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.
PropRise, a YC S23 startup, is seeking its first Founding Engineer to build a platform streamlining the commercial real estate appraisal process. This full-stack role involves building the core product from the ground up, including frontend, backend, and database architecture. The ideal candidate is experienced with modern web frameworks, enjoys fast-paced startup environments, and is passionate about improving efficiency in complex industries. Equity is offered, providing an opportunity to significantly impact and benefit from the company's growth.
The Hacker News comments discuss the unusual nature of the job posting for a "Founding Engineer" at a company that already seems to have a product and existing engineers. Several commenters express skepticism about the "Founding Engineer" title, suggesting it might be a way to underpay or mislead potential hires. Others speculate on the reasons behind the seemingly contradictory situation, proposing that perhaps the existing team is non-technical or that the company is pivoting and needs to rebuild its engineering team. Some users question the high salary range offered ($170k - $280k), wondering if it's realistic for a pre-seed company. There's also a brief discussion about the company's business model and potential market.
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.
The Forecasting Company, a Y Combinator (S24) startup, is seeking a Founding Machine Learning Engineer to build their core forecasting technology. This role will involve developing and implementing novel time series forecasting models, working with large datasets, and contributing to the company's overall technical strategy. Ideal candidates possess strong machine learning and software engineering skills, experience with time series analysis, and a passion for building innovative solutions. This is a ground-floor opportunity to shape the future of a rapidly growing startup focused on revolutionizing forecasting.
HN commenters discuss the broad scope of the job posting for a founding ML engineer at The Forecasting Company. Some question the lack of specific problem areas mentioned, wondering if the company is still searching for its niche. Others express interest in the stated collaborative approach and the opportunity to shape the technical direction. Several commenters point out the potentially high impact of accurate forecasting in various fields, while also acknowledging the inherent difficulty and potential pitfalls of such a venture. A few highlight the YC connection as a positive signal. Overall, the comments reflect a mixture of curiosity, skepticism, and cautious optimism regarding the company's prospects.
Roe AI, a YC W24 startup, is seeking a Founding Engineer to build AI-powered tools for reproductive health research and advocacy. The ideal candidate will have strong Python and data science experience, a passion for reproductive rights, and comfort working in a fast-paced, early-stage environment. Responsibilities include developing data pipelines, building statistical models, and creating user-facing tools. This role offers significant equity and the opportunity to make a substantial impact on an important social issue.
HN commenters discuss Roe AI's unusual name, given the sensitive political context surrounding "Roe v Wade," with some speculating it might hinder recruiting or international expansion. Several users question the startup's premise of building a "personalized AI copilot for everything," doubting its feasibility and expressing concerns about privacy implications. There's skepticism about the value proposition and whether this approach is genuinely innovative. A few commenters also point out the potentially high server costs associated with the "always-on" aspect of the AI copilot. Overall, the sentiment leans towards cautious skepticism about Roe AI's viability.
Reprompt, a YC W24 startup, is seeking a Founding AI Engineer to build their core location data infrastructure. This role involves developing and deploying machine learning models to process, clean, and enhance location data from various sources. The ideal candidate has strong experience in ML/AI, particularly with geospatial data, and is comfortable working in a fast-paced startup environment. They will be instrumental in building a world-class location data platform and play a key role in shaping the company's technical direction.
HN commenters discuss the Reprompt job posting, focusing on the vague nature of the "world-class location data" and the lack of specifics about the product. Several express skepticism about the feasibility of accurately mapping physical spaces with AI, particularly given privacy concerns and existing solutions like Google Maps. Others question the startup's actual problem space, suggesting the job description is more about attracting talent than filling a specific need. The YC association is mentioned as both a positive and negative signal, with some seeing it as validation while others view it as a potential indicator of a premature venture. A few commenters suggest potential applications, such as improved navigation or augmented reality experiences, but overall the sentiment reflects uncertainty about Reprompt's direction and viability.
SciPhi, a YC W24 startup, is seeking a Founding AI Research Engineer to build the "copilot for science." This role involves developing AI models for scientific discovery, potentially including tasks like designing experiments, analyzing data, and generating scientific text. Ideal candidates possess strong machine learning expertise, experience with large language models, and a passion for scientific advancement. This is a full-time, remote position offering significant equity and the opportunity to shape the future of scientific research.
HN commenters discuss SciPhi's job posting, expressing skepticism about the extremely broad required skillset, from AI research to frontend and backend development, devops, and even UI/UX design. Some speculate this signals a pre-seed stage startup looking for a "Swiss Army Knife" engineer to handle everything, which could be appealing to some but off-putting to specialists. Others question the feasibility of one person possessing such a diverse range of expertise at a high level. There's also debate on the appropriateness of requesting research publications for such a role and whether the compensation is competitive, given the demands. Several commenters highlight the high bar set by the requirements and the potential for burnout, while others see it as a great opportunity for a generalist to have a significant impact on a new company. The lack of specific research areas mentioned also draws some criticism, with commenters desiring more clarity on SciPhi's focus.
Converge, a YC S23 startup, is seeking a founding engineer to join their team in NYC. They're building a platform to simplify complex enterprise software procurement, aiming to bring transparency and efficiency to the process. The ideal candidate is a full-stack engineer with strong frontend experience, comfortable working in a fast-paced startup environment. Experience with React and Typescript is preferred, and a passion for building impactful products is essential. This is a ground-floor opportunity to shape a company from its early stages and have significant ownership over the product.
Several commenters on Hacker News expressed skepticism about the extremely broad required and "nice-to-have" skills listed in the job posting, finding it unrealistic for a single engineer to possess expertise in such a wide range. Others questioned the high equity offer (0.5-1.5%) for a second engineer, suggesting it might be inflated and not truly representative of the company's stage. The NYC location was also a point of discussion, with some commenters noting the high cost of living and questioning the long-term viability of remaining in the city given potential equity dilution. Finally, several users questioned the core business idea and its differentiation in a crowded market.
CollectWise, a YC F24 startup building a platform for collectibles, is hiring a Founding Engineer. They're looking for a full-stack engineer proficient in React, Node.js, and PostgreSQL to help build their core product. This role involves significant ownership and impact on the company's technical direction and offers competitive salary and equity. Ideal candidates are passionate about collectibles, eager to work in a fast-paced startup environment, and have a strong bias for shipping quickly.
Several Hacker News commenters expressed skepticism about CollectWise's business model, questioning the viability of selling directly to collectors and the potential market size. Some commenters also pointed out the competitive landscape, noting existing players in the collectibles management space. A few users inquired about technical details like the tech stack and the nature of the "founding engineer" role. There was a brief discussion around the valuation of collectibles and the challenges of accurate pricing. Overall, the comments reflected a cautious interest in the company, with many seeking further clarification on its strategy and target market.
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https://news.ycombinator.com/item?id=43257268
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.
The Hacker News post discussing Foundry's job posting for a Founding Engineer to build an internet-scale web crawler generated several comments, mostly focusing on the technical challenges and ethical considerations of such a project.
Several commenters discussed the complexities of building a web crawler at this scale. One commenter highlighted the importance of handling rate limiting, respecting robots.txt, and managing the massive data influx. They pointed out the difficulty of parsing different website structures and the need for robust error handling. Another user emphasized the engineering challenges related to distributed crawling, data deduplication, and efficient storage. The conversation touched upon the need for expertise in technologies like Scrapy, Selenium, and distributed processing frameworks. One comment specifically mentioned the importance of understanding and adhering to legal and ethical guidelines when scraping data.
The ethical implications of large-scale web scraping were also a recurring theme. Some users expressed concerns about potential misuse of scraped data and the privacy implications of collecting vast amounts of information from the web. One comment specifically questioned the company's plans for handling personally identifiable information (PII) and complying with data privacy regulations like GDPR. Another commenter raised the question of the environmental impact of running such a large-scale operation, pointing to the significant energy consumption required for data centers and network infrastructure.
One commenter questioned the "founding engineer" title, suggesting it might indicate a lack of clear direction for the project. They speculated that the company might be experimenting with different ideas, implying a higher degree of risk for the engineer joining at this stage.
Another comment pointed out the potential competitive landscape, suggesting that Foundry might face competition from established players in the web scraping and data aggregation space. They questioned the feasibility of building a truly differentiated offering in a market already dominated by large companies.
Finally, a few comments touched upon the potential benefits of such a project, including the ability to gather valuable data for research, market analysis, and other purposes. However, these comments were generally less detailed and focused more on the hypothetical applications of the technology rather than the specific challenges of building it.