Google's Gemini robotics models are built by combining Gemini's large language models with visual and robotic data. This approach allows the robots to understand and respond to complex, natural language instructions. The training process uses diverse datasets, including simulation, videos, and real-world robot interactions, enabling the models to learn a wide range of skills and adapt to new environments. Through imitation and reinforcement learning, the robots can generalize their learning to perform unseen tasks, exhibit complex behaviors, and even demonstrate emergent reasoning abilities, paving the way for more capable and adaptable robots in the future.
Spice Data, a Y Combinator-backed startup, is seeking a software engineer to build their AI-powered contract analysis platform. The ideal candidate is proficient in Python and JavaScript, comfortable working in a fast-paced startup environment, and passionate about leveraging large language models (LLMs) to extract insights from complex legal documents. Experience with natural language processing (NLP), information retrieval, or machine learning is a plus. This role offers the opportunity to significantly impact the product's direction and contribute to a rapidly growing company transforming how businesses understand and manage contracts.
HN commenters discuss the unusual job posting from Spice Data (YC S19). Several find the required skill of "writing C code like it's 1974" intriguing, debating whether this implies foregoing modern C practices or simply emphasizes a focus on efficiency and close-to-the-metal programming. Some question the practicality and long-term maintainability of such an approach. Others express skepticism about the company's claim of requiring "PhD-level CS knowledge" for seemingly standard software engineering tasks. The compensation, while unspecified, is a point of speculation, with commenters hoping it justifies the apparently demanding requirements. Finally, the company's unusual name and purported focus on satellite data also draw some lighthearted remarks.
Building an autorouter is significantly more complex than it initially appears. It's crucial to narrow the scope drastically, focusing on a specific problem subset like single-layer PCBs or a particular routing style. Thorough upfront research and experimentation with existing tools and algorithms is essential, as is a deep understanding of graph theory and computational geometry. Be prepared for substantial debugging and optimization, especially around performance bottlenecks, and recognize the importance of iterative development with constant testing and feedback. Don't underestimate the value of visualization for both debugging and user interaction, and choose your data structures and algorithms wisely with future scalability in mind. Finally, recognize that perfect routing is often computationally intractable, so aim for "good enough" solutions and prioritize practical usability.
Hacker News users generally praised the author's transparency and the article's practical advice for aspiring software developers. Several commenters highlighted the importance of focusing on a specific niche and iterating quickly based on user feedback, echoing the author's own experience. Some discussed the challenges of marketing and the importance of understanding the target audience. Others appreciated the author's honesty about the struggles of building a business, including the financial and emotional toll. A few commenters also offered technical insights related to autorouting and pathfinding algorithms. Overall, the comments reflect a positive reception to the article's pragmatic and relatable approach to software development and entrepreneurship.
Weave, a YC W25 startup, is seeking a founding product engineer to build the future of online reading. They're developing a collaborative reading platform to facilitate deeper understanding and engagement with complex topics. This role involves designing and building core product features, directly impacting the user experience. Ideal candidates are strong full-stack engineers with a passion for online communities, education, or productivity. Experience with TypeScript/React is preferred, but a proven ability to learn quickly is paramount.
Several commenters on Hacker News expressed skepticism about the extremely broad job description for a founding product engineer at Weave, finding the listed requirements of "full-stack," AI/ML, distributed systems, and mobile development excessive for a single role. Some questioned the feasibility of finding someone proficient in all those areas and suggested the company hadn't properly defined its product vision. Others pointed out the low salary range ($120k-$180k) for such a demanding role, particularly in a competitive market like San Francisco, speculating that it might indicate a lack of funding or unrealistic expectations. A few commenters defended the breadth, suggesting it's common for early-stage startups to require versatility, and emphasizing the learning opportunities inherent in such a role. There was also a brief discussion on the use of AI/ML, with some questioning its necessity at this stage.
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.
A study published in Primates reveals that chimpanzees exhibit engineering-like behavior when selecting materials for tool construction. Researchers observed chimpanzees in Guinea, West Africa, using probes to extract algae from ponds. They discovered that the chimps actively chose stiffer stems for longer probes, demonstrating an understanding of material properties and their impact on tool functionality. This suggests chimpanzees possess a deeper cognitive understanding of tool use than previously thought, going beyond simply using available materials to strategically selecting those best suited for a specific task.
HN users discuss the implications of chimpanzees selecting specific materials for tool creation, questioning the definition of "engineer" and whether the chimpanzees' behavior demonstrates actual engineering or simply effective tool use. Some argue that selecting the right material is inherent in tool use and doesn't necessarily signify advanced cognitive abilities. Others highlight the evolutionary aspect, suggesting this behavior might be a stepping stone towards more complex toolmaking. The ethics of studying chimpanzees in captivity are also touched upon, with some commenters expressing concern about the potential stress placed on these animals for research purposes. Several users point out the importance of the chimpanzees' understanding of material properties, showing an awareness beyond simple trial and error. Finally, the discussion also explores parallels with other animal species exhibiting similar material selection behaviors, further blurring the lines between instinct and deliberate engineering.
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 "Wheel Reinventor's Principles" advocate for strategically reinventing existing solutions, not out of ignorance, but as a path to deeper understanding and potential innovation. It emphasizes learning by doing, prioritizing personal growth over efficiency, and embracing the educational journey of rebuilding. While acknowledging the importance of leveraging existing tools, the principles encourage exploration and experimentation, viewing the process of reinvention as a method for internalizing knowledge, discovering novel approaches, and ultimately building a stronger foundation for future development. This approach values the intrinsic rewards of learning and the potential for uncovering unforeseen improvements, even if the initial outcome isn't as polished as established alternatives.
Hacker News users generally agreed with the author's premise that reinventing the wheel can be beneficial for learning, but cautioned against blindly doing so in professional settings. Several commenters emphasized the importance of understanding why something is the standard, rather than simply dismissing it. One compelling point raised was the idea of "informed reinvention," where one researches existing solutions thoroughly before embarking on their own implementation. This approach allows for innovation while avoiding common pitfalls. Others highlighted the value of open-source alternatives, suggesting that contributing to or forking existing projects is often preferable to starting from scratch. The distinction between reinventing for learning versus for production was a recurring theme, with a general consensus that personal projects are an ideal space for experimentation, while production environments require more pragmatism. A few commenters also noted the potential for "NIH syndrome" (Not Invented Here) to drive unnecessary reinvention in corporate settings.
"Designing Electronics That Work" emphasizes practical design considerations often overlooked in theoretical learning. It advocates for a holistic approach, considering component tolerances, environmental factors like temperature and humidity, and the realities of manufacturing processes. The post stresses the importance of thorough testing throughout the design process, not just at the end, and highlights the value of building prototypes to identify and address unforeseen issues. It champions "design for testability" and suggests techniques like adding test points and choosing components that simplify debugging. Ultimately, the article argues that robust electronics design requires anticipating potential problems and designing circuits that are resilient to real-world conditions.
HN commenters largely praised the article for its practical, experience-driven advice. Several highlighted the importance of understanding component tolerances and derating, echoing the author's emphasis on designing for real-world conditions, not just theoretical values. Some shared their own anecdotes about failures caused by overlooking these factors, reinforcing the article's points. A few users also appreciated the focus on simple, robust designs, emphasizing that over-engineering can introduce unintended vulnerabilities. One commenter offered additional resources on grounding and shielding, further supplementing the article's guidance on mitigating noise and interference. Overall, the consensus was that the article provided valuable insights for both beginners and experienced engineers.
A high school team designed and built a space probe named Project Daedalus, launched via high-altitude balloon. The probe, constructed using off-the-shelf components and custom PCBs, collected data on temperature, pressure, radiation, magnetic fields, and air quality during its flight. It also captured images and video throughout the ascent and descent. Successful data retrieval was achieved after landing, showcasing the team's ability to create a functional space probe on a limited budget.
The Hacker News comments express admiration for the high school team's ambitious space probe project, with several commenters praising the students' ingenuity and technical skills. Some discuss the challenges of high-altitude ballooning, offering advice on potential improvements like using a GPS tracker with an external antenna and considering the impact of the balloon bursting on the probe's descent. Others inquire about specific aspects of the project, such as the choice of microcontroller and the method of image transmission. The overall sentiment is one of encouragement and interest in the team's future endeavors.
The concept of the "10x engineer" – a mythical individual vastly more productive than their peers – is detrimental to building effective engineering teams. Instead of searching for these unicorns, successful teams prioritize "normal" engineers who possess strong communication skills, empathy, and a willingness to collaborate. These individuals are reliable, consistent contributors who lift up their colleagues and foster a positive, supportive environment where collective output thrives. This approach ultimately leads to greater overall productivity and a healthier, more sustainable team dynamic, outperforming the supposed benefits of a lone-wolf superstar.
Hacker News users generally agree with the article's premise that "10x engineers" are a myth and that focusing on them is detrimental to team success. Several commenters share anecdotes about so-called 10x engineers creating more problems than they solve, often by writing overly complex code, hoarding knowledge, and alienating colleagues. Others emphasize the importance of collaboration, clear communication, and a supportive team environment for overall productivity and project success. Some dissenters argue that while the "10x" label might be hyperbolic, there are indeed engineers who are significantly more productive than average, but their effectiveness is often dependent on a good team and proper management. The discussion also highlights the difficulty in accurately measuring individual developer productivity and the subjective nature of such assessments.
Shadeform, a YC S23 startup building a collaborative 3D design tool for game developers, is seeking a founding senior software engineer. They're looking for someone with strong experience in 3D graphics, game engines (especially Unreal Engine), and C++. This role will involve significant ownership and influence over the product's technical direction, working directly with the founders to build the core platform and its features from the ground up. Experience with distributed systems and cloud infrastructure is a plus.
Several Hacker News commenters expressed skepticism about the Shadeform job posting, primarily focusing on the requested skillset seeming overly broad and potentially unrealistic for a single engineer. Some questioned the viability of finding a candidate proficient in both frontend (React, WebGL) and backend (Rust, distributed systems) development, along with DevOps and potentially even ML experience. Others noted the apparent disconnect between seeking a "founding" engineer while simultaneously advertising a well-defined product and existing team, suggesting the "founding" title might be misleading. A few commenters also pointed out the low end of the offered salary range ($100k) as potentially uncompetitive, especially given the demanding requirements and Bay Area location. Finally, some discussion revolved around the nature of Shadeform's product, with some speculating about its specific application and target audience.
Pivot Robotics, a YC W24 startup building robots for warehouse unloading, is hiring Robotics Software Engineers. They're looking for experienced engineers proficient in C++ and ROS to develop and improve the perception, planning, and control systems for their robots. The role involves working on real-world robotic systems tackling challenging problems in a fast-paced startup environment.
HN commenters discuss the Pivot Robotics job posting, mostly focusing on the compensation offered. Several find the $160k-$200k salary range low for senior-level robotics software engineers, especially given the Bay Area location and YC backing. Some argue the equity range (0.1%-0.4%) is also below market rate for a startup at this stage. Others suggest the provided range might be for more junior roles, given the requirement for only 2+ years of experience, and point out that actual offers could be higher. A few express general interest in the company and its mission of automating grocery picking. The low compensation is seen as a potential red flag by many, while others attribute it to the current market conditions and suggest negotiating.
The first ammonia-powered container ship, built by MAN Energy Solutions, has encountered a delay. Originally slated for a 2024 launch, the ship's delivery has been pushed back due to challenges in securing approval for its novel ammonia-fueled engine. While the engine itself has passed initial tests, it still requires certification from classification societies, a process that is proving more complex and time-consuming than anticipated given the nascent nature of ammonia propulsion technology. This setback underscores the hurdles that remain in bringing ammonia fuel into mainstream maritime operations.
HN commenters discuss the challenges of ammonia fuel, focusing on its lower energy density compared to traditional fuels and the difficulties in handling it safely due to its toxicity. Some highlight the complexity and cost of the required infrastructure, including specialized storage and bunkering facilities. Others express skepticism about ammonia's viability as a green fuel, citing the energy-intensive Haber-Bosch process currently used for its production. One commenter notes the potential for ammonia to play a role in specific niches like long-haul shipping where its energy density disadvantage is less critical. The discussion also touches on alternative fuels like methanol and hydrogen, comparing their respective pros and cons against ammonia. Several commenters mention the importance of lifecycle analysis to accurately assess the environmental impact of different fuel options.
The Startup CTO Handbook offers practical advice for early-stage CTOs, covering a broad spectrum from pre-product market fit to scaling. It emphasizes the importance of a lean, iterative approach to development, focusing on rapid prototyping and validated learning. Key areas include defining the MVP, selecting the right technology stack based on speed and cost-effectiveness, building and managing engineering teams, establishing development processes, and navigating fundraising. The handbook stresses the evolving role of the CTO, starting with heavy hands-on coding and transitioning to more strategic leadership as the company grows. It champions pragmatism over perfection, advocating for quick iterations and adapting to changing market demands.
Hacker News users generally praised the handbook for its practicality and focus on execution, particularly appreciating the sections on technical debt, hiring, and fundraising. Some commenters pointed out potential biases towards larger, venture-backed startups and a slight overemphasis on speed over maintainability in the early stages. The handbook's advice on organizational structure and team building also sparked discussion, with some advocating for alternative approaches. Several commenters shared their own experiences and resources, adding further value to the discussion. The author's transparency and willingness to iterate on the handbook based on feedback was also commended.
Helpcare AI, a Y Combinator Fall 2024 company, is hiring a full-stack engineer. This role involves building the core product, an AI-powered platform for customer support automation specifically for e-commerce companies. Responsibilities include designing and implementing APIs, integrating with third-party services, and working with the founding team on product strategy. The ideal candidate is proficient in Python, JavaScript/TypeScript, React, and PostgreSQL, and has experience with AWS, Docker, and Kubernetes. An interest in AI/ML and a passion for building efficient and scalable systems are also highly desired.
Several Hacker News commenters express skepticism about the Helpcare AI job posting, questioning the heavy emphasis on "hustle culture" and the extremely broad range of required skills for a full-stack engineer, suggesting the company may be understaffed and expecting one person to fill multiple roles. Some point out the vague and potentially misleading language around compensation ("above market rate") and equity. Others question the actual need for AI in the product as described, suspecting it's more of a marketing buzzword than a core technology. A few users offer practical advice to the company, suggesting they clarify the job description and be more transparent about compensation to attract better candidates. Overall, the sentiment leans towards caution for potential applicants.
Koko, a mental health service providing anonymous peer support and clinical care, is seeking a CTO/Lead Engineer. This role will be responsible for leading the engineering team, building and scaling the platform, and shaping the technical strategy. The ideal candidate has experience building and scaling consumer-facing products, managing engineering teams, and working with complex data pipelines and infrastructure. This is a crucial role with significant impact, joining a fast-growing company focused on making mental healthcare more accessible.
HN commenters discuss Koko's CTO search, expressing skepticism and concern about the apparent lack of technical leadership within the company, especially given its focus on mental health and reliance on AI. Some question the wisdom of seeking a CTO so late in the company's development, suggesting it points to scaling or architectural challenges. Others raise ethical concerns about the use of AI in mental health, particularly regarding data privacy and the potential for algorithmic bias. Several comments note the potentially high-pressure environment of a mental health startup and the need for a CTO with experience navigating complex ethical and technical landscapes. Finally, the relatively high equity offered (0.5-1%) is seen by some as a red flag, indicating potential instability or a lack of other experienced engineers.
NASA has successfully demonstrated the ability to receive GPS signals at the Moon, a first for navigating beyond Earth’s orbit. The Navigation Doppler Lidar for Space (NDLS) experiment aboard the Lunar Reconnaissance Orbiter (LRO) locked onto GPS signals and determined LRO’s position, paving the way for more reliable and autonomous navigation for future lunar missions. This achievement reduces reliance on Earth-based tracking and allows spacecraft to more accurately pinpoint their location, enabling more efficient and flexible operations in lunar orbit and beyond.
Several commenters on Hacker News expressed skepticism about the value of this achievement, questioning the practical applications and cost-effectiveness of using GPS around the Moon. Some suggested alternative navigation methods, such as star trackers or inertial systems, might be more suitable. Others pointed out the limitations of GPS accuracy at such distances, especially given the moon's unique gravitational environment. A few commenters highlighted the potential benefits, including simplified navigation for lunar missions and improved understanding of GPS signal behavior in extreme environments. Some debated the reasons behind NASA's pursuit of this technology, speculating about potential future applications like lunar infrastructure development or deep space navigation. There was also discussion about the technical challenges involved in acquiring and processing weak GPS signals at such a distance.
Karl Hans Janke, though posing as a prolific engineer with fantastical inventions, was revealed to be a complete fabrication. His elaborate blueprints and detailed descriptions of complex machines, like the "nuclear reactor bicycle" and the "cloud-slicing airship," captured the public imagination and fooled experts. However, Janke's supposed inventions were ultimately exposed as technically impossible and physically nonsensical, products of a vivid imagination rather than engineering prowess. His legacy lies not in functional technology, but as a testament to the allure of creative invention and the blurring of lines between reality and fantasy.
Hacker News users discuss Karl Hans Janke's elaborate, fictional engineering projects, focusing on the psychological aspects of his creations. Some see Janke as a misunderstood genius, stifled by bureaucracy and driven to create imaginary worlds. Others compare him to a con artist or someone with mental health issues. The most compelling comments debate whether Janke's work was a form of escapism, a commentary on societal limitations, or simply a delusion. One user highlights the potential connection to outsider art, while another draws parallels to fictional detailed worlds, like those found in the works of J.R.R. Tolkien. Several commenters express fascination with the detailed nature of Janke's inventions and the effort he put into documenting them.
Foundry, a YC-backed startup, is seeking a founding engineer to build a massive web crawler. This engineer will be instrumental in designing and implementing a highly scalable and robust crawling infrastructure, tackling challenges like data extraction, parsing, and storage. Ideal candidates possess strong experience with distributed systems, web scraping technologies, and handling terabytes of data. This is a unique opportunity to shape the foundation of a company aiming to index and organize the internet's publicly accessible information.
Several commenters on Hacker News expressed skepticism and concern regarding the legality and ethics of building an "internet-scale web crawler." Some questioned the feasibility of respecting robots.txt and avoiding legal trouble while operating at such a large scale, suggesting the project would inevitably run afoul of website terms of service. Others discussed technical challenges, like handling rate limiting and the complexities of parsing diverse web content. A few commenters questioned Foundry's business model, speculating about potential uses for the scraped data and expressing unease about the potential for misuse. Some were interested in the technical challenges and saw the job as an intriguing opportunity. Finally, several commenters debated the definition of "internet-scale," with some arguing that truly crawling the entire internet is practically impossible.
Dr. Drang poses a puzzle from the March 2025 issue of Scientific American, involving a square steel plate with a circular hole and a matching square-headed bolt. The challenge is to determine how much the center of the hole moves relative to the plate's center when the bolt is tightened, pulling the head flush against the plate. He outlines his approach using vector analysis, trigonometric identities, and small-angle approximations to derive a simplified solution. He compares this to a purely geometric approach, also presented in the magazine, and finds it both more elegant and more readily generalizable to different hole/head sizes.
HN users generally found the puzzle trivial, with several pointing out the quick solution of simply measuring the gap between the bolts to determine which one is missing. Some debated the practicality of such a solution, suggesting calipers would be necessary for accuracy, while others argued a visual inspection would suffice. A few commenters explored alternative, more complex approaches involving calculating the center of mass or using image analysis software, but these were generally dismissed as overkill. The discussion also briefly touched on manufacturing tolerances and the real-world implications of such a scenario.
Building a jet engine is incredibly difficult due to the extreme conditions and tight tolerances involved. The core operates at temperatures exceeding the melting point of its components, requiring advanced materials, intricate cooling systems, and precise manufacturing. Furthermore, the immense speeds and pressures within the engine necessitate incredibly balanced and durable rotating parts. Developing and integrating all these elements, while maintaining efficiency and reliability, presents a massive engineering challenge, requiring extensive testing and specialized knowledge.
Hacker News commenters generally agreed with the article's premise about the difficulty of jet engine manufacturing. Several highlighted the extreme tolerances required, comparing them to the width of a human hair. Some expanded on specific challenges like material science limitations at high temperatures and pressures, the complex interplay of fluid dynamics, thermodynamics, and mechanical engineering, and the rigorous testing and certification process. Others pointed out the geopolitical implications, with only a handful of countries possessing the capability, and discussed the potential for future innovations like 3D printing. A few commenters with relevant experience validated the author's points, adding further details on the intricacies of the manufacturing and maintenance processes. Some discussion also revolved around the contrast between the apparent simplicity of the Brayton cycle versus the actual engineering complexity required for its implementation in a jet engine.
The YouTube video "Microsoft is Getting Rusty" argues that Microsoft is increasingly adopting the Rust programming language due to its memory safety and performance benefits, particularly in areas where C++ has historically been problematic. The video highlights Microsoft's growing use of Rust in various projects like Azure and Windows, citing examples like rewriting core Windows components. It emphasizes that while C++ remains important, Rust is seen as a crucial tool for improving the security and reliability of Microsoft's software, and suggests this trend will likely continue as Rust matures and gains wider adoption within the company.
Hacker News users discussed Microsoft's increasing use of Rust, generally expressing optimism about its memory safety benefits and suitability for performance-sensitive systems programming. Some commenters noted Rust's steep learning curve, but acknowledged its potential to mitigate vulnerabilities prevalent in C/C++ codebases. Several users shared personal experiences with Rust, highlighting its positive impact on their projects. The discussion also touched upon the challenges of integrating Rust into existing projects and the importance of tooling and community support. A few comments expressed skepticism, questioning the long-term viability of Rust and its ability to fully replace C/C++. Overall, the comments reflect a cautious but positive outlook on Microsoft's adoption of Rust.
Voker, a YC S24 startup building AI-powered video creation tools, is seeking a full-stack engineer in Los Angeles. This role involves developing core features for their platform, working across the entire stack from frontend to backend, and integrating AI models. Ideal candidates are proficient in Python, Javascript/Typescript, and modern web frameworks like React, and have experience with cloud infrastructure like AWS. Experience with AI/ML, particularly in video generation or processing, is a strong plus.
HN commenters were skeptical of the job posting, particularly the required "mastery" of a broad range of technologies. Several suggested it's unrealistic to expect one engineer to be a master of everything from frontend frameworks to backend infrastructure and AI/ML. Some also questioned the need for a full-stack engineer in an AI-focused role, suggesting specialization might be more effective. There was a general sentiment that the job description was a red flag, possibly indicating a disorganized or inexperienced company, despite the YC association. A few commenters defended the posting, arguing that "master" could be interpreted more loosely as "proficient" and that startups often require employees to wear multiple hats. The overall tone, however, was cautious and critical.
A Penn State student has refined a century-old math theorem known as the Kutta-Joukowski theorem, which calculates the lift generated by an airfoil. This refined theorem now accounts for rotational and unsteady forces acting on airfoils in turbulent conditions, something the original theorem didn't address. This advancement is significant for the wind energy industry, as it allows for more accurate predictions of wind turbine blade performance in real-world, turbulent wind conditions, potentially leading to improved efficiency and design of future turbines.
HN commenters express skepticism about the impact of this research. Several doubt the practicality, pointing to existing simulations and the complex, chaotic nature of wind making precise calculations less relevant. Others question the "100-year-old math problem" framing, suggesting the Betz limit is well-understood and the research likely focuses on a specific optimization problem within that context. Some find the article's language too sensationalized, while others are simply curious about the specific mathematical advancements made and how they're applied. A few commenters provide additional context on the challenges of wind farm optimization and the trade-offs involved.
Posh, a YC W22 startup, is hiring an Energy Analysis & Modeling Engineer. This role will involve building and maintaining energy models to optimize battery performance and efficiency within their virtual power plant (VPP) software platform. The ideal candidate has experience in energy systems modeling, optimization algorithms, and data analysis, preferably with a background in electrical engineering, mechanical engineering, or a related field. They are looking for someone proficient in Python and comfortable working in a fast-paced startup environment.
The Hacker News comments express skepticism and concern about Posh's business model and the specific job posting. Several commenters question the viability of Posh's approach to automating customer service for banks, citing the complexity of financial transactions and the potential for errors. Others express concerns about the low salary offered for the required skillset, particularly given the location (Boston). Some speculate about the high turnover hinted at by the constant hiring and question the long-term prospects of the company. The general sentiment seems to be one of caution and doubt about Posh's potential for success.
Exa Laboratories, a YC S24 startup, is seeking a founding engineer to develop AI-specific hardware. They're building chips optimized for large language models and generative AI, focusing on reducing inference costs and latency. The ideal candidate has experience with hardware design, ideally with a background in ASIC or FPGA development, and a passion for AI. This is a ground-floor opportunity to shape the future of AI hardware.
HN commenters discuss the ambitious nature of building AI chips, particularly for a small team. Some express skepticism about the feasibility of competing with established players like Google and Nvidia, questioning whether a startup can realistically develop superior hardware and software given the immense resources already poured into the field. Others are more optimistic, pointing out the potential for specialization and niche applications where a smaller, more agile company could thrive. The discussion also touches upon the trade-offs between general-purpose and specialized AI hardware, and the challenges of attracting talent in a competitive market. A few commenters offer practical advice regarding chip design and the importance of focusing on a specific problem within the broader AI landscape. The overall sentiment is a mix of cautious interest and pragmatic doubt.
Unsloth AI, a Y Combinator Summer 2024 company, is hiring machine learning engineers. They're building a platform to help businesses automate tasks using large language models (LLMs), focusing on areas underserved by current tools. They're looking for engineers with strong Python and ML/deep learning experience, preferably with experience in areas like LLMs, transformers, or prompt engineering. The company emphasizes a fast-paced, collaborative environment and offers competitive salary and equity.
The Hacker News comments are generally positive about Unsloth AI and its mission to automate tedious data tasks. Several commenters express interest in the technical details of their approach, asking about specific models used and their performance compared to existing solutions. Some skepticism is present regarding the feasibility of truly automating complex data tasks, but the overall sentiment leans towards curiosity and cautious optimism. A few commenters also discuss the hiring process and company culture, expressing interest in working for a smaller, mission-driven startup like Unsloth AI. The YC association is mentioned as a positive signal, but doesn't dominate the discussion.
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.
Jiga, a YC-backed startup (W21) building a B2B marketplace for industrial materials in Africa, is hiring full-stack engineers proficient in MongoDB, React, and Node.js. They're looking for individuals passionate about building a transformative product with significant real-world impact, comfortable working in a fast-paced environment, and eager to contribute to a rapidly growing company. Experience with Typescript and Next.js is a plus.
HN commenters discuss Jiga's unusual hiring approach, which emphasizes learning MongoDB, React, and Node.js after being hired. Some express skepticism, questioning the practicality of training experienced engineers in specific technologies and the potential for attracting less qualified candidates. Others are more optimistic, viewing it as a refreshing alternative to the overemphasis on specific tech stacks in typical job postings, potentially opening opportunities for talented individuals with strong fundamentals but lacking specific framework experience. The discussion also touches on the potential for lower salaries due to the training aspect and the overall cost-effectiveness of this hiring strategy for Jiga. Several commenters share personal anecdotes of successfully transitioning to new technologies on the job, suggesting that Jiga's approach could be viable.
Summary of Comments ( 68 )
https://news.ycombinator.com/item?id=43557310
Hacker News commenters generally express skepticism about Google's claims regarding Gemini's robotic capabilities. Several point out the lack of quantifiable metrics and the heavy reliance on carefully curated demos, suggesting a gap between the marketing and the actual achievable performance. Some question the novelty, arguing that the underlying techniques are not groundbreaking and have been explored elsewhere. Others discuss the challenges of real-world deployment, citing issues like robustness, safety, and the difficulty of generalizing to diverse environments. A few commenters express cautious optimism, acknowledging the potential of the technology but emphasizing the need for more concrete evidence before drawing firm conclusions. Some also raise concerns about the ethical implications of advanced robotics and the potential for job displacement.
The Hacker News post "How Google built its Gemini robotics models" (linking to a Google blog post about the development of their Gemini robotics models) has generated several comments discussing various aspects of the project.
Several commenters focus on the impressive nature of the robotic demonstrations shown in the accompanying video. They express amazement at the robots' ability to perform complex, multi-step tasks like sorting blocks, opening drawers, and even using tools, all seemingly with a level of dexterity and understanding not commonly seen. Some commenters compare the advancements to previous robotics demonstrations, highlighting the significant progress made. There's a general sentiment of excitement about the potential implications of this technology.
A recurring theme in the comments is the role of simulation in training these models. Commenters discuss the advantages of simulation environments, such as allowing for faster and more diverse training data generation, and the challenges of bridging the gap between simulation and the real world. Some users question the extent to which the demonstrations are purely simulated versus performed by physical robots, and there's a healthy discussion about the limitations of relying solely on simulation.
Some commenters delve into the technical details of the model architecture, discussing the use of techniques like reinforcement learning and imitation learning. They speculate on the specifics of Google's approach, drawing comparisons to other research in the field and raising questions about the scalability and generalizability of the demonstrated capabilities.
Several comments also touch upon the potential societal impact of advanced robotics. Some express concerns about job displacement, while others emphasize the potential benefits in areas like manufacturing, healthcare, and elder care. The ethical considerations surrounding the development and deployment of such technologies are also briefly mentioned.
Finally, a few commenters express skepticism about the claims made in the blog post, questioning the reproducibility of the results and the practicality of deploying these robots in real-world scenarios. They call for more transparency and rigorous evaluation of the technology. However, the overall sentiment appears to be one of cautious optimism, recognizing the significant advancements demonstrated while acknowledging the challenges that lie ahead.