Long before modern prediction markets, papal elections fueled a vibrant, informal betting scene. From the Renaissance onwards, gamblers in Italy and beyond wagered on everything from the next pope's nationality and name to the duration of the conclave. These wagers weren't just idle speculation; they reflected aggregated information and collective wisdom about the contenders, the political climate, and the power dynamics within the Catholic Church. This early form of prediction market offered valuable insights, albeit sometimes manipulated by those with vested interests. The practice eventually waned due to concerns about corruption and the Church's disapproval, but it serves as a fascinating precursor to today's formalized prediction platforms.
Autoregressive (AR) models predict future values based on past values, essentially extrapolating from history. They are powerful and widely applicable, from time series forecasting to natural language processing. While conceptually simple, training AR models can be complex due to issues like vanishing/exploding gradients and the computational cost of long dependencies. The post emphasizes the importance of choosing an appropriate model architecture, highlighting transformers as a particularly effective choice due to their ability to handle long-range dependencies and parallelize training. Despite their strengths, AR models are limited by their reliance on past data and may struggle with sudden shifts or unpredictable events.
Hacker News users discussed the clarity and helpfulness of the original article on autoregressive models. Several commenters praised its accessible explanation of complex concepts, particularly the analogy to Markov chains and the clear visualizations. Some pointed out potential improvements, suggesting the inclusion of more diverse examples beyond text generation, such as image or audio applications, and a deeper dive into the limitations of these models. A brief discussion touched upon the practical applications of autoregressive models, including language modeling and time series analysis, with a few users sharing their own experiences working with these models. One commenter questioned the long-term relevance of autoregressive models in light of emerging alternatives.
Merlion is an open-source Python machine learning library developed by Salesforce for time series forecasting, anomaly detection, and other time series intelligence tasks. It provides a unified interface for various popular forecasting models, including both classical statistical methods and deep learning approaches. Merlion simplifies the process of building and training models with automated hyperparameter tuning and model selection, and offers easy-to-use tools for evaluating model performance. It's designed to be scalable and robust, suitable for handling both univariate and multivariate time series in real-world applications.
Hacker News users discussing Merlion generally praised its comprehensive nature, covering many time series tasks in one framework. Some expressed skepticism about Salesforce's commitment to open source projects, citing previous examples of abandoned projects. Others pointed out the framework's complexity, potentially making it difficult for beginners. A few commenters compared it favorably to other time series libraries like Kats and tslearn, highlighting Merlion's broader scope and autoML capabilities, while acknowledging potential overlap. Some users requested clarification on specific features like anomaly detection evaluation and visualization capabilities. Overall, the discussion indicated interest in Merlion's potential, tempered by cautious optimism about its long-term support and usability.
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.
The blog post details the author's experience market making on Kalshi, a prediction market platform. They outline their automated strategy, which involves setting bid and ask prices around a predicted probability, adjusting spreads based on liquidity and event volatility. The author focuses on "Will the Fed cut interest rates before 2024?", highlighting the challenges of predicting this complex event and managing risk. Despite facing difficulties like thin markets and the need for continuous model refinement, they achieved a small profit, demonstrating the potential, albeit challenging, nature of algorithmic market making on these platforms. The post emphasizes the importance of careful risk management, constant monitoring, and adapting to market conditions.
HN commenters discuss the intricacies and challenges of market making on Kalshi, particularly regarding the platform's fee structure. Some highlight the difficulty of profiting given the 0.5% fee per trade and the need for substantial volume to overcome it. Others point out that Kalshi contracts are generally illiquid, making sustained profitability challenging even without fees. The discussion touches on the complexities of predicting probabilities and the potential for exploitation by insiders with privileged information. Some users express skepticism about the viability of retail market making on Kalshi, while others suggest potential strategies involving statistical arbitrage or focusing on less efficient, smaller markets. The conversation also briefly explores the regulatory landscape and Kalshi's unique position as a CFTC-regulated exchange.
Large language models (LLMs) can improve their future prediction abilities through self-improvement loops involving world modeling and action planning. Researchers demonstrated this by tasking LLMs with predicting future states in a simulated text-based environment. The LLMs initially used their internal knowledge, then refined their predictions by taking actions, observing the outcomes, and updating their world models based on these experiences. This iterative process allows the models to learn the dynamics of the environment and significantly improve the accuracy of their future predictions, exceeding the performance of supervised learning methods trained on environment logs. This research highlights the potential of LLMs to learn complex systems and make accurate predictions through active interaction and adaptation, even with limited initial knowledge of the environment.
Hacker News users discuss the implications of LLMs learning to predict the future by self-improving their world models. Some express skepticism, questioning whether "predicting the future" is an accurate framing, arguing it's more akin to sophisticated pattern matching within a limited context. Others find the research promising, highlighting the potential for LLMs to reason and plan more effectively. There's concern about the potential for these models to develop undesirable biases or become overly reliant on simulated data. The ethics of allowing LLMs to interact and potentially manipulate real-world systems are also raised. Several commenters debate the meaning of intelligence and consciousness in the context of these advancements, with some suggesting this work represents a significant step toward more general AI. A few users delve into technical details, discussing the specific methods used in the research and potential limitations.
Postmake.io/revenue offers a simple calculator to help businesses quickly estimate their annual recurring revenue (ARR). Users input their number of customers, average revenue per customer (ARPU), and customer churn rate to calculate current ARR, ARR growth potential, and potential revenue loss due to churn. The tool aims to provide a straightforward way to understand these key metrics and their impact on overall revenue, facilitating better financial planning.
Hacker News users generally reacted positively to Postmake's revenue calculator. Several commenters praised its simplicity and ease of use, finding it a helpful tool for quick calculations. Some suggested potential improvements, like adding more sophisticated features for calculating recurring revenue or including churn rate. One commenter pointed out the importance of considering customer lifetime value (CLTV) alongside revenue. A few expressed skepticism about the long-term viability of relying on a third-party tool for such calculations, suggesting spreadsheets or custom-built solutions as alternatives. Overall, the comments reflected an appreciation for a simple, accessible tool while also highlighting the need for more robust solutions for complex revenue modeling.
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https://news.ycombinator.com/item?id=43290892
HN commenters discuss the history and mechanics of papal betting markets, noting their surprising longevity (dating back to at least the 1500s) and their function as early prediction markets. Some question the article's claim these were the original prediction markets, pointing to earlier examples like commodity futures. Others elaborate on the intricacies of these papal elections, including the role of cardinals and the influence of powerful families like the Medici. The discussion also touches on modern prediction markets like PredictIt and Metaculus, comparing their accuracy and the factors that influence their outcomes. Several commenters delve into the incentives and information asymmetry inherent in such markets, including the potential for manipulation and insider trading.
The Hacker News post "Betting on the Pope was the original prediction market" sparked a moderately active discussion with a variety of comments focusing on historical context, the nature of prediction markets, and tangents inspired by the original article.
Several commenters delved deeper into the history of papal betting, offering additional context. One user highlighted the long history of betting on papal elections, noting its presence throughout the Renaissance and even earlier. They pointed out that these wagers weren't simply informal gambles but were often intertwined with complex financial instruments and used by powerful families like the Medici to hedge political risks. Another commenter expanded on the methods used for these early prediction markets, mentioning the use of informal networks and messengers to disseminate information and facilitate bets across geographical distances. This contributor also touched upon the challenges of enforcing these wagers given the lack of formal regulatory structures.
The discussion also explored the broader definition of prediction markets. One user questioned whether papal betting truly constituted a prediction market in the modern sense, arguing that true prediction markets require a mechanism for prices to fluctuate based on collective wisdom. They suggested that papal betting was more akin to simple gambling due to the lack of a dynamic pricing mechanism. This sparked a small debate, with another commenter countering that the information exchange and speculation surrounding papal elections did influence the odds offered by bookmakers, creating a rudimentary form of price discovery.
Some comments drifted tangentially from the core topic, drawing connections to other historical practices. One user mentioned the practice of betting on ship arrivals in 17th-century Amsterdam, suggesting it as another early form of prediction market. Another commenter noted the prevalence of political betting throughout history, implying that the desire to wager on uncertain future outcomes is a deeply ingrained human behavior. A different comment explored the role of information asymmetry in these early prediction markets, highlighting how access to inside information could significantly impact the outcome of these wagers.
Finally, some comments focused on more practical aspects of the original article. One user praised the article's writing style and the engaging way it presented historical information. Another commenter requested clarification on a specific historical detail mentioned in the piece.
While not a highly active discussion, the comments on the Hacker News post offered valuable historical context, examined the nature of prediction markets, and explored related historical examples. They provided a richer understanding of the topic beyond the scope of the original article.