Telli, a YC F24 startup building a collaborative knowledge-sharing platform akin to a shared second brain, is hiring founding engineers in Berlin, Germany. They're seeking individuals passionate about building intuitive and collaborative products using technologies like TypeScript, React, and Node.js. The ideal candidate is excited about early-stage startups, shaping product direction, and working directly with the founding team in a fast-paced, impactful environment. Relocation support is available.
The blog post introduces Query Understanding as a Service (QUaaS), a system designed to improve interactions with large language models (LLMs). It argues that directly prompting LLMs often yields suboptimal results due to ambiguity and lack of context. QUaaS addresses this by acting as a middleware layer, analyzing user queries to identify intent, extract entities, resolve ambiguities, and enrich the query with relevant context before passing it to the LLM. This enhanced query leads to more accurate and relevant LLM responses. The post uses the example of querying a knowledge base about company information, demonstrating how QUaaS can disambiguate entities and formulate more precise queries for the LLM. Ultimately, QUaaS aims to bridge the gap between natural language and the structured data that LLMs require for optimal performance.
HN users discussed the practicalities and limitations of the proposed LLM query understanding service. Some questioned the necessity of such a complex system, suggesting simpler methods like keyword extraction and traditional search might suffice for many use cases. Others pointed out potential issues with hallucinations and maintaining context across multiple queries. The value proposition of using an LLM for query understanding versus directly feeding the query to an LLM for task completion was also debated. There was skepticism about handling edge cases and the computational cost. Some commenters saw potential in specific niches, like complex legal or medical queries, while others believed the proposed architecture was over-engineered for general search.
The best programmers aren't defined by raw coding speed or esoteric language knowledge. Instead, they possess a combination of strong fundamentals, a pragmatic approach to problem-solving, and excellent communication skills. They prioritize building robust, maintainable systems over clever hacks, focusing on clarity and simplicity in their code. This allows them to effectively collaborate with others, understand the broader business context of their work, and adapt to evolving requirements. Ultimately, their effectiveness comes from a holistic understanding of software development, not just technical prowess.
HN users generally agreed with the author's premise that the best programmers are adaptable, pragmatic, and prioritize shipping working software. Several commenters emphasized the importance of communication and collaboration skills, noting that even highly technically proficient programmers can be ineffective if they can't work well with others. Some questioned the author's emphasis on speed, arguing that rushing can lead to technical debt and bugs. One highly upvoted comment suggested that "best" is subjective and depends on the specific context, pointing out that a programmer excelling in a fast-paced startup environment might struggle in a large, established company. Others shared anecdotal experiences supporting the author's points, citing examples of highly effective programmers who embodied the qualities described.
Bazel's next generation focuses on improving build performance and developer experience. Key changes include Starlark, a Python-like language for build rules offering more flexibility and maintainability, as well as a transition to a new execution phase, Skyframe v2, designed for increased parallelism and scalability. These upgrades aim to simplify complex build processes, especially for large projects, while also reducing overall build times and improving caching effectiveness through more granular dependency tracking and action invalidation. Additionally, remote execution and caching are being streamlined, further contributing to faster builds by distributing workload and reusing previously built artifacts more efficiently.
Hacker News commenters generally agree that Bazel's remote caching and execution are powerful features, offering significant build speed improvements. Several users shared positive experiences, particularly with large monorepos. Some pointed out the steep learning curve and initial setup complexity as drawbacks, with one commenter mentioning it took their team six months to fully integrate Bazel. The discussion also touched upon the benefits for dependency management and build reproducibility. A few commenters questioned Bazel's suitability for smaller projects, suggesting the overhead might outweigh the advantages. Others expressed interest in alternative build systems like BuildStream and Buck2. A recurring theme was the desire for better documentation and easier integration with various languages and platforms.
Senior developers can leverage AI coding tools effectively by focusing on high-level design, architecture, and problem-solving. Rather than being replaced, their experience becomes crucial for tasks like defining clear requirements, breaking down complex problems into smaller, AI-manageable chunks, evaluating AI-generated code for quality and security, and integrating it into larger systems. Essentially, senior developers evolve into "AI architects" who guide and refine the work of AI coding agents, ensuring alignment with project goals and best practices. This allows them to multiply their productivity and tackle more ambitious projects.
HN commenters largely discuss their experiences and opinions on using AI coding tools as senior developers. Several note the value in using these tools for boilerplate, refactoring, and exploring unfamiliar languages/libraries. Some express concern about over-reliance on AI and the potential for decreased code comprehension, particularly for junior developers who might miss crucial learning opportunities. Others emphasize the importance of prompt engineering and understanding the underlying code generated by the AI. A few comments mention the need for adaptation and new skill development in this changing landscape, highlighting code review, testing, and architectural design as increasingly important skills. There's also discussion around the potential for AI to assist with complex tasks like debugging and performance optimization, allowing developers to focus on higher-level problem-solving. Finally, some commenters debate the long-term impact of AI on the developer job market and the future of software engineering.
Type, a YC W23 startup building AI-powered writing tools, is seeking a senior software engineer. They're looking for someone with strong TypeScript/JavaScript and React experience to contribute to their core product. Ideal candidates will be passionate about building performant and user-friendly web applications and interested in working with cutting-edge AI technologies. This role offers the opportunity to significantly impact a rapidly growing startup and shape the future of writing.
Several commenters on Hacker News expressed skepticism about the job posting's emphasis on "impact" without clearly defining it, and the vague description of the product as "building tools for knowledge workers." Some questioned the high salary range ($200k-$400k) for a Series A startup, particularly given the lack of detailed information about the work itself. A few users pointed out the irony of Type using traditional job boards instead of their own purportedly superior platform for knowledge workers. Others questioned the company's focus, wondering if they were building a note-taking app or a broader platform. Overall, the comments reflect a cautious and somewhat critical view of the job posting, with many desiring more concrete details before considering applying.
Edsger Dijkstra argues against "natural language programming," believing it a foolish endeavor. He contends that natural language's inherent ambiguity and imprecision make it unsuitable for expressing the rigorous logic required in programming. Instead of striving for superficial readability through natural language, Dijkstra advocates for focusing on developing formal notations and abstractions that are clear, concise, and verifiable, even if they appear less "natural" initially. He emphasizes that programming requires a level of precision and unambiguity that natural language simply cannot provide, and attempting to bridge this gap will ultimately lead to more confusion and less reliable software.
HN commenters generally agree with Dijkstra's skepticism of "natural language programming." Some highlight the ambiguity inherent in natural language as fundamentally incompatible with the precision required for programming. Others point out the success of domain-specific languages (DSLs) as a middle ground, offering a more human-readable syntax without sacrificing clarity. One commenter suggests Dijkstra's critique is more aimed at vague specifications disguised as programs rather than genuinely well-defined natural language programming. Several commenters mention the value of formal methods and mathematical notation for clear program design, echoing Dijkstra's sentiments. A few offer historical context, suggesting the "natural language programming" Dijkstra criticized likely refers to early, overly ambitious attempts, and that modern NLP advancements might warrant revisiting the concept.
The Configuration Complexity Clock describes how configuration management evolves over time in software projects. It starts simply, with direct code modifications, then progresses to external configuration files, properties files, and eventually more complex systems like dependency injection containers. As projects grow, configurations become increasingly sophisticated, often hitting a peak of complexity with custom-built configuration systems. This complexity eventually becomes unsustainable, leading to a drive for simplification. This simplification can take various forms, such as convention over configuration, self-configuration, or even a return to simpler approaches. The cycle is then likely to repeat as the project evolves further.
HN users generally agree with the author's premise that configuration complexity grows over time, especially in larger systems. Several commenters point to specific examples of this phenomenon, such as accumulating unused configuration options and the challenges of maintaining backward compatibility. Some suggest strategies for mitigating this complexity, including using declarative configuration, version control, and rigorous testing. One highly upvoted comment highlights the importance of regularly reviewing and pruning configuration files, comparing it to cleaning out a closet. Another points out that managing complex configurations often necessitates dedicated tooling, and even the tools themselves can become complex. There's also discussion on the trade-offs between simple, limited configurations and powerful, complex ones, with some arguing that the additional complexity is sometimes justified by the flexibility it provides.
This Hacker News thread from April 2025 serves as a place for companies to post job openings and for individuals to seek employment. The original poster initiates the monthly "Who is hiring?" thread, inviting companies to share details about available positions, including location (remote or in-person), required skills, and company information. Job seekers are also encouraged to share their experience, desired roles, and location preferences. Essentially, the thread functions as an open marketplace connecting potential employers and employees within the tech community.
The Hacker News thread "Ask HN: Who is hiring? (April 2025)" is a continuation of a long-running series, and this iteration has attracted numerous comments from companies seeking talent and individuals looking for work. Many comments list specific roles and companies, often with links to job boards or application pages. Common areas of hiring include software engineering (front-end, back-end, full-stack), machine learning/AI, DevOps, and cybersecurity. Some commenters discuss the job market generally, noting desired skills or remote work opportunities. There's also a noticeable trend of AI-related roles, highlighting the continued growth in that sector. Several comments focus on specific locations, indicating a preference for certain geographic areas. Finally, some responses engage in humorous banter typical of these threads, expressing hopes for future employment or commenting on the cyclical nature of the "Who's Hiring" posts.
Extend (a YC W23 startup) is hiring engineers to build their LLM-powered document processing platform. They're looking for experienced full-stack and backend engineers proficient in Python and React to help develop core product features like data extraction, summarization, and search. The ideal candidate is excited about the potential of LLMs and eager to work in a fast-paced startup environment. Extend aims to streamline how businesses interact with documents, and they're offering competitive salary and equity for those who join their team.
Several Hacker News commenters express skepticism about the long-term viability of building a company around LLM-powered document processing, citing the rapid advancement of open-source LLMs and the potential for commoditization. Some suggest the focus should be on a very specific niche application to avoid direct competition with larger players. Other comments question the need for a dedicated tool, arguing existing solutions like GPT-4 might already be sufficient. A few commenters offer alternative application ideas, including leveraging LLMs for contract analysis or regulatory compliance. There's also a discussion around data privacy and security when processing sensitive documents with third-party tools.
The Go Optimization Guide at goperf.dev provides a practical, structured approach to optimizing Go programs. It covers the entire optimization process, from benchmarking and profiling to understanding performance characteristics and applying targeted optimizations. The guide emphasizes data-driven decisions using benchmarks and profiling tools like pprof and highlights common performance bottlenecks in areas like memory allocation, garbage collection, and inefficient algorithms. It also delves into specific techniques like using optimized data structures, minimizing allocations, and leveraging concurrency effectively. The guide isn't a simple list of tips, but rather a comprehensive resource that equips developers with the methodology and knowledge to systematically improve the performance of their Go code.
Hacker News users generally praised the Go Optimization Guide linked in the post, calling it "excellent," "well-written," and a "great resource." Several commenters highlighted the guide's practicality, appreciating the clear explanations and real-world examples demonstrating performance improvements. Some pointed out specific sections they found particularly helpful, like the advice on using sync.Pool and understanding escape analysis. A few users offered additional tips and resources related to Go performance, including links to profiling tools and blog posts. The discussion also touched on the nuances of benchmarking and the importance of considering optimization trade-offs.
The author argues that current AI agent development overemphasizes capability at the expense of reliability. They advocate for a shift in focus towards building simpler, more predictable agents that reliably perform basic tasks. While acknowledging the allure of highly capable agents, the author contends that their unpredictable nature and complex emergent behaviors make them unsuitable for real-world applications where consistent, dependable operation is paramount. They propose that a more measured, iterative approach, starting with dependable basic agents and gradually increasing complexity, will ultimately lead to more robust and trustworthy AI systems in the long run.
Hacker News users largely agreed with the article's premise, emphasizing the need for reliability over raw capability in current AI agents. Several commenters highlighted the importance of predictability and debuggability, suggesting that a focus on simpler, more understandable agents would be more beneficial in the short term. Some argued that current large language models (LLMs) are already too capable for many tasks and that reigning in their power through stricter constraints and clearer definitions of success would improve their usability. The desire for agents to admit their limitations and avoid hallucinations was also a recurring theme. A few commenters suggested that reliability concerns are inherent in probabilistic systems and offered potential solutions like improved prompt engineering and better user interfaces to manage expectations.
The post "Literate Development: AI-Enhanced Software Engineering" argues that combining natural language explanations with code, a practice called literate programming, is becoming increasingly important in the age of AI. Large language models (LLMs) can parse and understand this combination, enabling new workflows and tools that boost developer productivity. Specifically, LLMs can generate code from natural language descriptions, translate between programming languages, explain existing code, and even create documentation automatically. This shift towards literate development promises to improve code maintainability, collaboration, and overall software quality, ultimately leading to a more streamlined and efficient software development process.
Hacker News users discussed the potential of AI in software development, focusing on the "literate development" approach. Several commenters expressed skepticism about AI's current ability to truly understand code and its context, suggesting that using AI for generating boilerplate or simple tasks might be more realistic than relying on it for complex design decisions. Others highlighted the importance of clear documentation and modular code for AI tools to be effective. A common theme was the need for caution and careful evaluation before fully embracing AI-driven development, with concerns about potential inaccuracies and the risk of over-reliance on tools that may not fully grasp the nuances of software design. Some users expressed excitement about the future possibilities, while others remained pragmatic, advocating for a measured adoption of AI in the development process. Several comments also touched upon the potential benefits of AI in assisting with documentation and testing, and the idea that AI might be better suited for augmenting developers rather than replacing them entirely.
PermitFlow, a Y Combinator-backed startup streamlining the construction permitting process, is hiring Senior and Staff Software Engineers in NYC. They're looking for experienced engineers proficient in Python and Django (or similar frameworks) to build and scale their platform. Ideal candidates will have a strong product sense, experience with complex systems, and a passion for improving the construction industry. PermitFlow offers competitive salary and equity, and the opportunity to work on a high-impact product in a fast-paced environment.
HN commenters discuss PermitFlow's high offered salary range ($200k-$300k) for senior/staff engineers, with some expressing skepticism about its legitimacy or sustainability, especially for a Series A company. Others suggest the range might reflect NYC's high cost of living and competitive tech market. Several commenters note the importance of equity in addition to salary, questioning its potential at a company already valued at $80M. Some express interest in the regulatory tech space PermitFlow occupies, while others find the work potentially tedious. A few commenters point out the job posting's emphasis on "impact," a common buzzword they find vague and uninformative. The overall sentiment seems to be cautious interest mixed with pragmatic concerns about compensation and the nature of the work itself.
"Architecture Patterns with Python" introduces practical architectural patterns for structuring Python applications beyond simple scripts. It focuses on Domain-Driven Design (DDD) principles and demonstrates how to implement them alongside architectural patterns like dependency injection and the repository pattern to create well-organized, testable, and maintainable code. The book guides readers through building a realistic application, iteratively improving its architecture to handle increasing complexity and evolving requirements. It emphasizes using Python's strengths effectively while promoting best practices for software design, ultimately enabling developers to create robust and scalable applications.
Hacker News users generally expressed interest in "Architecture Patterns with Python," praising its clear writing and practical approach. Several commenters highlighted the book's focus on domain-driven design and its suitability for bridging the gap between simple scripts and complex applications. Some appreciated the free online availability, while others noted the value of supporting the authors by purchasing the book. A few users compared it favorably to other architecture resources, emphasizing its Python-specific examples. The discussion also touched on testing strategies and the balance between architecture and premature optimization. A couple of commenters pointed out the book's emphasis on using readily available tools and libraries rather than introducing new frameworks.
The author argues against the common practice of on-call rotations, particularly as implemented by many tech companies. They contend that being constantly tethered to work, even when "off," is detrimental to employee well-being and ultimately unproductive. Instead of reactive on-call systems interrupting rest and personal time, the author advocates for a proactive approach: building more robust and resilient systems that minimize failures, investing in thorough automated testing and observability, and fostering a culture of shared responsibility for system health. This shift, they believe, would lead to a healthier, more sustainable work environment and ultimately higher quality software.
Hacker News users largely agreed with the author's sentiment about the burden of on-call rotations, particularly poorly implemented ones. Several commenters shared their own horror stories of disruptive and stressful on-call experiences, emphasizing the importance of adequate compensation, proper tooling, and a respectful culture around on-call duties. Some suggested alternative approaches like follow-the-sun models or no on-call at all, advocating for better engineering practices to minimize outages. A few pushed back slightly, noting that some level of on-call is unavoidable in certain industries and that the author's situation seemed particularly egregious. The most compelling comments highlighted the negative impact poorly managed on-call has on mental health and work-life balance, with some arguing it can be a major factor in burnout and attrition.
Revyl, a Y Combinator-backed startup (F24) building a platform for interactive learning experiences, is seeking a Front-End Engineer Intern. The ideal candidate has experience with React, JavaScript, and TypeScript, and a passion for building user-friendly interfaces. Responsibilities include developing and maintaining Revyl's web application, collaborating with the engineering team, and contributing to the platform's growth and evolution. This is a paid, remote position offering valuable experience in a fast-paced startup environment.
Hacker News users discuss the Revyl internship posting, primarily focusing on the low offered compensation ($10/hr) for a YC-backed company. Many commenters express disbelief and concern that such a low rate undervalues the intern's work, especially given the expected skills and the association with Y Combinator. Some suggest that this rate may be a typo or misinterpretation, while others speculate about the potential reasons, including exploiting international interns or simply poor budgeting. A few commenters mention their own higher internship earnings, further highlighting the perceived inadequacy of Revyl's offer. The overall sentiment leans towards criticism of the low pay, questioning the company's priorities and treatment of interns.
The blog post argues that the current approach to software versioning and breaking changes, particularly the emphasis on Semantic Versioning (SemVer), is flawed. It contends that breaking changes are inevitable and often subjective, making strict adherence to SemVer impractical and sometimes misleading. Instead of focusing on meticulously categorizing every change, the author proposes a simpler approach: clearly document all changes, regardless of their perceived impact, and empower users with robust tooling to navigate and manage these changes effectively. This includes tools for automated code modification and comprehensive diffing, enabling developers to adapt to changes smoothly even without perfect backwards compatibility. The core message is that thoughtful documentation and effective tooling are more valuable than rigidly adhering to a potentially arbitrary versioning scheme.
Hacker News users generally agreed with the author's premise that breaking changes are often overemphasized, particularly in the context of libraries. Several commenters highlighted the importance of semantic versioning as a tool for managing change, not a rigid constraint. Some suggested that breaking changes are sometimes necessary for progress and that the cost of avoiding them can outweigh the benefits. A compelling point raised was the distinction between breaking changes for library authors versus application developers, with more leniency afforded to applications. Another commenter offered an alternative perspective, suggesting the "silly" aspect is actually the over-reliance on libraries instead of building simpler solutions in-house. Others noted the prevalence of "dependency hell" caused by frequent updates, even without breaking changes. Finally, the inherent tension between maintaining backwards compatibility and improving software was acknowledged as a complex issue.
The author argues that the rise of AI-powered coding tools, while increasing productivity in the short term, will ultimately diminish the role of software engineers. By abstracting away core engineering principles and encouraging prompt engineering instead of deep understanding, these tools create a superficial layer of "software assemblers" who lack the fundamental skills to tackle complex problems or maintain existing systems. This dependence on AI prompts will lead to brittle, poorly documented, and ultimately unsustainable software, eventually necessitating a return to traditional software engineering practices and potentially causing significant technical debt. The author contends that true engineering requires a deep understanding of systems and tradeoffs, which is being eroded by the allure of quick, AI-generated solutions.
HN commenters largely disagree with the article's premise that prompting signals the death of software engineering. Many argue that prompting is just another tool, akin to using libraries or frameworks, and that strong programming fundamentals remain crucial. Some point out that complex software requires structured approaches and traditional engineering practices, not just prompt engineering. Others suggest that prompting will create more demand for skilled engineers to build and maintain the underlying AI systems and integrate prompt-generated code. A few acknowledge a potential shift in skillset emphasis but not a complete death of the profession. Several commenters also criticize the article's writing style as hyperbolic and alarmist.
Driven by a desire for a more engaging and hands-on learning experience for Docker and Kubernetes, the author created iximiuz-labs. This platform uses a "firecracker-powered" approach, meaning it leverages lightweight virtual machines to provide isolated environments for each student. This allows users to experiment freely with container orchestration without risk, while also experiencing the realistic feel of managing real infrastructure. The platform's development journey involved overcoming challenges related to infrastructure automation, cost optimization, and content creation, resulting in a unique and effective way to learn complex cloud-native technologies.
HN commenters generally praised the author's technical choices, particularly using Firecracker microVMs for providing isolated environments for students. Several appreciated the focus on practical, hands-on learning and the platform's potential to offer a more engaging and effective learning experience than traditional methods. Some questioned the long-term business viability, citing potential scaling challenges and competition from existing platforms. Others offered suggestions, including exploring WebAssembly for even lighter-weight environments, incorporating more visual learning aids, and offering a free tier to attract users. One commenter questioned the effectiveness of Firecracker for simple tasks, suggesting Docker in Docker might be sufficient. The platform's pricing structure also drew some scrutiny, with some finding it relatively expensive.
GitHub Actions' opaque nature makes it difficult to verify the provenance of the code being executed in your workflows. While Actions marketplace listings link to source code, the actual runner environment often uses pre-built distributions hosted by GitHub, with no guarantee they precisely match the public repository. This discrepancy creates a potential security risk, as malicious actors could alter the distributed code without updating the public source. Therefore, auditing the integrity of Actions is crucial, but currently complex. The post advocates for reproducible builds and improved transparency from GitHub to enhance trust and security within the Actions ecosystem.
HN users largely agreed with the author's concerns about the opacity of third-party GitHub Actions. Several highlighted the potential security risks of blindly trusting external code, with some suggesting that reviewing the source of each action should be standard practice, despite the impracticality. Some argued for better tooling or built-in mechanisms within GitHub Actions to improve transparency and security. The potential for malicious actors to introduce vulnerabilities through seemingly benign actions was also a recurring theme, with users pointing to the risk of supply chain attacks and the difficulty in auditing complex dependencies. Some suggested using self-hosted runners or creating internal action libraries for sensitive projects, although this introduces its own management overhead. A few users countered that similar trust issues exist with any third-party library and that the benefits of using pre-built actions often outweigh the risks.
Activeloop, a Y Combinator-backed startup, is seeking experienced Python back-end and AI search engineers. They are building a data lake for deep learning, focusing on efficient management and access of large datasets. Ideal candidates possess strong Python skills, experience with distributed systems and cloud infrastructure, and a background in areas like search, databases, or machine learning. The company emphasizes a fast-paced, collaborative environment where engineers contribute directly to the core product and its open-source community. They offer competitive compensation, benefits, and the opportunity to work on cutting-edge technology impacting the future of AI.
HN commenters discuss Activeloop's hiring post with a focus on their tech stack and the nature of the work. Some express interest in the "AI search" aspect, questioning what it entails and hoping for more details beyond generic buzzwords. Others express skepticism about using Python for performance-critical backend systems, particularly with deep learning workloads. One commenter questions the use of MongoDB, expressing concern about its suitability for AI/ML applications. A few comments mention the company's previous pivot and subsequent fundraising, speculating on its current direction and financial stability. Overall, there's a mix of curiosity and cautiousness regarding the roles and the company itself.
Langfuse, a Y Combinator-backed startup (W23) building observability tools for LLM applications, is hiring in Berlin, Germany. They're seeking engineers across various levels, including frontend, backend, and full-stack, to help develop their platform for tracing, debugging, and analyzing LLM interactions. Langfuse emphasizes a collaborative, fast-paced environment where engineers can significantly impact a rapidly growing product in the burgeoning field of generative AI. They offer competitive salaries and benefits, with a strong focus on learning and professional growth.
Hacker News users discussed Langfuse's Berlin hiring push with a mix of skepticism and interest. Several commenters questioned the company's choice of Berlin, citing high taxes and bureaucratic hurdles. Others debated the appeal of developer tooling startups, with some expressing concern about the long-term viability of the market. A few commenters offered positive perspectives, highlighting Berlin's strong tech talent pool and the potential of Langfuse's product. Some users also discussed the specifics of the roles and company culture, seeking more information about remote work possibilities and the overall work environment. Overall, the discussion reflects the complex considerations surrounding startup hiring in a competitive market.
The author describes the "worst programmer" they know, not as someone unskilled, but as someone highly effective despite unconventional methods. This programmer prioritizes shipping functional code quickly over elegant or maintainable solutions, focusing intensely on the immediate problem and relying heavily on debugging and iterative tweaking. While this approach leads to messy, difficult-to-understand code and frustrates other developers, it consistently delivers working products within tight deadlines, making them a valuable, albeit frustrating, asset. The author ultimately questions conventional programming wisdom, suggesting that perhaps this "worst" programmer's effectiveness reveals a different kind of programming proficiency, prioritizing rapid results over long-term maintainability in specific contexts.
Hacker News users generally agreed with the author's premise that over-engineering and premature optimization are detrimental. Several commenters shared similar experiences with "worst programmers" who prioritized cleverness over simplicity, resulting in unmaintainable code. Some discussed the importance of communication and understanding project requirements before diving into complex solutions. One compelling comment highlighted the Dunning-Kruger effect, suggesting that the "worst programmers" often lack the self-awareness to recognize their shortcomings. Another pointed out that the characteristics described might not signify a "worst" programmer but rather someone mismatched to the project's needs, perhaps excelling in research or low-level programming instead. Several users cautioned against focusing solely on technical skills, emphasizing the importance of soft skills like teamwork and communication.
Edsger Dijkstra argues that array indexing should start at zero, not one. He lays out a compelling case based on the elegance and efficiency of expressing slices or subsequences within an array. Using half-open intervals, where the lower bound is inclusive and the upper bound exclusive, simplifies calculations and leads to fewer "off-by-one" errors. Dijkstra demonstrates that representing a subsequence from element 'i' through 'j' becomes significantly more straightforward when using zero-based indexing, as the length of the subsequence is simply j-i. This contrasts with one-based indexing, which necessitates more complex and less intuitive calculations for subsequence lengths and endpoint adjustments. He concludes that zero-based indexing offers a more natural and consistent way to represent array segments, aligning better with mathematical conventions and ultimately leading to cleaner, less error-prone code.
Hacker News users discuss Dijkstra's famous argument for zero-based indexing. Several commenters agree with Dijkstra's logic, emphasizing the elegance and efficiency of using half-open intervals. Some highlight the benefits in loop constructs and simplifying calculations for array slices. A few point out that one-based indexing can be more intuitive in certain contexts, aligning with how humans naturally count. One commenter notes the historical precedent, mentioning that Fortran used one-based indexing, influencing later languages. The discussion also touches on the trade-offs between conventions and the importance of consistency within a given language or project.
Nvidia Dynamo is a distributed inference serving framework designed for datacenter-scale deployments. It aims to simplify and optimize the deployment and management of large language models (LLMs) and other deep learning models. Dynamo handles tasks like model sharding, request batching, and efficient resource allocation across multiple GPUs and nodes. It prioritizes low latency and high throughput, leveraging features like Tensor Parallelism and pipeline parallelism to accelerate inference. The framework offers a flexible API and integrates with popular deep learning ecosystems, making it easier to deploy and scale complex AI models in production environments.
Hacker News commenters discuss Dynamo's potential, particularly its focus on dynamic batching and optimized scheduling for LLMs. Several express interest in benchmarks comparing it to Triton Inference Server, especially regarding GPU utilization and latency. Some question the need for yet another inference framework, wondering if existing solutions could be extended. Others highlight the complexity of building and maintaining such systems, and the potential benefits of Dynamo's approach to resource allocation and scaling. The discussion also touches upon the challenges of cost-effectively serving large models, and the desire for more detailed information on Dynamo's architecture and performance characteristics.
Verification-first development (VFD) prioritizes writing formal specifications and proofs before writing implementation code. This approach, while seemingly counterintuitive, aims to clarify requirements and design upfront, leading to more robust and correct software. By starting with a rigorous specification, developers gain a deeper understanding of the problem and potential edge cases. Subsequently, the code becomes a mere exercise in fulfilling the already-proven specification, akin to filling in the blanks. While potentially requiring more upfront investment, VFD ultimately reduces debugging time and leads to higher quality code by catching errors early in the development process, before they become costly to fix.
Hacker News users discussed the practicality and benefits of verification-first development (VFD). Some commenters questioned its applicability beyond simple examples, expressing skepticism about its effectiveness in complex, real-world projects. Others highlighted potential drawbacks like the added time investment for writing specifications and the difficulty of verifying emergent behavior. However, several users defended VFD, arguing that the upfront effort pays off through reduced debugging time and improved code quality, particularly when dealing with complex logic. Some suggested integrating VFD gradually, starting with critical components, while others mentioned tools and languages specifically designed to support this approach, like TLA+ and Idris. A key point of discussion revolved around finding the right balance between formal verification and traditional testing.
Component simplicity, in the context of functional programming, emphasizes minimizing the number of moving parts within individual components. This involves reducing statefulness, embracing immutability, and favoring pure functions where possible. By keeping each component small, focused, and predictable, the overall system becomes easier to reason about, test, and maintain. This approach contrasts with complex, stateful components that can lead to unpredictable behavior and difficult debugging. While acknowledging that some statefulness is unavoidable in real-world applications, the article advocates for strategically minimizing it to maximize the benefits of functional principles.
Hacker News users discuss Jerf's blog post on simplifying functional programming components. Several commenters agree with the author's emphasis on reducing complexity and avoiding over-engineering. One compelling comment highlights the importance of simple, composable functions as the foundation of good FP, arguing against premature abstraction. Another points out the value of separating pure functions from side effects for better testability and maintainability. Some users discuss specific techniques for achieving simplicity, such as using plain data structures and avoiding monads when unnecessary. A few commenters note the connection between Jerf's ideas and Rich Hickey's "Simple Made Easy" talk. There's also a short thread discussing the practical challenges of applying these principles in large, complex projects.
The blog post "Zlib-rs is faster than C" demonstrates how the Rust zlib-rs crate, a wrapper around the C zlib library, can achieve significantly faster decompression speeds than directly using the C library. This surprising performance gain comes from leveraging Rust's zero-cost abstractions and more efficient memory management. Specifically, zlib-rs uses a custom allocator optimized for the specific memory usage patterns of zlib, minimizing allocations and deallocations, which constitute a significant performance bottleneck in the C version. This specialized allocator, combined with Rust's ownership system, leads to measurable speed improvements in various decompression scenarios. The post concludes that careful Rust wrappers can outperform even highly optimized C code by intelligently managing resources and eliminating overhead.
Hacker News commenters discuss potential reasons for the Rust zlib implementation's speed advantage, including compiler optimizations, different default settings (particularly compression level), and potential benchmark inaccuracies. Some express skepticism about the blog post's claims, emphasizing the maturity and optimization of the C zlib implementation. Others suggest potential areas of improvement in the benchmark itself, like exploring different compression levels and datasets. A few commenters also highlight the impressive nature of Rust's performance relative to C, even if the benchmark isn't perfect, and commend the blog post author for their work. Several commenters point to the use of miniz, a single-file C implementation of zlib, suggesting this may not be a truly representative comparison to zlib itself. Finally, some users provided updates with their own benchmark results attempting to reconcile the discrepancies.
Lago, an open-source usage-based billing platform, is seeking Senior Ruby on Rails Engineers based in Latin America. They are building a developer-centric product to help SaaS companies manage complex billing models. Ideal candidates possess strong Ruby and Rails experience, enjoy collaborating with product teams, and are passionate about open-source software. This is a fully remote, LATAM-based position offering competitive compensation and benefits.
Several Hacker News commenters express skepticism about Lago's open-source nature, pointing out that the core billing engine is not open source, only the APIs and customer portal. This sparked a discussion about the definition of "open source" and whether Lago's approach qualifies. Some users defend Lago, arguing that open-sourcing customer-facing components is still valuable. Others raise concerns about the potential for vendor lock-in if the core billing logic remains proprietary. The remote work aspect and Latam hiring focus also drew positive comments, with some users appreciating Lago's transparency about salary ranges. There's also a brief thread discussing alternative billing solutions.
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https://news.ycombinator.com/item?id=43641407
HN commenters express skepticism about the viability of Telli's business model, questioning the market demand for another note-taking app, especially one focused on engineers. Several commenters point out the crowded nature of this market segment and suggest the need for a strong differentiator beyond what's described in the linked hiring page. Some also raise concerns about the emphasis on on-site work in Berlin, potentially limiting the applicant pool. Finally, a few commenters express confusion about Telli's value proposition and how it differs from existing tools like Notion or Obsidian. There is a general lack of enthusiasm and a sense that Telli needs to articulate its unique selling proposition more effectively to attract talent.
The Hacker News post discussing Telli hiring founding engineers in Berlin generated a moderate amount of discussion, mostly focused on the challenges and considerations related to relocating to Berlin for a startup role.
One commenter questioned the requirement of being on-site in Berlin, particularly given the current prevalence of remote work. They wondered if this was a strict requirement or if there was any flexibility, suggesting that enforcing on-site work could limit the pool of potential candidates, especially highly skilled ones. This comment sparked further discussion about the trade-offs between remote work and in-person collaboration, with some arguing that early-stage startups often benefit from the close interaction and rapid communication facilitated by co-location.
Another user expressed concern about the visa process for non-EU citizens looking to work in Germany. They pointed out that the process can be lengthy and complex, potentially posing a significant hurdle for interested candidates. This comment prompted a brief exchange about the specific visa requirements and the support that Telli might offer potential hires in navigating the immigration process.
Several comments focused on the cost of living in Berlin, with some suggesting that the provided salary range might not be particularly competitive given the rising rents and living expenses in the city. One user specifically mentioned the difficulty of finding affordable housing in Berlin, particularly for newcomers.
Finally, a few comments touched on the specifics of Telli's product and the technology they are working with. One user inquired about the choice of programming language and framework, expressing interest in the technical challenges involved in building the platform. Another commenter briefly discussed the potential market for Telli's product, speculating on its viability and target audience.
Overall, the comments on the Hacker News post reflect a mix of practical concerns about relocation, visa requirements, and cost of living, alongside some curiosity about the technical aspects of the role and the company's product. They highlight the factors that potential candidates might consider when evaluating a startup opportunity in a foreign country.