Unsure Calculator is a simple web-based calculator that handles uncertain inputs. Instead of precise numbers, users input estimated ranges (e.g., "100 to 200") or distributions (e.g., "normal(100, 10)"). The calculator then performs the requested arithmetic operations (add, subtract, multiply, divide) and displays the resulting probability distribution of possible outcomes, visualized as a histogram. This allows users to quickly see the range and likelihood of different outcomes when dealing with imprecise estimations, making it useful for back-of-the-napkin calculations involving uncertainty.
Tynan's 2023 work prioritization strategy centers around balancing enjoyment, impact, and urgency. He emphasizes choosing tasks he genuinely wants to do, ensuring alignment with his overall goals, and incorporating a small amount of urgent but less enjoyable work to maintain momentum. This system involves maintaining a ranked list of potential projects, regularly re-evaluating priorities, and focusing on a limited number of key areas, currently including fitness, finance, relationships, and creative pursuits. He acknowledges the influence of external factors but stresses the importance of internal drive and proactively shaping his own work.
HN users generally agreed with the author's approach of focusing on projects driven by intrinsic motivation. Some highlighted the importance of recognizing the difference between genuinely exciting work and mere procrastination disguised as "exploration." Others offered additional factors to consider, like market demand and the potential for learning and growth. A few commenters debated the practicality of this advice for those with less financial freedom, while others shared personal anecdotes about how similar strategies have led them to successful and fulfilling projects. Several appreciated the emphasis on choosing projects that feel right and avoiding forced productivity, echoing the author's sentiment of allowing oneself to be drawn to the most compelling work.
The post contrasts "war rooms," reactive, high-pressure environments focused on immediate problem-solving during outages, with "deep investigations," proactive, methodical explorations aimed at understanding the root causes of incidents and preventing recurrence. While war rooms are necessary for rapid response and mitigation, their intense focus on the present often hinders genuine learning. Deep investigations, though requiring more time and resources, ultimately offer greater long-term value by identifying systemic weaknesses and enabling preventative measures, leading to more stable and resilient systems. The author argues for a balanced approach, acknowledging the critical role of war rooms but emphasizing the crucial importance of dedicating sufficient attention and resources to post-incident deep investigations.
HN commenters largely agree with the author's premise that "war rooms" for incident response are often ineffective, preferring deep investigations and addressing underlying systemic issues. Several shared personal anecdotes reinforcing the futility of war rooms and the value of blameless postmortems. Some questioned the author's characterization of Google's approach, suggesting their postmortems are deep investigations. Others debated the definition of "war room" and its potential utility in specific, limited scenarios like DDoS attacks where rapid coordination is crucial. A few commenters highlighted the importance of leadership buy-in for effective post-incident analysis and the difficulty of shifting organizational culture away from blame. The contrast between "firefighting" and "fire prevention" through proper engineering practices was also a recurring theme.
The blog post "Ask for no, don't ask for yes (2022)" argues that when seeking agreement or buy-in, framing requests negatively—asking for objections rather than approval—can be more effective. This "opt-out" approach lowers the barrier to engagement, making it easier for people to voice concerns they might otherwise keep to themselves. By explicitly inviting dissent, you gather valuable feedback, uncover hidden obstacles, and ultimately increase the likelihood of genuine agreement and successful implementation down the line. This proactive approach to identifying and addressing potential problems can lead to more robust solutions and stronger commitment from all involved parties.
Hacker News users discuss the nuances of the "ask for no" strategy. Several commenters point out that it's not about literally asking for "no," but rather framing the request in a way that makes it easy for someone to decline without feeling guilty or pressured. This approach is seen as particularly useful in sales, negotiations, and managing teams, fostering better relationships by respecting autonomy. Some argue it's a form of manipulation, while others defend it as a way to create psychological safety. The discussion also touches on cultural differences, noting that the directness of "asking for no" might not translate well in all environments. A few users share personal anecdotes of how this strategy has led to better outcomes, emphasizing the importance of sincerity and genuine respect for the other party's decision.
The blog post "On Zero Sum Games (The Informational Meta-Game)" argues that while many real-world interactions appear zero-sum, they often contain hidden non-zero-sum elements, especially concerning information. The author uses poker as an analogy: while the chips exchanged represent a zero-sum component, the information revealed through betting, bluffing, and tells creates a meta-game that isn't zero-sum. This meta-game involves learning about opponents and improving one's own strategies, generating future value even within apparently zero-sum situations like negotiations or competitions. The core idea is that leveraging information asymmetry can transform seemingly zero-sum interactions into opportunities for mutual gain by increasing overall understanding and skill, thus expanding the "pie" over time.
HN commenters generally appreciated the post's clear explanation of zero-sum games and its application to informational meta-games. Several praised the analogy to poker, finding it illuminating. Some extended the discussion by exploring how this framework applies to areas like politics and social dynamics, where manipulating information can create perceived zero-sum scenarios even when underlying resources aren't truly limited. One commenter pointed out potential flaws in assuming perfect rationality and complete information, suggesting the model's applicability is limited in real-world situations. Another highlighted the importance of trust and reputation in navigating these information games, emphasizing the long-term cost of deceptive tactics. A few users also questioned the clarity of certain examples, requesting further elaboration from the author.
CEO Simulator: Startup Edition is a browser-based simulation game where players take on the role of a startup CEO. You manage resources like cash, morale, and ideas, making decisions across departments such as marketing, engineering, and sales. The goal is to navigate the challenges of running a startup, balancing competing priorities and striving for a successful exit, either through acquisition or an IPO. The game features randomized events that force quick thinking and strategic adaptation, offering a simplified but engaging experience of the pressures and triumphs of the startup world.
HN commenters generally found the CEO Simulator simplistic but fun for a short time. Several pointed out the unrealistic aspects of the game, like instantly hiring hundreds of engineers and the limited scope of decisions. Some suggested improvements, including more complex financial modeling, competitive dynamics, and varied employee personalities. A common sentiment was that the game captured the "feeling" of being overwhelmed as a CEO, even if the mechanics were shallow. A few users compared it favorably to other similar games and praised its clean UI. There was also a brief discussion about the challenges of representing startup life accurately in a game format.
This post explores the inherent explainability of linear programs (LPs). It argues that the optimal solution of an LP and its sensitivity to changes in constraints or objective function are readily understandable through the dual program. The dual provides shadow prices, representing the marginal value of resources, and reduced costs, indicating the improvement needed for a variable to become part of the optimal solution. These values offer direct insights into the LP's behavior. Furthermore, the post highlights the connection between the simplex algorithm and sensitivity analysis, explaining how pivoting reveals the impact of constraint adjustments on the optimal solution. Therefore, LPs are inherently explainable due to the rich information provided by duality and the simplex method's step-by-step process.
Hacker News users discussed the practicality and limitations of explainable linear programs (XLPs) as presented in the linked article. Several commenters questioned the real-world applicability of XLPs, pointing out that the constraints requiring explanations to be short and easily understandable might severely restrict the solution space and potentially lead to suboptimal or unrealistic solutions. Others debated the definition and usefulness of "explainability" itself, with some suggesting that forcing simple explanations might obscure the true complexity of a problem. The value of XLPs in specific domains like regulation and policy was also considered, with commenters noting the potential for biased or manipulated explanations. Overall, there was a degree of skepticism about the broad applicability of XLPs while acknowledging the potential value in niche applications where transparent and easily digestible explanations are paramount.
The blog post "Emerging reasoning with reinforcement learning" explores how reinforcement learning (RL) agents can develop reasoning capabilities without explicit instruction. It showcases a simple RL environment called Simplerl, where agents learn to manipulate symbolic objects to achieve desired outcomes. Through training, agents demonstrate an emergent ability to plan, execute sub-tasks, and generalize their knowledge to novel situations, suggesting that complex reasoning can arise from basic RL principles. The post highlights how embedding symbolic representations within the environment allows agents to discover and utilize logical relationships between objects, hinting at the potential of RL for developing more sophisticated AI systems capable of abstract thought.
Hacker News users discussed the potential of SimplerL, expressing skepticism about its reasoning capabilities. Some questioned whether the demonstrated "reasoning" was simply sophisticated pattern matching, particularly highlighting the limited context window and the possibility of the model memorizing training data. Others pointed out the lack of true generalization, arguing that the system hadn't learned underlying principles but rather specific solutions within the confined environment. The computational cost and environmental impact of training such large models were also raised as concerns. Several commenters suggested alternative approaches, including symbolic AI and neuro-symbolic methods, as potentially more efficient and robust paths toward genuine reasoning. There was a general sentiment that while SimplerL is an interesting development, it's a long way from demonstrating true reasoning abilities.
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."
Summary of Comments ( 134 )
https://news.ycombinator.com/item?id=43690289
HN users generally praised the Unsure Calculator for its intuitive approach to dealing with uncertainty in calculations. Several commenters highlighted its potential usefulness in various fields, from project management and cost estimation to personal finance and everyday decision-making. Some suggested improvements, like adding support for distributions beyond normal distributions, and integration with other tools. The clean UI and ease of use were also commended, though one user pointed out a potential ambiguity in the syntax. The developer engaged with the comments, responding to suggestions and clarifying usage. A few commenters also discussed broader implications of embracing uncertainty in calculations and the importance of tools like this for better decision-making.
The Hacker News post discussing the "Unsure Calculator" has generated a fair number of comments, exploring various aspects and potential improvements of the tool.
Several commenters appreciate the simplicity and user-friendliness of the calculator, praising its intuitive syntax and ease of use for quick, probabilistic calculations. They find the ability to express uncertainty directly within calculations particularly helpful. One commenter even suggests integrating it into a spreadsheet environment, highlighting its potential for broader application.
A common thread among the comments involves discussing alternative approaches and existing tools for similar probabilistic computations. Commenters mention libraries like
uncertaintiesin Python and point to existing Monte Carlo simulation techniques as more robust solutions for complex scenarios. They acknowledge the Unsure Calculator's niche as a lightweight tool for simpler estimations, contrasting it with the more comprehensive functionalities of established libraries.The discussion also delves into the specific implementation details of the calculator, including the choice of the PERT distribution for representing uncertainty. Some commenters question this choice and propose alternative distributions or methods for defining uncertainty ranges. A detailed discussion ensues regarding the implications of using different distributions and their impact on the final results.
Furthermore, commenters explore potential improvements and extensions to the calculator's functionality. Suggestions include adding support for correlations between variables, implementing different aggregation methods, and providing more visualizations for the probability distributions. The possibility of incorporating unit handling and error propagation analysis is also raised.
Finally, a few comments focus on the user interface and user experience, proposing enhancements like improved display of results, better handling of edge cases, and more comprehensive documentation. The overall sentiment is positive, with commenters recognizing the value of the Unsure Calculator as a convenient tool for simple probabilistic estimations while acknowledging the potential for further development and refinement.