental C concepts like data types, control flow, and memory management, but with a specific focus on how these concepts are relevant to low-level programming and exploitation techniques. The guide emphasizes practical application, featuring numerous code examples and exercises demonstrating buffer overflows, format string vulnerabilities, and other common security flaws. It also delves into topics like interacting with the operating system, working with assembly language, and reverse engineering, all within the context of utilizing C for offensive security purposes.
Hacker News users largely praised "Hacktical C" for its clear writing style and focus on practical application, particularly for those interested in systems programming and security. Several commenters appreciated the author's approach of explaining concepts through real-world examples, like crafting shellcode and exploiting vulnerabilities. Some highlighted the book's coverage of lesser-known C features and quirks, making it valuable even for experienced programmers. A few pointed out potential improvements, such as adding more exercises or expanding on certain topics. Overall, the sentiment was positive, with many recommending the book for anyone looking to deepen their understanding of C and its use in low-level programming.
Fibonacci hashing offers a faster alternative to the typical modulo operator (%) for distributing items into hash tables, especially when the table size is a power of two. It leverages the golden ratio's properties by multiplying the hash key by a large constant derived from the golden ratio and then bit-shifting the result, effectively achieving a modulo operation without the expensive division. This method produces a more even distribution compared to modulo with prime table sizes, particularly when dealing with keys exhibiting sequential patterns, thus reducing collisions and improving performance. While theoretically superior, its benefits may be negligible in modern systems due to compiler optimizations and branch prediction for modulo with powers of two.
HN commenters generally praise the article for clearly explaining Fibonacci hashing and its benefits over modulo. Some point out that the technique is not forgotten, being used in game development and hash table implementations within popular languages like Java. A few commenters discuss the nuances of the golden ratio's properties and its suitability for hashing, with one noting the importance of good hash functions over minor speed differences in the hashing algorithm itself. Others shared alternative hashing methods like "Multiply-with-carry" and "SplitMix64", along with links to resources on hash table performance testing. A recurring theme is that Fibonacci hashing shines with power-of-two table sizes, losing its advantages (and potentially becoming worse) with prime table sizes.
This blog post explores the architecture and evolution of Darwin, Apple's open-source operating system foundation, and its XNU kernel. It explains how Darwin, built upon the Mach microkernel, incorporates components from BSD and Apple's own I/O Kit. The post details the hybrid kernel approach of XNU, combining the message-passing benefits of a microkernel with the performance advantages of a monolithic kernel. It discusses key XNU subsystems like the process manager, memory manager, file system, and networking stack, highlighting the interplay between Mach and BSD layers. The post also traces Darwin's history, from its NeXTSTEP origins through its evolution into macOS, iOS, watchOS, and tvOS, emphasizing the platform's adaptability and performance.
Hacker News users generally praised the article for its clarity and depth in explaining a complex topic. Several commenters with kernel development experience validated the information presented, noting its accuracy and helpfulness for understanding the evolution of XNU. Some discussion arose around specific architectural choices made by Apple, including the Mach microkernel and its interaction with the BSD environment. One commenter highlighted the performance benefits of the hybrid kernel approach, while others expressed interest in the challenges of maintaining such a system. A few users also pointed out areas where the article could be expanded, such as delving further into I/O Kit details and exploring the security implications of the XNU architecture.
"Understanding Machine Learning: From Theory to Algorithms" provides a comprehensive overview of machine learning, bridging the gap between theoretical principles and practical applications. The book covers a wide range of topics, from basic concepts like supervised and unsupervised learning to advanced techniques like Support Vector Machines, boosting, and dimensionality reduction. It emphasizes the theoretical foundations, including statistical learning theory and PAC learning, to provide a deep understanding of why and when different algorithms work. Practical aspects are also addressed through the presentation of efficient algorithms and their implementation considerations. The book aims to equip readers with the necessary tools to both analyze existing learning algorithms and design new ones.
HN users largely praised Shai Shalev-Shwartz and Shai Ben-David's "Understanding Machine Learning" as a highly accessible and comprehensive introduction to the field. Commenters highlighted the book's clear explanations of fundamental concepts, its rigorous yet approachable mathematical treatment, and the helpful inclusion of exercises. Several pointed out its value for both beginners and those with prior ML experience seeking a deeper theoretical understanding. Some compared it favorably to other popular ML resources, noting its superior balance between theory and practice. A few commenters also shared specific chapters or sections they found particularly insightful, such as the treatment of PAC learning and the VC dimension. There was a brief discussion on the book's coverage (or lack thereof) of certain advanced topics like deep learning, but the overall sentiment remained strongly positive.
This book, "Introduction to System Programming in Linux," offers a practical, project-based approach to learning low-level Linux programming. It covers essential concepts like process management, memory allocation, inter-process communication (using pipes, message queues, and shared memory), file I/O, and multithreading. The book emphasizes hands-on learning through coding examples and projects, guiding readers in building their own mini-shell, a multithreaded web server, and a key-value store. It aims to provide a solid foundation for developing system software, embedded systems, and performance-sensitive applications on Linux.
Hacker News users discuss the value of the "Introduction to System Programming in Linux" book, particularly for beginners. Some commenters highlight the importance of Kay Robbins and Dave Robbins' previous work, expressing excitement for this new release. Others debate the book's relevance given the wealth of free online resources, although some counter that a well-structured book can be more valuable than scattered web tutorials. Several commenters express interest in seeing more practical examples and projects within the book, particularly those focusing on modern systems and real-world applications. Finally, there's a brief discussion about alternative learning resources, including the Linux Programming Interface and Beej's Guide.
The paper "File Systems Unfit as Distributed Storage Back Ends" argues that relying on traditional file systems for distributed storage systems leads to significant performance and scalability bottlenecks. It identifies fundamental limitations in file systems' metadata management, consistency models, and single points of failure, particularly in large-scale deployments. The authors propose that purpose-built storage systems designed with distributed principles from the ground up, rather than layered on top of existing file systems, are necessary for achieving optimal performance and reliability in modern cloud environments. They highlight how issues like metadata scalability, consistency guarantees, and failure handling are better addressed by specialized distributed storage architectures.
HN commenters generally agree with the paper's premise that traditional file systems are poorly suited for distributed storage backends. Several highlighted the impedance mismatch between POSIX semantics and distributed systems, citing issues with consistency, metadata management, and performance bottlenecks. Some questioned the novelty of the paper's findings, arguing these limitations are well-known. Others discussed alternative approaches like object storage and databases, emphasizing the importance of choosing the right tool for the job. A few commenters offered anecdotal experiences supporting the paper's claims, while others debated the practicality of replacing existing file system-based infrastructure. One compelling comment suggested that the paper's true contribution lies in quantifying the performance overhead, rather than merely identifying the issues. Another interesting discussion revolved around whether "cloud-native" storage solutions truly address these problems or merely abstract them away.
Summary of Comments ( 12 )
https://news.ycombinator.com/item?id=44106842
HN users discussed the practicality and limitations of outcome-driven reinforcement learning (RL) as presented in the linked paper. Some questioned the feasibility of specifying desired outcomes comprehensively enough for complex real-world scenarios, while others pointed out that defining outcomes might be easier than engineering reward functions in certain applications. The reliance on language models to interpret outcomes was also debated, with concerns raised about their potential biases and limitations. Several commenters expressed interest in seeing the method applied to robotics and real-world control problems, acknowledging the theoretical nature of the current work. The overall sentiment was one of cautious optimism, acknowledging the novelty of the approach but also recognizing the significant hurdles to practical implementation.
The Hacker News post titled "Outcome-Based Reinforcement Learning to Predict the Future," linking to the arXiv paper "Outcome-Based Reinforcement Learning to Predict the Future," has generated a modest discussion with several insightful comments.
One commenter points out a crucial distinction between predicting the future and influencing it. They argue that the title is misleading, as the paper focuses on training an agent to achieve desired outcomes, not necessarily to accurately predict the future in a general sense. The commenter emphasizes that the method described doesn't involve building a world model, but rather learning a policy that maximizes the likelihood of reaching a specific goal. This comment highlights the nuance between outcome-driven behavior and predictive modeling.
Another commenter builds on this idea, suggesting that the approach described in the paper is more akin to planning than prediction. They explain that the agent learns to take actions that lead to the desired outcome, without necessarily needing to form an explicit prediction of the future state of the world. This comment further clarifies the distinction between predicting and acting strategically.
A third comment raises a practical concern regarding the computational cost of the proposed method. The commenter questions the scalability of the approach, particularly in complex environments where evaluating the potential impact of actions can be computationally intensive. This comment brings a practical perspective to the theoretical discussion, highlighting the challenges of real-world application.
Finally, one commenter expresses skepticism about the novelty of the approach, suggesting that it closely resembles existing reinforcement learning methods. They argue that the paper's contribution is primarily in framing the problem in a specific way, rather than introducing fundamentally new algorithms or techniques. This comment adds a critical lens to the discussion, urging a cautious evaluation of the paper's claims.
In summary, the comments on Hacker News offer a valuable critique and contextualization of the research presented in the linked arXiv paper. They highlight the importance of differentiating between prediction and control, raise practical concerns about scalability, and question the degree of novelty introduced by the proposed approach. The discussion provides a nuanced perspective on the paper's contribution to the field of reinforcement learning.