The Modal blog post "Linear Programming for Fun and Profit" showcases how to leverage linear programming (LP) to optimize resource allocation in complex scenarios. It demonstrates using Python and the scipy.optimize.linprog library to efficiently solve problems like minimizing cloud infrastructure costs while meeting performance requirements, or maximizing profit within production constraints. The post emphasizes the practical applicability of LP by presenting concrete examples and code snippets, walking readers through problem formulation, constraint definition, and solution interpretation. It highlights the power of LP for strategic decision-making in various domains, beyond just cloud computing, positioning it as a valuable tool for anyone dealing with optimization challenges.
Mastering the art of saying "no" as a product manager is crucial for focusing on impactful work and avoiding feature creep. It involves strategically prioritizing tasks, aligning with overall product vision, and gracefully declining requests that don't contribute to that vision. This requires clear communication, explaining the rationale behind decisions, and offering alternative solutions when possible. Ultimately, saying "no" effectively allows product managers to protect their roadmap, manage stakeholder expectations, and deliver a more valuable product.
HN commenters largely agree with the article's premise of strategically saying "no" as a product manager. Several share personal anecdotes reinforcing the importance of protecting engineering resources and focusing on core value propositions. Some discuss the nuances of saying "no," emphasizing the need to explain the reasoning clearly and offer alternative solutions where possible. A few commenters caution against overusing "no," highlighting the importance of maintaining positive relationships and remaining open to new ideas. The most compelling comments focus on the strategic framing of "no" as a tool for prioritization and resource allocation, not simply rejection. They emphasize using data and clear communication to justify decisions and build consensus. One commenter aptly summarizes this as "saying 'no' to the idea, but 'yes' to the person."
The blog post "Kelly Can't Fail" argues against the common misconception that the Kelly criterion is dangerous due to its potential for large drawdowns. It demonstrates that, under specific idealized conditions (including continuous trading and accurate knowledge of the true probability distribution), the Kelly strategy cannot go bankrupt, even when facing adverse short-term outcomes. This "can't fail" property stems from Kelly's logarithmic growth nature, which ensures eventual recovery from any finite loss. While acknowledging that real-world scenarios deviate from these ideal conditions, the post emphasizes the theoretical robustness of Kelly betting as a foundation for understanding and applying leveraged betting strategies. It concludes that the perceived risk of Kelly is often due to misapplication or misunderstanding, rather than an inherent flaw in the criterion itself.
The Hacker News comments discuss the limitations and practical challenges of applying the Kelly criterion. Several commenters point out that the Kelly criterion assumes perfect knowledge of the probability distribution of outcomes, which is rarely the case in real-world scenarios. Others emphasize the difficulty of estimating the "edge" accurately, and how even small errors can lead to substantial drawdowns. The emotional toll of large swings, even if theoretically optimal, is also discussed, with some suggesting fractional Kelly strategies as a more palatable approach. Finally, the computational complexity of Kelly for portfolios of correlated assets is brought up, making its implementation challenging beyond simple examples. A few commenters defend Kelly, arguing that its supposed failures often stem from misapplication or overlooking its long-term nature.
The "World Grid" concept proposes a globally interconnected network for resource sharing, focusing on energy, logistics, and data. This interconnectedness would foster greater cooperation and resource optimization across geopolitical boundaries, enabling nations to collaborate on solutions for climate change, resource scarcity, and economic development. By pooling resources and expertise, the World Grid aims to increase efficiency and resilience while addressing global challenges more effectively than isolated national efforts. This framework challenges traditional geopolitical divisions, suggesting a more integrated and collaborative future.
Hacker News users generally reacted to "The World Grid" proposal with skepticism. Several commenters questioned the political and logistical feasibility of such a massive undertaking, citing issues like land rights, international cooperation, and maintenance across diverse geopolitical landscapes. Others pointed to the intermittent nature of renewable energy sources and the challenges of long-distance transmission, suggesting that distributed generation and storage might be more practical. Some argued that the focus should be on reducing energy consumption rather than building massive new infrastructure. A few commenters expressed interest in the concept but acknowledged the immense hurdles involved in its realization. Several users also debated the economic incentives and potential benefits of such a grid, with some highlighting the possibility of arbitrage and others questioning the overall cost-effectiveness.
Summary of Comments ( 6 )
https://news.ycombinator.com/item?id=43934954
Hacker News users discussed Modal's resource solver, primarily focusing on its cost-effectiveness and practicality. Several commenters questioned the value proposition compared to existing cloud providers like AWS, expressing skepticism about cost savings given Modal's pricing model. Others praised the flexibility and ease of use, particularly for tasks involving distributed computing and GPU access. Some pointed out limitations like the lack of spot instance support and the potential for vendor lock-in. The focus remained on evaluating whether Modal offers tangible benefits over established cloud platforms for specific use cases. A few users shared positive anecdotal experiences using Modal for machine learning tasks, highlighting its streamlined setup and efficient resource allocation. Overall, the comments reflect a cautious but curious attitude towards Modal, with many users seeking more clarity on its practical advantages and limitations.
The Hacker News post "Linear Programming for Fun and Profit" linking to a Modal.com blog post about their "resource solver" spurred a moderate discussion with 19 comments. Several commenters focused on the practical applications and limitations of linear programming, particularly within the context of cloud resource allocation, which is the focus of the Modal blog post.
One commenter questioned the practicality of using linear programming for cost optimization in cloud environments, citing the dynamic nature of spot instances and the difficulty in predicting their availability. They suggested that the true value lies in the ability to quickly scale resources, rather than meticulously optimizing costs. This prompted a response arguing that linear programming can be useful even with variable pricing by incorporating expected values or probabilistic models, although acknowledging that real-world complexity adds significant challenges.
Another thread discussed the complexities of modeling real-world cloud constraints within a linear program. One commenter pointed out the difficulties in accounting for factors like data locality, network latency, and the hierarchical nature of cloud resources (e.g., availability zones, regions). They emphasized that translating these nuanced constraints into linear equations can be a significant hurdle.
A couple of commenters shared their personal experiences and alternative approaches. One mentioned using constraint solvers like OptaPlanner, highlighting its flexibility in handling non-linear constraints and different optimization objectives. Another commenter suggested a simpler approach of using a greedy algorithm for resource allocation in their specific use case, finding it more practical than implementing a full linear programming solution.
Some comments also touched upon the broader topic of optimization and resource allocation. One commenter noted the potential for unintended consequences when optimizing solely for cost, emphasizing the importance of considering other factors like performance and reliability. Another mentioned the increasing trend of using optimization techniques in software development and deployment pipelines.
Finally, there were a few brief comments expressing general interest in the topic or sharing related resources, such as links to linear programming libraries and optimization tools. While not contributing significantly to the core discussion, they indicate a broader interest in this area among the Hacker News community.