The blog post details a formal verification of the standard long division algorithm using the Dafny programming language and its built-in Hoare logic capabilities. It walks through the challenges of representing and reasoning about the algorithm within this formal system, including defining loop invariants and handling edge cases like division by zero. The core difficulty lies in proving that the quotient and remainder produced by the algorithm are indeed correct according to the mathematical definition of division. The author meticulously constructs the necessary pre- and post-conditions, and elaborates on the specific insights and techniques required to guide the verifier to a successful proof. Ultimately, the post demonstrates the power of formal methods to rigorously verify even relatively simple, yet subtly complex, algorithms.
The paper "The FFT Strikes Back: An Efficient Alternative to Self-Attention" proposes using Fast Fourier Transforms (FFTs) as a more efficient alternative to self-attention mechanisms in Transformer models. It introduces a novel architecture called the Fast Fourier Transformer (FFT), which leverages the inherent ability of FFTs to capture global dependencies within sequences, similar to self-attention, but with significantly reduced computational complexity. Specifically, the FFT Transformer achieves linear complexity (O(n log n)) compared to the quadratic complexity (O(n^2)) of standard self-attention. The paper demonstrates that the FFT Transformer achieves comparable or even superior performance to traditional Transformers on various tasks including language modeling and machine translation, while offering substantial improvements in training speed and memory efficiency.
Hacker News users discussed the potential of the Fast Fourier Transform (FFT) as a more efficient alternative to self-attention mechanisms. Some expressed excitement about the approach, highlighting its lower computational complexity and potential to scale to longer sequences. Skepticism was also present, with commenters questioning the practical applicability given the constraints imposed by the theoretical framework and the need for further empirical validation on real-world datasets. Several users pointed out that the reliance on circular convolution inherent in FFTs might limit its ability to capture long-range dependencies as effectively as attention. Others questioned whether the performance gains would hold up on complex tasks and datasets, particularly in domains like natural language processing where self-attention has proven successful. There was also discussion around the specific architectural choices and hyperparameters, with some users suggesting modifications and further avenues for exploration.
Without TCP or UDP, internet communication as we know it would cease to function. Applications wouldn't have standardized ways to send and receive data over IP. We'd lose reliability (guaranteed delivery, in-order packets) provided by TCP, and the speed and simplicity offered by UDP. Developers would have to implement custom protocols for each application, leading to immense complexity, incompatibility, and a much less efficient and robust internet. Essentially, we'd regress to a pre-internet state for networked applications, with ad-hoc solutions and significantly reduced interoperability.
Hacker News users discussed alternatives to TCP/UDP and the implications of not using them. Some highlighted the potential of QUIC and HTTP/3 as successors, emphasizing their improved performance and reliability features. Others explored lower-level protocols like SCTP as a possible replacement, noting its multi-streaming capabilities and potential for specific applications. A few commenters pointed out that TCP/UDP abstraction is already somewhat eroded in certain contexts like RDMA, where applications can interact more directly with the network hardware. The practicality of replacing such fundamental protocols was questioned, with some suggesting it would be a massive undertaking with limited benefits for most use cases. The discussion also touched upon the roles of the network layer and the possibility of protocols built directly on IP, acknowledging potential issues with fragmentation and reliability.
The Simons Institute for the Theory of Computing at UC Berkeley has launched "Stone Soup AI," a year-long research program focused on collaborative, open, and decentralized development of foundation models. Inspired by the folktale, the project aims to build a large language model collectively, using contributions of data, compute, and expertise from diverse participants. This open-source approach intends to democratize access to powerful AI technology and foster greater transparency and community ownership, contrasting with the current trend of closed, proprietary models developed by large corporations. The program will involve workshops, collaborative coding sprints, and public releases of data and models, promoting open science and community-driven advancement in AI.
HN commenters discuss the "Stone Soup AI" concept, which involves prompting LLMs with incomplete information and relying on their ability to hallucinate missing details to produce a workable output. Some express skepticism about relying on hallucinations, preferring more deliberate methods like retrieval augmentation. Others see potential, especially for creative tasks where unexpected outputs are desirable. The discussion also touches on the inherent tendency of LLMs to confabulate and the need for careful evaluation of results. Several commenters draw parallels to existing techniques like prompt engineering and chain-of-thought prompting, suggesting "Stone Soup AI" might be a rebranding of familiar concepts. A compelling point raised is the potential for bias amplification if hallucinations consistently fill gaps with stereotypical or inaccurate information.
The paper "Is this the simplest (and most surprising) sorting algorithm ever?" introduces the "Sleep Sort" algorithm, a conceptually simple, albeit impractical, sorting method. It relies on spawning a separate thread for each element to be sorted. Each thread sleeps for a duration proportional to the element's value and then outputs the element. Thus, smaller elements are outputted first, resulting in a sorted sequence. While intriguing in its simplicity, Sleep Sort's correctness depends on precise timing and suffers from significant limitations, including poor performance for large datasets, inability to handle negative or duplicate values directly, and reliance on system-specific thread scheduling. Its main contribution is as a thought-provoking curiosity rather than a practical sorting algorithm.
Hacker News users discuss the "Mirror Sort" algorithm, expressing skepticism about its novelty and practicality. Several commenters point out prior art, referencing similar algorithms like "Odd-Even Sort" and existing work on sorting networks. There's debate about the algorithm's true complexity, with some arguing the reliance on median-finding hides significant cost. Others question the value of minimizing comparisons when other operations, like swaps or data movement, dominate the performance in real-world scenarios. The overall sentiment leans towards viewing "Mirror Sort" as an interesting theoretical exercise rather than a practical breakthrough. A few users note its potential educational value for understanding sorting network concepts.
Sublinear time algorithms provide a way to glean meaningful information from massive datasets too large to examine fully. They achieve this by cleverly sampling or querying only small portions of the input, allowing for approximate solutions or property verification in significantly less time than traditional algorithms. These techniques are crucial for handling today's ever-growing data, enabling applications like quickly estimating the average value of elements in a database or checking if a graph is connected without examining every edge. Sublinear algorithms often rely on randomization and probabilistic guarantees, accepting a small chance of error in exchange for drastically improved efficiency. They are a vital tool in areas like graph algorithms, statistics, and database management.
Hacker News users discuss the linked resource on sublinear time algorithms, primarily focusing on its practical applications. Several commenters express surprise and interest in the concept of algorithms that don't require reading all input data, with examples like property testing and finding the median element cited. Some question the real-world usefulness, while others point to applications in big data analysis, databases, and machine learning where processing the entire dataset is infeasible. There's also discussion about the trade-offs between accuracy and speed, with some suggesting these algorithms provide "good enough" solutions for certain problems. Finally, a few comments highlight specific sublinear algorithms and their associated use cases, further emphasizing the practicality of the subject.
People with the last name "Null" face a constant barrage of computer-related problems because their name is a reserved term in programming, often signifying the absence of a value. This leads to errors on websites, databases, and various forms, frequently rejecting their name or causing transactions to fail. From travel bookings to insurance applications and even setting up utilities, their perfectly valid surname is misinterpreted by systems as missing information or an error, forcing them to resort to workarounds like using a middle name or initial to navigate the digital world. This highlights the challenge of reconciling real-world data with the rigid structure of computer systems and the often-overlooked consequences for those whose names conflict with programming conventions.
HN users discuss the wide range of issues caused by the last name "Null," a reserved keyword in many computer systems. Many shared similar experiences with problematic names, highlighting the challenges faced by those with names containing spaces, apostrophes, hyphens, or characters outside the standard ASCII set. Some commenters suggested technical solutions like escaping or encoding these names, while others pointed out the persistent nature of the problem due to legacy systems and poor coding practices. The lack of proper input validation was frequently cited as the root cause, with one user mentioning that SQL injection vulnerabilities often stem from similar issues. There's also discussion about the historical context of these limitations and the responsibility of developers to handle edge cases like these. A few users mentioned the ironic humor in a computer scientist having this particular surname, especially given its significance in programming.
Hillel Wayne's post dissects the concept of "nondeterminism" in computer science, arguing that it's often used ambiguously and encompasses five distinct meanings. These are: 1) Implementation-defined behavior, where the language standard allows for varied outcomes. 2) Unspecified behavior, similar to implementation-defined but offering even less predictability. 3) Error/undefined behavior, where anything could happen, often leading to crashes. 4) Heisenbugs, which are bugs whose behavior changes under observation (e.g., debugging). 5) True nondeterminism, exemplified by hardware randomness or concurrency races. The post emphasizes that these are fundamentally different concepts with distinct implications for programmers, and understanding these nuances is crucial for writing robust and predictable software.
Hacker News users discussed various aspects of nondeterminism in the context of Hillel Wayne's article. Several commenters highlighted the distinction between predictable and unpredictable nondeterminism, with some arguing the author's categorization conflated the two. The importance of distinguishing between sources of nondeterminism, such as hardware, OS scheduling, and program logic, was emphasized. One commenter pointed out the difficulty in achieving true determinism even with seemingly simple programs due to factors like garbage collection and just-in-time compilation. The practical challenges of debugging nondeterministic systems were also mentioned, along with the value of tools that can help reproduce and analyze nondeterministic behavior. A few comments delved into specific types of nondeterminism, like data races and the nuances of concurrency, while others questioned the usefulness of the proposed categorization in practice.
Microsoft has announced a significant advancement in quantum computing with its new Majorana-based chip, called Majorana 1. This chip represents a crucial step toward creating a topological qubit, which is theoretically more stable and less prone to errors than other qubit types. Microsoft claims to have achieved the first experimental milestone in their roadmap, demonstrating the ability to control Majorana zero modes – the building blocks of topological qubits. This breakthrough paves the way for scalable and fault-tolerant quantum computers, bringing Microsoft closer to realizing the full potential of quantum computation.
HN commenters express skepticism about Microsoft's claims of progress towards topological quantum computing. Several point out the company's history of overpromising and underdelivering in this area, referencing previous retractions of published research. Some question the lack of independent verification of their results and the ambiguity surrounding the actual performance of the Majorana chip. Others debate the practicality of topological qubits compared to other approaches, highlighting the technical challenges involved. A few commenters offer more optimistic perspectives, acknowledging the potential significance of the announcement if the claims are substantiated, but emphasizing the need for further evidence. Overall, the sentiment is cautious, with many awaiting peer-reviewed publications and independent confirmation before accepting Microsoft's claims.
Relaxed Radix Balanced Trees (RRB Trees) offer a persistent, purely functional alternative to traditional balanced tree structures. They achieve balance through a radix-based approach, grouping nodes into fixed-size "chunks" analogous to digits in a number. Unlike traditional B-trees, RRB Trees relax the requirement for full chunks at all levels except the root, improving space efficiency and simplifying update operations. This "relaxed" structure, combined with path copying for persistence, allows for efficient modifications without mutating existing data. The result is a data structure well-suited for immutable data contexts like functional programming, offering competitive performance for many common operations while maintaining structural sharing for efficient memory usage and undo/redo functionality.
Hacker News users discussed the complexity and performance characteristics of Relaxed Radix Balanced Trees (RRB Trees). Some questioned the practical benefits over existing structures like B-trees or ART trees, especially given the purported constant-time lookup touted in the article. Others pointed out that while the "relaxed" balancing might simplify implementation, it could also lead to performance degradation in certain scenarios. The discussion also touched upon the niche use cases where RRB Trees might shine, like in functional or immutable data structures due to their structural sharing properties. One commenter highlighted the lack of a formal proof for the claimed O(1) lookup complexity, expressing skepticism. Finally, the conversation drifted towards comparing RRB Trees with similar data structures and their suitability for different workloads, with some advocating for more benchmarks and real-world testing to validate the theoretical claims.
The paper "Tensor evolution" introduces a novel framework for accelerating tensor computations, particularly focusing on deep learning operations. It leverages the inherent recurrence structures present in many tensor operations, expressing them as tensor recurrence equations (TREs). By representing these operations with TREs, the framework enables optimized code generation that exploits data reuse and minimizes memory accesses. This leads to significant performance improvements compared to traditional implementations, especially for large tensors and complex operations like convolutions and matrix multiplications. The framework offers automated transformation and optimization of TREs, allowing users to express tensor computations at a high level of abstraction while achieving near-optimal performance. Ultimately, tensor evolution aims to simplify and accelerate the development and deployment of high-performance tensor computations across diverse hardware architectures.
Hacker News users discuss the potential performance benefits of tensor evolution, expressing interest in seeing benchmarks against established libraries like PyTorch. Some question the novelty, suggesting the technique resembles existing dynamic programming approaches for tensor computations. Others highlight the complexity of implementing such a system, particularly the challenge of automatically generating efficient code for diverse hardware. Several commenters point out the paper's focus on solving recurrences with tensors, which could be useful for specific applications but may not be a general-purpose tensor computation framework. A desire for clarity on the practical implications and broader applicability of the method is a recurring theme.
Catalytic computing, a new theoretical framework, aims to overcome the limitations of traditional computing by leveraging the entire storage capacity of a device, such as a hard drive, for computation. Instead of relying on limited working memory, catalytic computing treats the entire memory system as a catalyst, allowing data to transform itself through local interactions within the storage itself. This approach, inspired by chemical catalysts, could drastically expand the complexity and scale of computations possible, potentially enabling the efficient processing of massive datasets that are currently intractable for conventional computers. While still theoretical, catalytic computing represents a fundamental shift in thinking about computation, promising to unlock the untapped potential of existing hardware.
Hacker News users discussed the potential and limitations of catalytic computing. Some expressed skepticism about the practicality and scalability of the approach, questioning the overhead and energy costs involved in repeatedly reading and writing data. Others highlighted the potential benefits, particularly for applications involving massive datasets that don't fit in RAM, drawing parallels to memory mapping and virtual memory. Several commenters pointed out that the concept isn't entirely new, referencing existing techniques like using SSDs as swap space or leveraging database indexing. The discussion also touched upon the specific use cases where catalytic computing might be advantageous, like bioinformatics and large language models, while acknowledging the need for further research and development to overcome current limitations. A few commenters also delved into the theoretical underpinnings of the concept, comparing it to other computational models.
The post "XOR" explores the remarkable versatility of the exclusive-or (XOR) operation in computer programming. It highlights XOR's utility in a variety of contexts, from cryptography (simple ciphers) and data manipulation (swapping variables without temporary storage) to graphics programming (drawing lines and circles) and error detection (parity checks). The author emphasizes XOR's fundamental mathematical properties, like its self-inverting nature (A XOR B XOR B = A) and commutativity, demonstrating how these properties enable elegant and efficient solutions to seemingly complex problems. Ultimately, the post advocates for a deeper appreciation of XOR as a powerful tool in any programmer's arsenal.
HN users discuss various applications and interpretations of XOR. Some highlight its reversibility and use in cryptography, while others explain its role in parity checks and error detection. A few comments delve into its connection with addition and subtraction in binary arithmetic. The thread also explores the efficiency of XOR in comparison to other bitwise operations and its utility in situations requiring toggling, such as graphics programming. Some users share personal anecdotes of using XOR for tasks like swapping variables without temporary storage. A recurring theme is the elegance and simplicity of XOR, despite its power and versatility.
A Brown University undergraduate, Noah Golowich, disproved a long-standing conjecture in data science related to the "Kadison-Singer problem." This problem, with implications for signal processing and quantum mechanics, asked about the possibility of extending certain "frame" functions while preserving their key properties. A 2013 proof showed this was possible in specific high dimensions, leading to the conjecture it was true for all higher dimensions. Golowich, building on recent mathematical tools, demonstrated a counterexample, proving the conjecture false and surprising experts in the field. His work, conducted under the mentorship of Assaf Naor, highlights the potential of exploring seemingly settled mathematical areas.
Hacker News users discussed the implications of the undergraduate's discovery, with some focusing on the surprising nature of such a significant advancement coming from an undergraduate researcher. Others questioned the practicality of the new algorithm given its computational complexity, highlighting the trade-off between statistical accuracy and computational feasibility. Several commenters also delved into the technical details of the conjecture and its proof, expressing interest in the specific mathematical techniques employed. There was also discussion regarding the potential applications of the research within various fields and the broader implications for data science and machine learning. A few users questioned the phrasing and framing in the original Quanta Magazine article, finding it slightly sensationalized.
The blog post explores the surprising observation that repeated integer addition can approximate floating-point multiplication, specifically focusing on the case of multiplying by small floating-point numbers slightly greater than one. It explains this phenomenon by demonstrating how the accumulation of fractional parts during repeated addition mimics the effect of multiplication. When adding a floating-point number slightly larger than one to itself repeatedly, the fractional part grows with each addition, eventually getting large enough to increment the integer part. This stepping increase in the integer part, combined with the accumulating fractional component, closely resembles the scaling effect of multiplication by that same number. The post illustrates this relationship using both visual representations and mathematical explanations, linking the behavior to the inherent properties of floating-point numbers and their representation in binary.
Hacker News commenters generally praised the article for clearly explaining a non-obvious relationship between integer addition and floating-point multiplication. Some highlighted the practical implications, particularly in older hardware or specialized situations where integer operations are significantly faster. One commenter pointed out the historical relevance to Quake III's fast inverse square root approximation, while another noted the connection to logarithms and how this technique could be extended to other operations. A few users discussed the limitations and boundary conditions, emphasizing the approximation's validity only within specific ranges and the importance of understanding those constraints. Some commenters provided further context by linking to related concepts like the "magic number" used in the Quake III algorithm and resources on floating-point representation.
The author expresses confusion about generational garbage collection, specifically regarding how a young generation object can hold a reference to an old generation object without the garbage collector recognizing this dependency. They believe the collector should mark the old generation object as reachable if it's referenced from a young generation object during a minor collection, preventing its deletion. The author suspects their mental model is flawed and seeks clarification on how the generational hypothesis (that most objects die young) can hold true if young objects can readily reference older ones, seemingly blurring the generational boundaries and making minor collections less efficient. They posit that perhaps write barriers play a crucial role they haven't fully grasped yet.
Hacker News users generally agreed with the author's sentiment that generational garbage collection, while often beneficial, can be a source of confusion, especially when debugging memory issues. Several commenters shared anecdotes of difficult-to-diagnose bugs related to generational GC, echoing the author's experience. Some pointed out that while generational GC is usually efficient, it doesn't eliminate all memory leaks, and can sometimes mask them, making them harder to find later. The cyclical nature of object dependencies and how they can unexpectedly keep objects alive across generations was also discussed. Others highlighted the importance of understanding how specific garbage collectors work in different languages and environments for effective debugging. A few comments offered alternative strategies to generational GC, but acknowledged the general effectiveness and prevalence of this approach.
This post explores the inherent explainability of linear programs (LPs). It argues that the optimal solution of an LP and its sensitivity to changes in constraints or objective function are readily understandable through the dual program. The dual provides shadow prices, representing the marginal value of resources, and reduced costs, indicating the improvement needed for a variable to become part of the optimal solution. These values offer direct insights into the LP's behavior. Furthermore, the post highlights the connection between the simplex algorithm and sensitivity analysis, explaining how pivoting reveals the impact of constraint adjustments on the optimal solution. Therefore, LPs are inherently explainable due to the rich information provided by duality and the simplex method's step-by-step process.
Hacker News users discussed the practicality and limitations of explainable linear programs (XLPs) as presented in the linked article. Several commenters questioned the real-world applicability of XLPs, pointing out that the constraints requiring explanations to be short and easily understandable might severely restrict the solution space and potentially lead to suboptimal or unrealistic solutions. Others debated the definition and usefulness of "explainability" itself, with some suggesting that forcing simple explanations might obscure the true complexity of a problem. The value of XLPs in specific domains like regulation and policy was also considered, with commenters noting the potential for biased or manipulated explanations. Overall, there was a degree of skepticism about the broad applicability of XLPs while acknowledging the potential value in niche applications where transparent and easily digestible explanations are paramount.
Jan Miksovsky's blog post presents a humorous screenplay introducing the fictional programming language "Slowly." The screenplay satirizes common programming language tropes, including obscure syntax, fervent community debates, and the promise of effortless productivity. It follows the journey of a programmer attempting to learn Slowly, highlighting its counterintuitive features and the resulting frustration. The narrative emphasizes the language's glacial pace and convoluted approach to simple tasks, ultimately culminating in the programmer's realization that "Slowly" is ironically named and incredibly inefficient. The post is a playful commentary on the often-complex and occasionally absurd nature of learning new programming languages.
Hacker News users generally reacted positively to the screenplay format for introducing a programming language. Several commenters praised the engaging and creative approach, finding it a refreshing change from traditional tutorials. Some suggested it could be particularly effective for beginners, making the learning process less intimidating. A few pointed out the potential for broader applications of this format to other technical subjects. There was some discussion on the specifics of the chosen language (Janet) and its suitability for introductory purposes, with some advocating for more mainstream options. The practicality of using a screenplay for a full language tutorial was also questioned, with some suggesting it might be better suited as a brief introduction or for illustrating specific concepts. A common thread was the appreciation for the author's innovative attempt to make learning programming more accessible.
The blog post "Fat Rand: How Many Lines Do You Need to Generate a Random Number?" explores the surprising complexity hidden within seemingly simple random number generation. It dissects the code behind Python's random.randint() function, revealing a multi-layered process involving system-level entropy sources, hashing, and bit manipulation to ultimately produce a seemingly simple random integer. The post highlights the extensive effort required to achieve statistically sound randomness, demonstrating that generating even a single random number relies on a significant amount of code and underlying system functionality. This complexity is necessary to ensure unpredictability and avoid biases, which are crucial for security, simulations, and various other applications.
Hacker News users discussed the surprising complexity of generating truly random numbers, agreeing with the article's premise. Some commenters highlighted the difficulty in seeding pseudo-random number generators (PRNGs) effectively, with suggestions like using /dev/random, hardware sources, or even mixing multiple sources. Others pointed out that the article focuses on uniformly distributed random numbers, and that generating other distributions introduces additional complexity. A few users mentioned specific use cases where simple PRNGs are sufficient, like games or simulations, while others emphasized the critical importance of robust randomness in cryptography and security. The discussion also touched upon the trade-offs between performance and security when choosing a random number generation method, and the value of having different "grades" of randomness for various applications.
ArXivTok presents arXiv research papers in a short-video format, aiming to make complex topics more accessible. The site leverages AI to summarize papers and generates engaging videos with visuals, voiceover narration, and background music. This allows users to quickly grasp the core ideas of a paper without needing to delve into the full text, offering a faster and potentially more engaging way to explore scientific research.
HN users generally praised ArXivTok for its accessibility, making dense academic papers more digestible. Several commenters appreciated the use of TikTok's format, highlighting its effectiveness in quickly conveying complex information. Some expressed concern over potential simplification or misrepresentation of research, but the prevailing sentiment was positive, viewing ArXivTok as a valuable tool for disseminating scientific knowledge to a wider audience and sparking curiosity. A few users suggested improvements like linking directly to the original papers and providing more context around the research being presented. There was also discussion about the broader implications of using social media platforms like TikTok for scientific communication.
Russ Cox's "Go Data Structures: Interfaces" explains how Go's interfaces are implemented efficiently. Unlike languages with vtables (virtual method tables) associated with objects, Go uses interface tables (itabs) associated with the interface itself. When an interface variable holds a concrete type, the itab links the interface's methods to the concrete type's corresponding methods. This approach allows for efficient lookups and avoids the overhead of storing method pointers within every object. Furthermore, Go supports implicit interface satisfaction, meaning types don't explicitly declare they implement an interface. This contributes to decoupled and flexible code. The article demonstrates this through examples of common data structures like stacks and sorted maps, showcasing how interfaces enable code reuse and extensibility without sacrificing performance.
HN commenters largely praise Russ Cox's clear explanation of Go's interfaces, particularly how they differ from and improve upon traditional object-oriented approaches. Several highlight the elegance and simplicity of Go's implicit interface satisfaction, contrasting it with the verbosity of explicit declarations like those in Java or C#. Some discuss the performance implications of interface calls, with one noting the potential cost of indirect calls, though another points out that Go's compiler effectively optimizes many of these. A few comments delve into more specific aspects of interface design, like the distinction between value and pointer receivers and the use of the empty interface. Overall, there's a strong sense of appreciation for the article's clarity and the design of Go's interface system.
This blog post explores methods for proving false statements within formal systems like logic and mathematics. It focuses on proof by contradiction, where you assume the statement is true and then demonstrate that this assumption leads to a logical inconsistency, thereby proving the original statement false. The post uses the example of proving the irrationality of √2, illustrating how assuming its rationality (expressibility as a fraction) ultimately contradicts the fundamental theorem of arithmetic. It highlights the importance of clearly defining the terms and axioms of the system within which the proof operates.
Hacker News users discuss the potential misuse of zero-knowledge proofs (ZKPs), expressing concern that they could be used to convincingly lie or create fraudulent attestations. Some commenters highlight the importance of distinguishing between a ZKP verifying a computation versus verifying a real-world fact. They argue that while ZKPs can prove the correct execution of a program on given inputs, they cannot inherently prove the veracity of those inputs. Others discuss the "garbage in, garbage out" principle in this context, suggesting the need for robust, real-world verification methods alongside ZKPs to prevent their misuse. The trustworthiness of the prover remains crucial, and ZKPs alone cannot bridge the gap between computation and reality. A few comments also touch upon the complexity of understanding and implementing ZKPs correctly, potentially leading to vulnerabilities.
The article details the frustrating experiences of individuals named "Null," whose names cause software glitches due to its interpretation as a null value or lack of input. From online forms rejecting their names to databases corrupting their records, people named Null face constant challenges in a digitally-driven world. They've developed workarounds, like using middle names or initialized first names, but the underlying problem highlights the inflexibility of many systems and the lack of consideration for edge cases in software development. The article emphasizes the importance of comprehensive data validation and the need for developers to anticipate diverse and unusual names to avoid inadvertently excluding or inconveniencing real people.
HN commenters largely discuss their own experiences with problematic names and data entry systems. Several share anecdotes about names with apostrophes, spaces, or titles causing issues. Some point out the irony of the article's author having a relatively common surname (Null) while claiming digital invisibility. Others discuss the technical reasons behind such issues, mentioning database design, character encoding, and validation practices. A few commenters note that the problem isn't new and express frustration with the persistent nature of these bugs. One highly upvoted comment suggests that the real issue lies with programmers who fail to properly sanitize inputs, rather than with the names themselves. There's a brief discussion of legal names versus preferred names and the challenges this presents for systems.
The blog post details methods for eliminating left and mutual recursion in context-free grammars, crucial for parser construction. Left recursion, where a non-terminal derives itself as the leftmost symbol, is problematic for top-down parsers. The post demonstrates how to remove direct left recursion using factorization and substitution. It then explains how to handle indirect left recursion by ordering non-terminals and systematically applying the direct recursion removal technique. Finally, it addresses mutual recursion, where two or more non-terminals derive each other, converting it into direct left recursion, which can then be eliminated using the previously described methods. The post uses concrete examples to illustrate these transformations, making it easier to understand the process of converting a grammar into a parser-friendly form.
Hacker News users discussed the potential inefficiency of the presented left-recursion elimination algorithm, particularly its reliance on repeated string concatenation. They suggested alternative approaches using stacks or accumulating results in a list for better performance. Some commenters questioned the necessity of fully eliminating left recursion in all cases, pointing out that modern parsing techniques, like packrat parsing, can handle left-recursive grammars directly. The lack of formal proofs or performance comparisons with established methods was also noted. A few users discussed the benefits and drawbacks of different parsing libraries and techniques, including ANTLR and various parser combinator libraries.
The blog post "The Simplicity of Prolog" argues that Prolog's declarative nature makes it easier to learn and use than imperative languages for certain problem domains. It demonstrates this by building a simple genealogy program in Prolog, highlighting how its concise syntax and built-in search mechanism naturally express relationships and deduce facts. The author contrasts this with the iterative loops and explicit state management required in imperative languages, emphasizing how Prolog abstracts away these complexities. The post concludes that while Prolog may not be suitable for all tasks, its elegant approach to logic programming offers a powerful and efficient solution for problems involving knowledge representation and inference.
Hacker News users generally praised the article for its clear introduction to Prolog, with several noting its effectiveness in sparking their own interest in the language. Some pointed out Prolog's historical significance and its continued relevance in specific domains like AI and knowledge representation. A few users highlighted the contrast between Prolog's declarative approach and the more common imperative style of programming, emphasizing the shift in mindset required to effectively use it. Others shared personal anecdotes of their experiences with Prolog, both positive and negative, with some mentioning its limitations in performance-critical applications. A couple of comments also touched on the learning curve associated with Prolog and the challenges in debugging complex programs.
A new algorithm for the "pancake sorting problem" — sorting a disordered stack by repeatedly flipping sections of it — has achieved near-optimal efficiency. While the minimal number of flips required to sort any stack remains unknown, the new algorithm, developed by researchers at MIT and other institutions, guarantees completion within 1.375 times the theoretical minimum. This represents a significant improvement over previous algorithms, edging closer to a perfect solution for a problem that has puzzled computer scientists for decades. The researchers employed a recursive strategy that breaks down large stacks into smaller, more manageable substacks, optimizing the flipping process and setting a new benchmark for pancake sorting efficiency.
Hacker News users discussed the practicality and significance of the new book-sorting algorithm. Some questioned the real-world applicability given the specialized constraints, like pre-sorted sections and a single robot arm. Others debated the definition of "perfection" in sorting, pointing out that minimizing the arm's travel distance might not be the only relevant metric. The algorithm's novelty and mathematical elegance were acknowledged, but skepticism remained about its potential impact beyond theoretical computer science. Several commenters highlighted the existing highly optimized solutions for real-world sorting problems and suggested that this new algorithm is more of an interesting theoretical exercise than a practical breakthrough. There was also discussion about the difference between this algorithm and existing techniques like Timsort, with some arguing the new algorithm addresses a distinctly different problem.
Dan Luu's "Working with Files Is Hard" explores the surprising complexity of file I/O. While seemingly simple, file operations are fraught with subtle difficulties stemming from the interplay of operating systems, filesystems, programming languages, and hardware. The post dissects various common pitfalls, including partial writes, renaming and moving files across devices, unexpected caching behaviors, and the challenges of ensuring data integrity in the face of interruptions. Ultimately, the article highlights the importance of understanding these complexities and employing robust strategies, such as atomic operations and careful error handling, to build reliable file-handling code.
HN commenters largely agree with the premise that file handling is surprisingly complex. Many shared anecdotes reinforcing the difficulties encountered with different file systems, character encodings, and path manipulation. Some highlighted the problems of hidden characters causing issues, the challenges of cross-platform compatibility (especially Windows vs. *nix), and the subtle bugs that can arise from incorrect assumptions about file sizes or atomicity. A few pointed out the relative simplicity of dealing with files in Plan 9, and others mentioned more modern approaches like using memory-mapped files or higher-level libraries to abstract away some of the complexity. The lack of libraries to handle text files reliably across platforms was a recurring theme. A top comment emphasizes how corner cases, like filenames containing newlines or other special characters, are often overlooked until they cause real-world problems.
Over 50 years in computing, the author reflects on key lessons learned. Technical brilliance isn't enough; clear communication, especially writing, is crucial for impact. Building diverse teams and valuing diverse perspectives leads to richer solutions. Mentorship is a two-way street, enriching both mentor and mentee. Finally, embracing change and continuous learning are essential for navigating the ever-evolving tech landscape, along with maintaining a sense of curiosity and playfulness in work.
HN commenters largely appreciated the author's reflections on his long career in computer science. Several highlighted the importance of his point about the cyclical nature of computer science, with older ideas and technologies often becoming relevant again. Some commenters shared their own anecdotes about witnessing this cycle firsthand, mentioning specific technologies like LISP, Smalltalk, and garbage collection. Others focused on the author's advice about the balance between specializing and maintaining broad knowledge, noting its applicability to various fields. A few also appreciated the humility and candidness of the author in acknowledging the role of luck in his success.
The blog post "The Missing Mentoring Pillar" argues that mentorship focuses too heavily on career advancement and technical skills, neglecting the crucial aspect of personal development. It proposes a third pillar of mentorship, alongside career and technical guidance, focused on helping mentees navigate the emotional and psychological challenges of their field. This includes addressing issues like imposter syndrome, handling criticism, building resilience, and managing stress. By incorporating this "personal" pillar, mentorship becomes more holistic, supporting individuals in developing not just their skills, but also their capacity to thrive in a demanding and often stressful environment. This ultimately leads to more well-rounded, resilient, and successful professionals.
HN commenters generally agree with the article's premise about the importance of explicit mentoring in open source, highlighting how difficult it can be to break into contributing. Some shared personal anecdotes of positive and negative mentoring experiences, emphasizing the impact a good mentor can have. Several suggested concrete ways to improve mentorship, such as structured programs, better documentation, and more welcoming communities. A few questioned the scalability of one-on-one mentoring and proposed alternatives like improved documentation and clearer contribution guidelines. One commenter pointed out the potential for abuse in mentor-mentee relationships, emphasizing the need for clear codes of conduct.
Martin Fowler's short post "Two Hard Things" humorously points out the inherent difficulty in software development. He argues that naming things well and cache invalidation are the two hardest problems. While seemingly simple, choosing accurate, unambiguous, and consistent names within a large codebase is a significant challenge. Similarly, knowing when to invalidate cached data to ensure accuracy without sacrificing performance is a complex problem requiring careful consideration. Essentially, both challenges highlight the intricate interplay between human comprehension and technical implementation that lies at the heart of software development.
HN commenters largely agree with Martin Fowler's assertion that naming things and cache invalidation are the two hardest problems in computer science. Some suggest other contenders, including off-by-one errors and distributed systems complexities (especially consensus). Several commenters highlight the human element in naming, emphasizing the difficulty of conveying nuance and intent, particularly across cultures and technical backgrounds. Others point out the subtle bugs that can arise from improper cache invalidation, impacting data consistency and causing difficult-to-track issues. The interplay between these two hard problems is also mentioned, as poor naming can exacerbate the difficulties of cache invalidation by making it harder to understand what data a cache key represents. A few humorous comments allude to these challenges being far less daunting than other life problems, such as raising children.
Summary of Comments ( 1 )
https://news.ycombinator.com/item?id=43185059
Hacker News users discussed the application of Hoare logic to verify long division, with several expressing appreciation for the clear explanation and visualization of the algorithm. Some commenters debated the practical benefits of formal verification for such a well-established algorithm, questioning the likelihood of uncovering unknown bugs. Others highlighted the educational value of the exercise, emphasizing the importance of understanding foundational algorithms. A few users delved into the specifics of the chosen proof method and its implications. One commenter suggested exploring alternative verification approaches, while another pointed out the potential for applying similar techniques to other arithmetic operations.
The Hacker News post "Long division verified via Hoare logic" discussing the article about verifying long division using Hoare logic sparked a small but focused conversation. Several commenters express appreciation for the clear explanation of both Hoare logic and its application to a concrete example like long division. One commenter highlights the pedagogical value of such demonstrations, suggesting it's a good way to teach people about formal verification methods. They appreciate the author's approach of starting with a simple example and gradually introducing complexities, making the concepts more accessible.
Another commenter delves into the practical implications, pondering whether such verified algorithms could find their way into real-world applications like optimizing compilers. They acknowledge the potential benefits of guaranteed correctness for critical operations like division, especially in performance-sensitive contexts.
A different user questions the choice of long division as the example, wondering if simpler algorithms might have served the illustrative purpose equally well while requiring less intricate proofs. This commenter suggests that the complexity of the long division algorithm might overshadow the core principles of Hoare logic being demonstrated.
Finally, a comment points out the historical context, mentioning Edsger W. Dijkstra's early work on formal program verification and how the article's approach aligns with Dijkstra's vision. This comment connects the present work to the foundational ideas in the field.
Overall, the comments demonstrate a positive reception of the article, praising its clarity and educational value. The discussion also touches upon practical considerations and historical context, enriching the understanding of the presented work.