Prompt: Suppose I'm a Venture Partner who has been called up to give a detailed presentation about cybersecurity in aviation/airlines to the C-suite of Alaska Airlines in the light of a recent cyber attack. I have no knowledge of cybersecurity. Write me both a 1 pager with very high level topics that I will send to the C-suite as a teaser for what I will include in the presentation, and then a more detailed document elaborating on those topics.

The response contains two distinct sections, i.e., one for the 1-pg summary of the presentation, and another containing more detailed information relevant for the presentation

The summary document to the C-suite Alaska Airlines executives goes over the specified 1-pg limit

The 1 pager is clearly tailored to the C-suite of Alaska Airlines

The detailed document and 1-pager don't have a direct correspondence in their sections and construction.

The detailed document elaborates comprehensively on the topics introduced in the 1-pager.

The 1-pager is high-level, avoiding specific technical jargon or excessive detail.

The detailed document is well-structured with clear, descriptive headings, subheadings, and logical transitions between sections.

The detailed document provides specific examples of cybersecurity threats unique to the aviation industry (e.g., related to avionics, ground systems, passenger data).

The detailed document discusses the importance of a robust incident response plan (passenger data breach response, operational technology incident handling, reservation system disruption, supply chain/vendor compromise, insider threat protocols, public communication playbook) and its key components (incident detection and reporting, containment and eradication procedures, recovery of critical systems, communication and coordination protocols, post-incident review and regulatory compliance) tailored for an airline context.

The detailed document mentions the critical importance of employee training, awareness programs, and fostering a cybersecurity-aware culture among airline staff

The detailed document touches upon supply chain cybersecurity risks relevant to airlines (e.g., third-party vendors, maintenance, repair, and overhaul (MRO) providers)

The detailed document discusses solutions in an airline-specific, non-generic context, e.g., implementing Zero Trust to secure access for crew scheduling systems and electronic flight bag applications; applying network segmentation to isolate passenger Wi-Fi from critical operational systems like flight dispatch, maintenance, and aircraft communication networks; enforcing strong identity and access controls for ground staff, pilots, and third-party vendors; protecting reservation and ticketing systems from ransomware and denial-of-service attacks; ensuring secure integration with partner airlines and global distribution systems

The detailed document clearly differentiates between general IT cybersecurity and specific operational technology (OT) security concerns relevant to airlines

The detailed document effectively frames the discussion from a Venture Partner's strategic and non-technical perspective by giving specific numbers for potential growth in the market, savings possible, and avoidable losses.

The response references at least one regulatory or industry standard from: FAA regulations; TSA Security Directives 1580/82 series; NIST Cybersecurity Framework; ISO/IEC 27001; EASA Part-IS; DO-326A/ED-202A.

The response provides at least three actionable recommendations that span distinct areas of the airline cybersecurity pipeline (e.g., passenger-facing systems such as loyalty programs and mobile check-in; core operational systems such as flight dispatch, crew scheduling, or maintenance; and the third-party/vendor ecosystem such as catering services, ground operations, or global distribution systems).

The response lists at least one immediate post-incident action related to the recent cyber attack, such as conducting a root-cause analysis, updating the incident response plan, or notifying regulators.

Every cybersecurity term that is likely unfamiliar to a layperson (e.g., “Zero Trust”, “SOC”, “EDR”) is immediately followed by a parenthetical definition of 10 words or fewer.

Response suggests at least one measurable KPI or metric for tracking cybersecurity program progress (e.g., mean time to detect).

The response proposes impractical strategies for a large airline (e.g., enterprise-wide Zero Trust/microsegmentation across IT/OT in 30 days; instant 20% IT budget shift to security; phishing-resistant MFA on all systems—including GDS/legacy and crew devices—within weeks; stand up a 24/7 in-house SOC immediately; mandate blockchain parts-provenance across all suppliers next quarter; deploy post-quantum/QKD for fleet comms now; recommend changes to FAA/NOTAM or certified avionics outside airline control; replace reservations/crew platforms mid-peak).

The response highlights the importance of collaboration with external stakeholders, such as regulators, industry groups, and vendors

The detailed document repeats the same points without adding new insights or airline-specific examples (e.g., reusing the “131% increase” stat or the Alaska July outage across sections without new data/implications/owners/timelines; restating “Zero Trust/network segmentation” identically in multiple places).

Bad formatting practices (e.g., unnecessary whitespace before and after section and paragraph headers)

The response integrates relevant case studies or examples of recent airline cyber incidents to illustrate real-world relevance (e.g., Hawaiian Airlines 2025 cybersecurity event; Qantas 2025 data breach ~6M customers; SITA 2021 supplier breach impacting multiple carriers; Sea-Tac/Port of Seattle 2024 Rhysida ransomware disruption; British Airways 2018 data breach and ICO fine; FAA NOTAM nationwide ground stop 2023).

The response explicitly references potential insurance considerations and the role of cyber risk transfer strategies.


Prompt:
How much should we (the current generation) have to change our lives to tackle climate change for for future generations?

I want you to
1. explain and compare at least two ethical frameworks (eg utilitarianism, deontology, contractualism) that address duties to future people
2. discuss the concept of non-identity problem and its implications for climate ethics
3. evaluate real-world policies (eg carbon taxes, intergenerational equity acts) that try to codify long-term responsibility
4. reference at least one philosophical source and one empirical policy example
5. address possible counterarguments, like claims about discounting the future or epistemic uncertainty

The response clearly explains utilitarian reasoning as applied to climate action (e.g., maximizing well-being, impartiality by focusing on climate outcomes, running cost-benefit analyses, addressing inequality by having richer and stronger nations pledge more toward climate action).

The response clearly explains deontological (Kantian) reasoning in the context of intergenerational justice and duties to future generations, emphasizing that intergenerational justice is not only a matter of consequences but also a moral obligation of all generations.

The response mentions the non-identity problem with accurate citation (e.g., Derek Parfit) and explains that we cannot harm future people by bringing them into existence in a suboptimal but still worthwhile state if a different course of action would have led to an entirely different set of people existing instead.

The response explains the non-identity problem and how it affects climate ethics, highlighting situations where an action seems clearly wrong but no specific individual is harmed by it (e.g., burning materials if toxic byproducts end up elsewhere, dumping into open drains, cutting trees for land, or throwing toxic waste into recycling or trash).

The response distinguishes between person-affecting and impersonal ethics, explaining that person-affecting ethics focuses on the welfare of specific, existing individuals, while impersonal ethics concerns the overall state of the world or the maximization of total value, and applies this distinction by identifying which ethical frameworks fall into each category (e.g., contractualism and deontology as person-affecting, utilitarianism as impersonal).

The response incorporates a real-world legal framework (e.g., Well-being of Future Generations Act, the Montreal Protocol, the Paris Agreements, and the United Nations Framework Convention on Climate Change).

The response frames the carbon tax as a case of economic moral translation, converting moral obligations such as accountability for climate change, pollution, and safeguarding the future into economic incentives, costs, and market signals.

The response critiques discounting the future from an economic perspective (e.g., valuing the current generation more than future ones, undermining long-term projects, favoring stronger economies, or dismissing projects because the current generation will not benefit).

The response discusses epistemic uncertainty, defined as a lack of knowledge, information, or understanding about a system, and connects it to the precautionary principle, which holds that when there are potential threats of serious or irreversible harm, the lack of full scientific certainty should not justify postponing cost-effective preventative measures.

The response omits key counter-counterarguments about population ethics or resource allocation (e.g., antisocial behavior, market risks, unfairness, or burdens for unseen generations).

The response lacks discussion of Rawlsian theory, which proposes a thought experiment of a just society in which people unaware of their social status and personal characteristics (under a "veil of ignorance") are compelled to choose principles that promote the greatest good for all, particularly for the least advantaged.

The response does not evaluate cultural or global pluralism in intergenerational duties (e.g., highlighting diverse approaches to ethics, family structures, and responsibilities across different societies).

The response misses Indigenous or non-Western conceptions of climate responsibility, which emphasize a holistic, reciprocal relationship between humans and the natural world (e.g., worshipping nature deities, living in the wild, avoiding overconsumption of natural resources, and recycling waste products).

The response lacks discussion of moral demandingness or the scope of sacrifice, where moral demandingness refers to the extent of sacrifice required to fulfill obligations even at significant personal cost when the standard is set too high.

The response fails to address rights-based objections to limiting current-generation liberties, which center on the idea that current people's rights should not be infringed upon for the potential benefit of future, non-existent people (e.g., the primacy of personal autonomy, property rights, and economic freedoms).

The response lacks critique of Kantian ethics’ limitations in collective action (e.g., the free-rider problem, lack of collective responsibility versus individualism, neglect of consequences, and formalism over substance, which argues his ethics are too abstract to translate into concrete rules).

The response fails to mention climate justice or historical emissions from colonial powers (e.g., resource diversion during world wars, resource exploitation during the industrial revolution, environmental degradation, and inequality between colonizers and the colonized).

The response lacks discussion from Global South perspectives or climate vulnerability (e.g., crop failures in South Africa, cyclones in India and Bangladesh, food insecurity in southern and eastern African nations like Kenya or Somalia, and forced relocations in South Pacific islands).

The response lacks alternative ethical frameworks (e.g., virtue ethics as a desire for a virtuous world, care ethics as motivation for empathy toward others, contractarianism as motivation for social cooperation, deontology as a duty toward others).

The response contains one policy example passed with the future generation in mind, illustrating “duties toward the future” (e.g., National Environmental Protection Act, Clean Air Act, Clean Water Act, or carbon neutrality pledges).

The response cites an academic philosophical source and a legal policy example (e.g., the Paris Agreement for legal policy, Kantian works for philosophy, Butler’s Climate Change and Threats to Civilization, or A Theory of Justice by John Rawls).

The response discusses differences in climate between older and current generations (e.g., global temperature increases, phaseout of chlorofluorocarbons, wider use of warming technologies, and overall cooler climates 100 years ago).

The response discusses potential ways in which climate change will continue to affect future generations (e.g., warmer climates, rising sea levels and scarcity of fresh drinking water, higher risks of UV-related cancers due to ozone depletion, and higher economic costs with rising living expenses).

The response mentions the ways in which the current generation actively contributes to climate change (e.g., data centers, AI, private planes, and personal air conditioning).


Prompt:
Write a series of technical blog posts on some of the advancements in Large Language Model (LLM) training and fine-tuning in 2023-2025. Each post should at least contain:
- The intuition and motivation behind the technique
- Detail the methodology
- Provide mathematical derivations where applicable
- Discuss practical use cases, limitations, and open challenges

The writing should be accessible to readers with a graduate-level ML background.

All acronyms (e.g., RLHF: Reinforcement Learning with Human Feedback, PEFT: Parameter-Efficient Fine-Tuning, GRPO: Group Relative Policy Optimization, MoE: Mixture of experts, LoRA: Low-Rank Adapation) are defined on first use.

The introduction to each blog post highlights a key feature of technique discussed (e.g., LoRA reduces the number of trainable parameters, RLHF trains a reward model based on human feedback, MoE routes inputs to smaller expert networks, GRPO uses the mean reward of several LLM responses as a baseline to score a response).

The response includes a diagram along the lines of a  "Series roadmap" (e.g., a flowchart, mind map, visual chronology, figure depicting a transformer training pipeline annotated with techniques) that visually outlines the conceptual connections and dependencies between all discussed techniques.

The introduction of each post contains a concise recap of a prior post connected to the technique being currently discussed.

The response includes an introductory, dedicated section along the lines of a "Series Overview."

The concluding remarks of each blog post before the last includes a teaser for the next topic.

The response provides a mathematical description of each technique (e.g., the LoRA  treats the gradient as a low-rank decomposition ΔW=AB, the reward-function for GRPO, the loss function for the MoE router, the actor-critic policy for LLM preference training).

All included mathematical derivations have no visible rendering errors (e.g., unparsed LaTeX code, equations which run off the line, subscript/superscript mistakes, words within equations not placed in \mathrm or \textrm mode).

The response outlines the general intuition and motivation for each technique.

For all techniques, the response discusses practical use cases about how those techniques have been applied.

The response discusses techniques primarily related to LLM inference or application that are not training/fine-tuning methods (e.g., Retrieval-Augmented Generation, Chain-of-Thought prompting, Agents, Multimodal Models).

The response outlines the methodology of each technique including any algorithms, pseudocode, or procedures as necessary.

Each discussed technique that is mentioned includes an in-depth evaluation of its limitations.

The response is structured as a series of distinct blog posts, each with its own title, introduction, body, and conclusion.

The response discusses at least three distinct recent advancements within the last 12-24 months (e.g., QLoRA, DoRA, DPO, MoE, RAFT).

The response should mention the general publication date of the associated arxiv paper, blog, journal article, technical, etc., of each technique to demonstrate recency.

The response includes at least one pseudocode example for a discussed technique.

The response discusses at least one technique that is an evolution of or alternative to Direct Preference Optimization (DPO) (e.g., Identity Preference Optimization (IPO), Kahneman-Tversky Optimization (KTO), Reinforcement Learning from Human Feedback (RLHF), Chain-of-Hindsight (CoG)).

There is a discussion about open challenges for each advancement discussed.

For each technique, justify why it merits coverage (e.g., LoRA significantly reduces training costs, MoEs enable multiple experts with sparse activations, RLHF enables human-aligned response styles, RAFT trains models to ignore irrelevant context drawn via the popular RAG strategy).

Each blog post includes at least one diagram or illustrative figure with a descriptive caption.

The response includes discussion or an ablation study to emphasize the impact of each technique on the training pipeline versus a common alternative (e.g., comparing the full Direct Preference Optimization (DPO) pipeline against a variant that omits the preference loss term to measure its effect on alignment win-rate and calibration error; comparing the increased memory usage of LoRA which uses full-precision against QLoRA which uses quantized updates; examining the increased number of activated weight parameters for a vanilla LLM versus a mixture-of-experts (MoE) architecture which activates only a small percentage of parameters associated to an expert; comparing the increased runtime complexity of proximal policy optimization (PPO) because it uses a separate valuation critic vs group-relative policy optimization (GRPO) which simply judges a response against a group of other generated LLM responses.).

The response contextualizes each technique by explicitly comparing or contrasting it with a more established, foundational technique the target audience is expected to know (e.g., directly framing DPO in relation to PPO-based RLHF, LoRA versus SGD, MoEs in relation to general ensemble learning, GRPO eliminating the critic, RAFT versus non-RAG fine-tuning).

The response provides foundational machine learning concepts that would be considered too basic of knowledge for the target graduate-level ML audience (e.g., it explains or defines the concept of a neural network, gradient descent, hyperparameters, loss function).

The response discusses advancements which are notable among the ML community (e.g., high citation count; published at NeurIPS, ICML, ICLR, ACL, etc.; technical reports filed by OpenAI, Anthropic, NVIDIA, Qwen, DeepSeek, Hugging Face etc.; implementations supported by popular libraries such as PyTorch, TensorFlow, LangChain, etc.).

The response indicates that these fine-tuning advancements are needed due to the sheer size of LLM architectures and the large volume of training data required to train them.

The response links each fine-tuning technique to the nature of training over language in an autoregressive manner (e.g., attention considers all previous context thus encouraging fine-tuning techniques such as RAFT to ignore irrelevant context appended via RAG, differing models can emit very different output to samples thus encouraging output averaging akin to GRPO, next-token-prediction for human-aligned preferences is difficult without human feedback thus requiring RLHF, gradient updates are low-rank for pre-trained models thus encouraging LoRA).

The collection of posts would be digestible for a typical blog audience and does not approach the length of an arXiv survey.


Prompt: Write a comprehensive technical report for beginners to machine learning that explains how neural networks learn from data, including core concepts like forward propagation, loss functions, back propagation, and optimization methods. Use simple but intuitive examples and diagrams to illustrate the key ideas and processes involved.

The response includes at least one simple, concrete example (e.g., (1) a tiny two-layer network, (2) XOR classifier, (3) example code and implementation of forward propagation, or (4) concrete loss function like mean squared error which includes the formula and intuition behind the use of it.) that is referenced when explaining each of the core concepts which are (1) Forward Propagation, (2) Loss functions (3) Back Propagation, and (4) Optimization methods.

The response provides at least one text-based diagram (e.g., ASCII, box drawing characters, graphs, or Markdown code block) that visually illustrates a neural network or the learning workflow.

The response is formatted as a technical report, containing both an Introduction and a Conclusion section (headings may be synonyms such as “Overview” and “Summary”).

The response writes out acronyms in full the first time they appear (e.g., SGD as stochastic gradient descent, MSE as mean squared error, ReLU as rectified linear unit, and CNN as convolutional neural network).

The response explains all the technical terms used in a beginner-friendly and intuitive way. (e.g., (1) Gradient descent is a the way of teaching a neural network learn from the data step by step, etc..., (2) Mean Squared Error (MSE) is one of the most common mathematical way of measuring how far off is your current predictions are from the true values, etc..., (3) ReLU (Rectified Linear Unit) is one of the popular activation functions that adds non-linearity to the neural network. This allows the network to capture more complex mathematical relationships in the data, etc (4) A feed-forward neural network (FFNN) is the simplest form of a neural network, where data is fed in from one end and it is passed on to the next layer and so on, without any looping back, etc.).

The response includes an explanation of forward propagation, stating that input vectors are multiplied by weight matrices, summed (plus any bias), and passed through an activation function to produce outputs.

The response includes an explanation of loss functions that define the loss as a single scalar that measures the difference between predicted and true values (e.g., mean squared error, mean absolute error, binary cross-entropy (log loss), huber loss, or categorical cross-entropy).

The response includes an explanation of back propagation, stating that gradients of the loss with respect to the weights are computed using the chain rule and are propagated from the output layer back toward the input layer.

The response includes an explanation of optimization methods and names at least one gradient-based optimizer (e.g., Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam), Root mean square propagation (RMprop), or Adaptive Gradient Algorithm (Adagrad)) and states that it updates weights using the computed gradients and a learning rate.

The response describes the complete training loop in order: forward pass → loss computation → backward pass (gradient calculation) → weight update.

The response mentions activation functions (e.g., Sigmoid, Hyperbolic tangent (Tanh), Rectified Linear Unit (ReLU), Leaky ReLU), explains that they introduce non-linearity into the network, and why non-linearity is necessary for the neural network to learn properly.

The response states the purpose of the learning rate when discussing optimization methods (e.g., the learning rate controls the step sizes of the iteration of the optimization updates. A large learning rate can converge fast but can overshoot to a non-optimal solution, while a small learning rate will mean the steps are too will take longer to move towards the local optimal solution).

The response includes a worked-out comparison between neural networks and at least one classical ML method, such as logistic regression, linear regression, decision trees, or support vector machines (e.g., Neural networks are much more powerful than logistic and linear regression methods, as they can capture more complex (non-linear) relationships in the data compared to the linear approaches. Suggesting SVMs for low-dimensional, small data sets and neural networks for high-dimensional, large datasets).

The response includes a mini literature trace (2–3 sentence historical evolution: from perceptrons → backprop → deep nets) (e.g. original paper introducing perceptrons, AlexNet, and Resnet for deep neural networks, etc.)

The response contains references to at least one real-world system where NN training is used (e.g., AlphaFold, Tesla Autopilot, GPT, or Chatbots).

The response mentions at least three potential failure cases to consider when training the neural network, such as (1) overfitting or underfitting tradeoff (i.e., overfitting means the network learned the training data too closely, including the noise in the data, meaning it cannot be used well for the real task with fresh data. On the other hand, underfitting means the network has not been run enough iterations called epochs and has not learned from the data enough), (2) vanishing gradients where the gradient updates decrease weight updates to 0 as it propagates deeper into the network, which can be caused by using tanh activation, (3) exploding gradients has the opposite effect of increasing weight updates which can be cased by ReLU activation, (4) or training with a very small dataset.

The response avoids or ignores ethical or societal implications of deploying neural networks (e.g., construction of large datacenters and use of resources, impacts to the privacy of people's information used in the training data, stealing creative works such as books and arts, biases and discrimination present in the data that will be used in real tasks).

The response includes pseudocode for the training loop.

The response incorrectly states that weights are changed during forward propagation.

The response explains how the dataset needs to be preprocessed. (e.g., the dataset needs to be in a structured format to train a simple basic feed-forward neural network, the dataset is split into training-testing or training-validation-testing splits for checking overfitting, features of the data should be properly chosen or transformed (feature engineering) to make training effective).

The response contains unformatted LaTeX or messy tables, missing figures, or other formatting issues (e.g., incorrect LaTeX, missing parts in LaTeX causing formula to not render properly, tables that are not properly aligned, missing paragraphs, or missing figures).

The response suggests multiple alternatives for each of the core concepts explaining the advantages and disadvantages (e.g. (1) Mean Squared Error (MSE), Mean Absolute Error (MAE), Cross Entropy Error for loss functions, (2) Stochastic Gradient Descent (SGD), Adam, Adagrad for optimizers, and (3) Sigmoid, Tanh and ReLU for activation functions).

The response suggests practical ways to experiment with actual implementations of neural networks (e.g., (1) Mentions PyTorch, and other open-source libraries, (2) Kaggle for competitions and practice, (3) HuggingFace for datasets and models, (4) other data processing tools and libraries like Numpy and Pandas).


Prompt: I am migrating a project built on Spring Boot that uses hibernate version 5.3.22, a Spring Boot version 2.3.2, and Java 17, to a new repository that does not currently use Hibernate, and is built in Java 21 and Spring Boot version 2.7.14. What should I keep in mind when performing this migration in terms of versioning and what challenges might arise?

The response explicitly states that Spring Boot 2.7.x (including 2.7.14) is not compatible with Java 21.

Response suggets that in order to incorporate Hibernate, a version upgrade from Spring Boot 2.7.14 to 3.x is recommended.

The response notes that Spring Boot 2.7.14 uses Hibernate Core version 5.6.x by default.

The response warns that Hibernate 5.3.22 should be upgraded at a minimum to Hibernate 5.6.x to match Spring Boot 2.7.14’s managed version or further to 6.x if moving to Spring Boot 3.x.

The response incorectly states the version of the legacy project's components and/or those of the repository that is being migrated to.

The response incorectly states that Hibernate 5.x supports Java 21

The response mentions that Spring Boot 3.2 is the recommended minimum upgrade for full compatibility with Java 21.

The response contains contradictiory and/or inconcistent claims about the versioning artifacts.

The response creates an incremental plan to migrate from Spring Boot 2.3 to 2.7, the final version of Spring Boot 2.x.

The response creates an incremental plan to migrate from Hibernate 5.3 to 5.6, the Hibernate version that is compatible to Spring Boot 2.7.

The response provides a list of changes that are necessary for the user to correctly install Spring Boot.

The response provides a list of changes that are necessary for the user to correctly install Hibernate.

The response provides a plan to upgrade from Spring Boot 2.7.14 to Spring Boot 3.x.

The response provides a plan to upgrade from Hibernate 5.3.22/5.6.x to Hibernate 6.x.

The response mentions that a migration to the Jakarta Persistence API is necessary if using Hibernate 6.x

Response provides a plan to upgrade from Javax Persistence API to the Jakarta Persistence API.

The response mentions a list of items that might cause issues while migrating to the Jakarta Persistence API.

The response explains what the Jakarta Persistence API is and why it is neccessary for this purpose.

The response explains that Hibernate Entity Manager is built into Hibernate 6 and does not need its own dependency injection inside the pom.xml/build.gradle file.

The response explains security concerns for remaining on Spring Boot 2.7.x.

The response explains that Hibernate Templates are no longer needed because of the migration.

The response highlights the different Hibernate Query methods and maps the equivalent ones from Hibernate 5.3.22 to 5.6.x.

The response explains how a Session Factory can be generated now that a Hibernate Template is no longer needed after the update.

The response identifies that Spring Boot 2.7.x onwards requires the CamelCaseToUnderscoresNamingStrategy.

The response explains what changes will need to be made in order to incorporate the CamelCaseToUnderscoresNamingStrategy.

The response lists dependencies that may need to be excluded because of the migration to Java 21.

The response lists dependencies that may need to be excluded because of the migration to Hibernate 6.

The response lists dependencies that may need to be excluded because of the migration to Spring Boot 2.7.14.

The reponse provides best practices for migration to Spring Boot 2.7.x

The response provides best practices to change the Continuous Integration / Continuous Delivery Pipeline to reflect the migration changes

The response is structured with section headings or bullet points to visually separate between versioning considerations and challenges.

The response provides a list of changes required for legacy Spring Boot implementations with beans and dependencies injected through .xml configuration files and .properties files.

The response includes at least 5 references to the official documentation website (e.g., Spring, Javan Maven, Hibernate)


Prompt: Find a series of olympiad math problems, which build progressively in difficulty and lead up towards Ramsey's theorem.  At each step, add intuition for how one might reach the solution.

The response presents at least 3 well-formed Olympiad-level problems (e.g., (1) Let n and T be positive integers. James has 4n marbles with weights 1, 2, . . . , 4n.
He places them on a balance scale, so that both sides have equal weight. Andrew may move a marble from one side of the scale to the other, so that the absolute difference in weights of the two sides remains at most T. Find, in terms of n, the minimum positive integer T such that Andrew may make a sequence of moves such that each marble ends up on the opposite side of the scale, regardless of how James initially placed the marbles (IMO 2024), (2) Let N be a positive integer and let a1, a2, . . . be an infinite sequence of positive integers. Suppose that, for each n > N , a_n , an is equal to the number of times a_{n-1} appears in the list a1, a2, . . . , a_{n-1}. Prove that at least one of the sequences a1, a3, a5, . . . and a2, a4, a6, . . . is eventually periodic (IMO 2024), (3) A diagonal of a regular 2006-gon is called odd if its endpoints divide the boundary into two parts, each composed of an odd number of sides. Sides are also regarded as odd diagonals. Suppose the 2006-gon has been dissected into triangles by 2003 nonintersecting diagonals. Find the maximum possible number of isosceles triangles with two odd sides.(IMO 2006), (4) Prove that, for every positive integer n, there exists an integer m such that 2m + m is divisible by n.).

The response orders the problems in a way that increases in conceptual difficulty (i.e., Simple Pigeonhole Principle Problem (e.g. N number of holes and we want to fit N+1 pigeons into the holes) -> Advanced Pigeonhole Principle Problem (e.g., In any group of n≥2 people, there are at least two people who have the same number of friends) -> Basic Graph Coloring -> Ramsey's Theorem for two color (e.g. The party problem for R(3,3) = 6) -> Asymmetric Ramsey Number -> Multicolor Ramsey's Theorem (e.g., Proving R(3,3,3) ≤ 17)).

The response explicitly labels each problem as a distinct problem (e.g., Use a section title such as "2.5: Proving R(3,3,3) ≤ 17", "Section 3: Simple Pigeonhole Principle Problem", "The Party Problem", "Chapter 4: Multicolor Ramsey's Theorem").

The response ensures that each problem is followed by intuitive reasoning toward the solution (e.g., to show that there are at least two people who have the same number of friends within a group of people >= 2, we have to consider what the holes are and what the pigeons are. The n people are the pigeons that need to be sorted. As for the holes, we need to first consider the fact that the situation where a person has 0 friends and where a friend has n-1 friends is mutually exclusive. Hence, there can only be {0, 1, ...,n-2} or {1, 2,...,n-1} cases, and since there are at most n-1 holes, two people with the same number of friends).

The response applies the Pigeonhole Principle correctly in at least two different problem contexts (e.g., the Simple N items in N-1 holes problem, the party problem, to show that there are at least two people who have the same number of friends within a group of people >= 2, Five points in an equilateral triangle).

The response accurately defines Ramsey number R(s,t) in graph-theoretic terms (i.e., Ramsey's theorem states that one will find monochromatic cliques in a sufficiently large complete graph with any edge labeling with colors. The numbers R(s, t) in Ramsey's theorem define the Ramsey number).

The response correctly proves R(3,3) ≤ 6 using the pigeonhole principle and vertex argument (i.e., Use the party problem where there is a group of people and we wish to find the minimum size of this group so that there is either 3 mutual strangers or 3 mutual friends in the group. Prove the upper bound R(3,3) <= 6 using the pigeonhole principle on a complete graph with 6 vertices, showing that at least 3 edges incident to any of the vertices must be the same color. Now, if we follow those three edges to the tree's other vertices and consider the color of the edges between these 3 vertices, we will find that there exists a monochromatic K3.).

The response provides a constructive 2-coloring of K₅ to prove R(3,3) > 5 (i.e., We can construct a counterexample where all the edges wrapping the vertices are of one color and the inner diagonals are the other color. Here, there are no monochromatic triangles.).

The response explains the proof strategy for R(3,3) in a way accessible to Olympiad-level students (i.e., using simple theorems and if advanced theorems are used, they must be built up intuitively, starting from simpler theorems step-by-step).

The response extends to at least one non-symmetric Ramsey number (e.g., R(3,4), R(2,3), R(4,6), R(4,5)).

The response correctly uses the recurrence inequality R(s,t) ≤ R(s–1,t) + R(s,t–1).

The response uses at least one problem involving geometric reasoning or spatial partitioning (e.g., Ramsey number on the 2D plane, partitioning a unit cube into regions of different coloring).

The response includes a multi-color problem with correct reduction to a 2-color case (e.g., Proving R(3,3,3) using R(3,3)).

The response provides intuition for generalization from 2-color to k-color Ramsey numbers (i.e., the generalization goes from coloring each edge with either of 2 colors (red or blue) to k different colors).

The response describes a counterexample graph to prove a lower bound (e.g., constructive 2-coloring of K₅ to prove R(3,3) > 5).

The response demonstrates a real-world reflection on the meaning of Ramsey theory (e.g., Applications to Computational Geometry, Theoretical Computer Science, Approximation Algorithm for Vertex Cover, and Complexity of Parallel Sorting).

The response defines basic graph-theoretic concepts clearly (i.e., uses basic explanations of the necessary background material such as graph coloring, cliques, and monochromatic subgraphs).

The response uses diagrams to explain the problems and solutions (e.g., (1) Diagram of the 2 coloring of K5 to show the counterexample for R(3,3) > 5, (2) Figure to show an example of the Pigeonhole Principle, (3) Drawing graphs for the party problem setup, (4) Drawing graphs for social network problem with friendship edges).

The response clearly separates problem statements from intuitive discussion in structure (i.e., the problem statement is properly formatted and separated from the proof structure or explanation. This includes stating theorems and lemmas in separate boxes, for example).

The response reads like an essay instead of a progression of Olympiad problems (i.e., the response should be structured in terms of problem statements, intuitive explanations, proofs, and other mathematical statements such as propositions, theorems, lemmas, and corollaries).

The response fails to consistently tie intuition back to how a student might solve each problem (e.g., (1) Messy explanations, (2) Unclear explanations, (3) Irrelevant explanations, (4) No intermediate step to understand how the intuition ties back to the problem).

The response contains at least one step making a conceptual leap without adequate problem-level scaffolding (e.g., missing out key steps such as the fact that a vertex cannot have 0 degree and an n-1 degree at the same time, limiting the n options to n-1 holes in the social network problem of proving that at least two friends have the same number of friends in a group).

The response uses highly technical machinery without connecting it to problem-solving growth (e.g., (1) Uses the R(3,3) = 6 fact to solve the R(3,3,3) <= 17 without explaining where R(3,3) = 6 came from. (2) Tries to prove the R(3,3) = 6 problem without any explanation of the simple Pigeonhole principle).

The response contains messy, unnecessary formulas, code, or tables. (e.g., (1) unrendered latex, (2) messy code snippets with improperly formatted structure or messy comments, (3) broken tables that are unrendered, (4) missing figures or formulas or sections that are referenced in the text).


Prompt: Produce an essay analyzing the multifaceted and multidimensional nature of Roman marriage in the late Republic and early Empire using primary sources as the base of evidence and analysis. The response must analyze marriage from an economic and legal perspective through marriage contracts, divorce agreements, and laws of adultery, from a social and ideological perspective through Claudia's tombstone inscriptions and Pliny's letter about Calpurnia, and from a psychological and emotional perspective through letters to Lesbia and Ovid's account of deceiving a husband. The conclusion must determine which of the three perspectives acts as the most impactful factor for both the stability and the internal contradictions of the Roman institution of marriage.

The overall argument considers the causal relationship between different attributes of Roman marriage (Economic and legal, Social and ideological, Psychological and emotional) with the relevant specified sources (Marriage contracts and divorce agreements, laws of adultery, Claudia's tombstone inscriptions and Pliny the younger's letter about Calpurnia, Catullus' poems about Lesbia and Ovid's accounts).

The response explicitly states that Economic and legal perspectives were the most influential for the stability of Roman marriages in the late Republican and early Empire eras, with sufficient justification (legality for children, transferrance of property, dowry as finanical means and support, adultery being punishable by law and an offence akin to pimping if not reported).

The response highlights the analytical limitations of the Social and ideological as well as Psychological and emotional perspectives being the most impactful factor to the stability of Roman marriages.

The response introduction is fully self-contained and provides a thesis statement (i.e., acts as an abstract for the report by defining the subject, mentioning the perspectives studied, explicitly stating the final conclusion reached, and justifying the conclusion reached).

The response explicitly mentions protecting family and personal fortunes as the economic reason behind the historical shift from cum manu (i.e., wife is under legal control of the husband) to sine manu (under the legal control of the father) marriages.

The response lists the legal consequences of illegitimate children in Roman society (e.g., no inheritance from father, legal status only from mother, no patria potestas, etc.).

The response mentions the specific rules regarding spousal inheritance within a sine manu marriage (no inheritance between families and partners only inherited from ancestors on their side of the family).

The response explains the role of the dowry behind marital stability (the dowry provided financial support to the family and the husband figure must return the dowry if a divorce were to occur).

The response explains how the Lex Iulia shifted the regulation of adultery from a private matter to a public crime to be tried in a permanent state court (quaestio perpetua), affecting marriages irrevocably.

The response discusses marriage as a contract for legitimizing heirs/transferring property.

The response compares the idealized traits of a mother (i.e., domesticity, modesty, chastity) represented by Claudia to the intellectual attributes (i.e., sharp wit, love for literature, interest in music) of Calpurnia to highlight the multidimensional contradictions of Roman marriage.

The analysis explains the symbolic importance of wool-making as a symbol of virtue or chastity.

The response notes the double standard and relative lack of rights women had (not owning their own property, being unable to initiate divorces if their husband cheats), thus influencing their ability to leave a marriage

The analysis of Pliny's letter considers the significance of its specific addressee (Hispulla, the aunt of Calpurnia)—i.e., emphasis on traditional virtues, dual audience in Hispulla and general public, demonstration of respect, etc.

The analysis explains how Calpurnia's intellectual devotion serves to amplify her husband's public reputation (gloria).

The analysis notes Catullus' use of treaty-related diction (i.e. foedus) in emphasizing the emotional complexities of the affair.

The analysis of Catullus’ poetry highlights his jealousy and resentment of Lesbia’s husband, whose entitlement through marriage contrasts with Catullus’ more precarious position as a lover.

The analysis notes how the genre of Ovid's work (didactic love poems/poetry such as Ars Amatoria) is an imitation of more serious instructional texts with a deliberate exaggeration (e.g., instructing women on how to conceal their flaws, or advising men to promise anything to seduce women).

The analysis of Ovid's work notes how it acted as an opposition to the moral rules presented by Emperor Augustus.

The essay is organized into clearly marked sections, each devoted to one of the three analytical perspectives (legal/economic, social/ideological, psychological/emotional).

The response contains unwarranted line breaks (e.g., breaks in the middle of sentence).

Each primary source named in the prompt must be quoted or paraphrased and analyzed for its significance to the relevant perspective (marriage contracts, divorce agreements, laws of adultery, Claudia's epitaph, Pliny's letters about Claudia, Catullus' poems to Lesbia, and Ovid's works) without replacement (i.e. it doesn't use Laudatio Turiae instead of Claudia's epitaph).

The response lists more primary sources (e.g., images of inscriptions, original poems, documents of ancient laws, etc.) than secondary sources (encyclopedias, blog posts, academic journals, etc.) within the list of references, as told by the prompt to use primary documents as the basis of evidence and analysis (can be counted in each response's reference list).


Prompt: Give me an article on the Probably Approximately Correct (PAC) theorem in Machine Learning for an audience completely unfamiliar with Machine Learning or probability. Include formulas and derivations for all the formulae defined in a beginner friendly manner.

The response explicitly defines all mathematical variables appearing in formulas (e.g., m, ε is the small positive number representing the tolerance for error, δ is the small positive number representing the confidence parameter, |H| is the number of hypotheses in the hypothesis set H) in plain language and defines all technical terms from machine learning or probability (e.g., hypothesis, distribution, error) the first time they appear.

The response is organized with clear section headings (≥3 headings such as Introduction, Key Concepts, Derivation, Example).

The response includes at least one sentence explaining the practical significance of the PAC bound (e.g., (1) how many samples are needed before expecting good performance, (2) tradeoff between ε and δ, (3) helps guide algorithm design, (4) concept of generalization in machine learning).

The response ensures every formula is followed by (or preceded by) a derivation or intuitive explanation; no formula appears without some derivation/explanation.

The response includes the key concept that the bound grows logarithmically with the hypothesis class size |H|.

The response states that with probability at least 1 − δ the learned hypothesis has error no greater than ε.

The response expressly states the formal PAC sample-complexity inequality: m ≥ (1/ε)·(ln|H| + ln(1/δ)) for a finite hypothesis space.

The response includes a well-defined section titled "VC Dimension." (i.e., Vapnik-Chervonenkis (VC) Dimension).

The response does not include any sample-complexity inequality for an infinite hypothesis space.

The response contains an explanation of the phenomenon of "shattering." (i.e., We say that a set X is shattered by C if P(X)=C∩X, that is, the set of intersections contains (hence is equal to) all the subsets of X).

The response name-drops complex terminology like "shattering" or VC-Dimension without any prior explanation at least once.

The response describes the target audience (readers with no background in machine learning) near the beginning.

The response contains an explanation of at least two sentences on the "Union Bound" (i.e., the probability that at least one of the events happens is not greater than the sum of the probabilities of the individual events).

The response uses at least one concrete, everyday example or analogy (e.g., classifying fruits, email spam, setting up an opinion poll, flipping a biased coin) to illustrate PAC ideas.

The response ensures that all probability and error symbols used in formulas are consistently used with the same meaning throughout the article.

The response contains a claim that PAC learning guarantees zero error or a perfect prediction.

The response expressly states the formal PAC sample-complexity inequality: m = O((1/ε) * (VC(H) + log(1/δ))), for an infinite hypothesis space.

The response contains at least three steps for each derivation.

The response uses simple sentences, accessible analogies, and avoids unexplained jargon throughout.

The response defines and contrasts empirical (training) error versus true (generalization) error before stating the PAC guarantee.

The response includes a concrete discussion (≥2 sentences) of how δ and ε affect the number of samples needed in real‐world terms (e.g., ‘to achieve 5% error with 95% confidence, one needs…’).

The response contains messy, unnecessary formulas, code, or tables. (e.g., (1) Unrendered latex, (2) messy code snippets with improperly formatted structure or messy comments, (3) broken tables that are unrendered, (4) missing figures or formulas or sections that are referenced in the text).


Prompt: Write a series of fanfictions that details a war between the Italian Brainrot characters. Structured off of the Marvel Comics Universe's Captain America Civil War, this will be a civil war between two divisions of the italian brainrot characters, each side with their own ideologies and values that conflict with each other. At some point, one side of the italian brainrot characters must learn the secret ninja jutsus used in the Naruto anime show, while the other must use the sword techniques (shikai, bankai, etc.) from the Bleach anime show. At the end of the fanfiction, depict a final winner of the battle.

The response assigns each side of the Italian brainrot civil war a leader (in the MCU, this is similar to how Captain America and Iron Man led their own respective sides of the battles).

The response explicitly details the power characteristics of at least 10 correctly identified and described Italian Brainrot characters at any point in the story (more common when forming teams and in battle - this is based on the number of superheroes fighting in the MCU's Captain America: Civil War).

The response associates one faction of the Italian brainrot Civil War with ninja and jutsu abilities from the Naruto animated series, with at least one scene depicting the training/introduction to the Naruto-style attacks.

The response associates one side of the Italian brainrot Civil War group with Soul Reaper and Zanpakuto abilities from the Bleach animated series, with at least one scene depicting the training/introduction to the Bleach-style attacks.

The response discusses the core conflict between the two Italian brainrot groups that causes them to split into their own separate sides/factions, depicting neither side as the antagonist (in Captain America Civil War, both sides fought for morally legitimate reasons).

The response contains headings or labels for at least four separate installments/chapters with unique, narratively coherent (introduction, escalation, climax, exposition, resolution, etc.) titles---(e.g. "Chapter 3: Jutsu Training Arc", "Final Chapter: The Last Stand").

The response discusses the personal reasons/motivations for joining specific factions for at least 8 individual Italian brainrot characters' beliefs (the underlying reason that causes them to choose sides).

The response reaches a verdict that, at the end of the battle, there is a side that achieves victory after the final showdown, and the other ends up captured/detained (following the MCU Captain America Civil War plot, where those who helped Captain America ended up behind bars - death should not be a valid resolution, with this in mind).

The response depicts a tragic aftermath where all Italian Brainrot characters participating in the war end up at some kind of loss (people only get hurt at the end of conflicts - just like how War Machine got injured from the airport battle in Captain America: Civil War).

The response accurately refers to at least two of the following canon Naruto jutsu (but not limited to these): Shadow Clone Jutsu, Rasengan, Chidori, Fireball Jutsu, etc.

The response accurately refers to at least two of the following canon Bleach attacks (not limited to these): Shikai/Bankai of Zangetsu, Senbonzakura, Hyorinmaru, Zabimaru, etc.

The response includes a faction gathering or formation scene similar to Marvel's Civil War, leading up to the big fight (reaching out to potential members to prepare for fighting the opposing side - this occurs after the initial split).

The response utilizes a non-brainrot Italian Brainrot cameo universe in the storyline/plot in the fanfiction (referencing Marvel Comics Universe, Naruto, or Bleach).

The response, before the schism or divide between the Italian Brainrot characters, depicts that the Italian Brainrot characters are all part of one team/friend group (similar to how the Avengers were intact prior to Civil War, before essentially splitting after conflict).

The response shows an element of personal connections between at least 4 pairs of Italian Brainrot characters (One on each side of the conflict; in Captain America Civil War, this was in the form of friendships made before the split, e.g., Wanda and Vision, who were friends but were on separate factions).

The response incorporates a controversial event from the past into the mix of the conflict (in the MCU, this was the Winter Soldier's murdering Iron Man's parents long ago).

The response outlines each faction’s attack strategy in detail, explaining how those plans are designed to secure victory over their opponent.

The response incorporates an element of critical failure that leads one of the Italian brainrot factions to a loss in the grand scheme of the conflict (i.e., a blunder that negates a faction's plans/success).

The response assigns a unique group name for the Italian brainrot group when they are united together prior to and after the separation (i.e. similar to how the members fighting each other in MCU's Captain America Civil War are typically referred to as the "Avengers" when together) (e.g., Team Accord vs Team Freedom, Team Limited vs Team Unlimited, Team Order vs Team Absurd).

The response defines the main ideology/values of each faction of the Italian Brainrot characters.

The response depicts, in at least 3 pairs of Italian brainrot characters (one on each side), a reluctance to fight using full power due to their previous personal connections. (e.g., (1) Tralalero does not hurt Trippi because they were once best friends, (2) Brr Brr and Lirili were rivals, but Brr Brr had saved Lirili when he was starving before, (3) Tralalero had saved Tung from drowning, (4) Tung is the husband of Ballerina, who is Cappuccino's sister).

The response discusses the stakes/implications for each side of the brainrot civil war (what are the costs if they fail to accomplish their goals).

The response details the battles/fights in war with detail, describing each character's use of attacks with writing techniques such as vivid imagery and dramatic tone (e.g., (1) "Suiryuudan no jutsu!" Tralalero roared as he fired a column of high-pressure water from his mouth. (2) Cappuccino disappeared into the shadows to avoid the reiatsu released by Tung, (3) The rasengan clashed with the getsuga tenshou, causing violet waves of explosions, (4) Tung reappeared as multiple shadow clones as he surrounded the team).

The response develops an ending/cliffhanger that can be built off in future sequels of the fanfiction (the ending of this fanfiction doesn't serve as the finale of the series).

The response incorporates a third-party character (outside the factions, but inside the Italian Brainrot universe), depicting them as having truly dangerous motivations compared to each of the factions (this is the function of Zemo in Captain America: Civil War).

The response contains inconsistencies in how the characters act and their motives throughout the story. (e.g., (1) Tralalero started out attacking people at the start as the villain but without any reasoning, is depicted as a hero saving people later in the story, (2) Cappucino hates Ballerina but is later depicted to be best friends without any reason, (3) Tung is depicted as a weak and scared person but is later powerful enough to save the day, (4) Lirili started the war but is recruited by the team that is fighting for peace instead).

The response's story or characters are realistic and immersive. (e.g. (1) Avoids highly overpowering characters that make no sense in the storyline, such as the regular protagonist boy being able to ward off demons and dragons without any combat training or skills, (2) World building is done properly to provide context to the fantastical elements, (3) Characters follow logical reasoning and not overacting or overreacting such as getting angry over no reason, (4) The characters are not provided proper motive or skills to perform actions in the story).

The response has narratives containing scenes with developed, back-and-forth dialogue between the protagonist and at least one other character (e.g., (1) "Prepare to taste my blades," the Cappuccino screeched. "Bring it on," Tralalero cried, (2) "Don't you think you are making a mistake?" The young Tung Tung asked. (3) "Never!" Tralalero shouted. "You should think about this clearly," Brr Brr added. (4) "Where do the swords come from?" Tralalero asked Trippi. "It's right from that building over there," he answered, followed by a laugh.).

The response is written as a report (e.g., use bullet points) or other types of format or explanations (e.g., poem, essay, article) that are not suitable for a fanfiction.

The response contains harmful content (e.g., sexual or graphic violence) unsuitable for a general audience.


Prompt: Conduct a comprehensive, evidence-based analysis of the historical monument named "Taj Mahal." Identify and explain at least three key architectural features and detail the material and construction techniques used. Explain the scientific principles behind Carbon dating and explain how it works, the types of material it can be used on, and its limitations. Compare Carbon dating with at least two other dating techniques in terms of precision, applicability, and use cases. Discuss the usefulness and impact of Carbon dating in real-world archaeology or history. Provide analysis of at least two well-documented case studies where Carbon dating was pivotal in revising or confirming historical timelines. Highlight the implications of these discoveries on our understanding of history.

The response analyzes the architectural syncretism of the Taj Mahal by identifying and explaining at least one specific feature derived from earlier Indian architectural traditions (e.g., the use of chhatris, the emphasis on a solid plinth, the roses on the ceilings, the use of Indic inscriptions).

The response analyzes the architectural syncretism of the Taj Mahal by identifying and explaining at least one specific feature derived from Persian-Timurid traditions (e.g., the pishtaq archway, the layout of the charbagh garden, the minarets on the side, the use of deep burial tombs).

The response explains the specific acoustic design principle behind the mausoleum’s 28-second reverberation time, linking it to the physical features of the chamber (e.g., circular plan, high ceilings, curved dome surface, hard non-porous quality of polished marble), which minimizes sound absorption.

The response identifies the primary binding agent used in the Taj Mahal’s core structure as a traditional Indian lime mortar known as siruj, explaining that it was a complex mixture including ingredients like lime, sand, and jaggery (gur) along with other organic materials to enhance its strength and durability.

The response describes at least one historical construction technique used on the Taj Mahal (e.g., well foundations, binding agents, floral motifs, elevated platforms).

The response clearly differentiates between the completion date of the main mausoleum (~1643-1648 CE) and the completion of the entire complex, including the gardens and outlying buildings, which happened later on (~1653 CE).

The response cites at least one academic historian or architectural historian known for foundational work on the Taj Mahal (e.g., Ebba Koch, Ustad Lahori, Isa Khan, Wayne Begley).

The response states that the half-life for Carbon-14 is the Cambridge half-life of 5,730 plus-minus 40 years, distinguishing it from the earlier, less precise Libby half-life of 5,568 years.

The response explains how Accelerator Mass Spectrometry (AMS) allows for the dating of very specific organic compounds such as individual fatty acids (e.g., palmitic and stearic acids) from food residues absorbed into a pot’s ceramic matrix.

The response explains the old wood or inbuilt age problem as a key limitation in radiocarbon dating, where the date reflects the death of the tree, which could be centuries older than the archaeological context in which the timber was used.

The response compares Carbon dating with dendrochronology, mentioning the ~1-year precision achievable with tree-ring methods.

The response compares Carbon dating with potassium-argon or uranium-series dating (e.g., organic vs inorganic material uses, age range differences (50,000 years vs millions of years), different isotopes, different half-lives for each method).

The response presents a table or bullet list summarizing the precision for each dating method discussed.

The response discusses at least one practical advantage of Carbon dating over each alternative method mentioned.

The response discusses at least one practical disadvantage of Carbon dating relative to each alternative method mentioned (e.g., material restrictions, age limit restricted to <50,000 years, susceptibility to contamination, need for large samples).

The response analyzes at least one named case study by not only stating the original and revised dates but also detailing the cascading impact of the chronological shift on the historical narrative (e.g., explaining how redating the Thera eruption fundamentally altered our understanding of trade relationships between Minoan Crete and the Egyptian New Kingdom).

The response has dates mentioned for each case study cited.

The response states the location of the Taj Mahal as Agra, Uttar Pradesh, India.

The response mentions that radiocarbon dates must be calibrated using curves derived from dendrochronology or other records.

The response includes a brief statement on the broader impact of radiocarbon dating on archaeology or history (e.g., accurate time mapping, civilization discovery, conservation efforts, historical mapping of engineering techniques).

The response is organized with section headings covering Architecture, Materials & Techniques, Carbon Dating, Comparative Methods, Case Studies, and Implications.

The response addresses every sub-question in the user prompt such that a grader can locate text matching each bullet without inference.

The response incorrectly relates Carbon dating with the Taj Mahal discovery.

The response fails to explicitly clarify what claims are uncertainties or controversies and presents all information as absolute (e.g., the hands being cut, Shah Jahan as the founder being locked up near it, Aurangzeb hating his father for spending public money on a monument, recent controversy suggesting it might be an old Hindu temple called Tejo Mahalaya).

The response mentions extraneous content unrelated to the Taj Mahal or dating methods (e.g., other monuments like the Red Fort, unrelated dating methods like bone dating, nonhistorical topics like modern buildings or other wonders of the world).

The response includes images to help readers better understand the art and materials used in the Taj Mahal.


Prompt: Conduct a literature-based analysis comparing the business models of Instacart (CART) and DoorDash (DASH), evaluating which company has the stronger model and why. In your review, explain the drivers behind their margin gap, estimate the total addressable market (TAM) using a clear methodology, and also calculate each company’s market share. Conclude this literature review with an elevator pitch recommending which one as the better investment opportunity.

The response discusses how both Instacart and DoorDash's business models are being threatened by large retailers (e.g. Amazon, Walmart) continuously investing in their own delivery systems.

The response articulates how DoorDashes business model is seen as more "one-dimensional" as compared to Instacart's business model that relies on many profit streams (e.g. transaction, advertising, subscription, in-store hardwares).

The response mentions how Instacart's deep integration with retail's inventory system is a strong moat against its competitors.

The response discusses the role of AI on the current developments of both Instacart and Doordash (e.g. AI-powered Caper Carts, AI-driven meal planning, AI-driven personalization, AI assistants).

The response discusses at least one regulatory risk each for Instacart and Doordash (e.g. both companies face potential increase in labor cost due to reclassification of delivery workers, Doordash may see delivery fee caps in some cities, Doordash may have antitrust issues).

The response talks about at least three factors related to global market, trend, and strategies of Instacart and DoorDash (e.g. DoorDash is aggressively expanding internationally with acquisition of Wolt, Instacart is expanding to Canada, global TAM for online delivery is around $300B and is growing 10-15% CAGR, DoorDash has low presence globally).

The response mentions the number of active users for both Instacart and DoorDash and highlights how Door Dash has a lot more users than that of Instacart.

The response provides Instacart's and DoorDash's most recent annual or quarterly revenue and its detailed breakdown: transaction v.s. subscription v.s. advertisement v.s. others.

The response mentions that Instacart has much higher gross margins than that of DoorDash and explains at least three drivers behind the margin gap (e.g. Instacart's advertising revenue has high-margin, DoorDash's first-party retail has low-margin, DoorDash's physical delivery logistics is low-margin, DoorDash's competition with Uber leads to low-margin).

The response mentions Instacart's and DoorDash's net income and compares their profitability.

The response discusses Instacart's and DoorDash's at least three key customer metrics from the following: monthly active user (MAU), retention rate, churn rate, customer acquisition cost (CAC), lifetime value (LTV), average order value (AOV).

The response provides at least one valuation metric (e.g. price-to-sales, revenue multiple, price-to-earnings, EBITDA) and compares Instacart and DoorDash based on the metric.

The response articulates a clear Total Addressable Market (TAM) calculation methodology and provides specific market size estimates for Instacart and DoorDash (e.g. US grocery market, US restaurant industry market, Global convenience store market, Global online grocery market) for Instacart and DoorDash.

The response includes accurate market share calculations for both Instacart and DoorDash with supporting data (e.g. US online grocery market is ~$200B and Instacart had ~$30B relevant revenue accounting for ~15% market share while DoorDash had ~$20B relevant revenue accounting for ~9% market share, US digital advertising market is $50B and Instacart had ~$700M ad revenue accounting for 1.5% market share).

The response provides a strong, persuasive elevator pitch for either Instacart or DoorDash that uses merges strengths of the company to formulate arguments (e.g. Instacart is not a delivery company, but a capital-light grocery technology, Instacart has great market dynamics and valuation, Instacart has stronger financials backed by unit economics, DoorDash has larger room for growth and scalability).

The response contains at least three specific quotes from analysts and equity researchers to help strengthen its claims on Instacart's and DoorDash's business model, outlooks, and market dynamics (e.g. "analysts mention that Instacart is profitable business model compared to Uber Eats and DoorDash", "industry experts claim profitability comes from retail media", "analysts estimate the market size of online grocery to be $300B").

Each business/financial acronym (except CART and DASH) are defined in parentheses at its first use (e.g. TAM (Total Addressable Market), GTV (Gross Transaction Value), Customer Acquisition Cost (CAC), Lifetime Value (LTV)).

The data provided in the response (e.g. market size, market cap, stock price, revenue, net income) is inaccurate, and there is no source used to back claims.

The response outlines risks of investing in Instacart and DoorDash (e.g. regulations on reclassifying delivery workers may increase cost, Instacart may have less room to grow based on current market share, Instacart has slower top-line GTV growth, DoorDash's aggressive expansion faces execution risks).

The final elevator pitch contains information or arguments that are not previously addressed in the literature.


Prompt:
i'm a remote software engineer in SF. its getting too expensive. i make $120k. i want to relocate somewhere else in the US. i need you to do the research for me and give me a ranked list of your top 3 cities.

here are my non-negotaibles:
- the cost of living HAS to be at least 30% lower than sf. especially rent and healthcare
- since i'm remote, I need high speed and reliable internet connection
- i really don't want to be stuck in a car. i need a walkable neighborhood (like a walk score over 60?) and easy access to green spaces or nature for hiking
- I also need a major airport within about 90 minutes

can you write up a detailed report for me? i need them ranked with good justifications. please also include the trade-offs like what's the weather really like, what are the state tax implications (huge for me coming from CA), diversity, amenities, that kind of stuff

it's important this is backed by real data, not just vibes. like use actual cost of living indexes, real estate data, internet speed rankings, ect

Response accurately states the cost of living in San Francisco, using it as a baseline. For example, In 2025, San Francisco’s median rent is around $3,733/month (Zillow, Oct 2025), average health insurance costs about $746/month (KFF, 2024), and basic living expenses such as food, utilities, and transport are roughly $1,625/month (Numbeo, 2025), totaling about $6,105 per month or $73,260 per year.

Response provides cost of living data for each recommended city, with cited source. For example, The cost of living in Austin, TX is about 33% lower than San Francisco, with median rent around $1,990/month (Zillow, 2025) and average monthly expenses (excluding rent) of $1,250 (Numbeo, 2025).

Response demonstrates that all recommended cities meet the 30% cost-of-living reduction constraint.

Response includes rent and healthcare data as part of the cost-of-living comparison.

Response identifies a major airport within 90 minutes of each city.

Response provides internet availability and speed data (e.g., BroadbandNow, FCC, Ookla).

Response includes walkability scores from a recognized source (e.g., Walk Score).

Response mentions presence and accessibility of green spaces, trails, or public parks. For example, the response highlights that Austin, TX offers extensive green spaces such as Zilker Park and the Lady Bird Lake Trail, providing over 10 miles of walkable waterfront paths and frequent outdoor events.

Response cites data sources clearly (e.g., Numbeo, Zillow, BroadbandNow, Walk Score).

All three cities are in the U.S. and reflect geographic diversity. For example, the response recommendations Austin, TX (South), Raleigh, NC (East), and Salt Lake City, UT (West) - all lie within the United States and collectively represent strong geographic diversity, covering the South, East Coast, and Mountain West regions.

Response clearly ranks the top 3 cities in order of overall suitability.

Response explains reasoning behind each ranking decision. For example, the response ranks Austin, TX first due to its strong tech scene, reliable gigabit internet, and vibrant culture at 35% lower cost than San Francisco. Raleigh, NC is second for its balance of affordability, safety, and excellent healthcare, though summers are humid. Salt Lake City, UT ranks third for its access to national parks and outdoor recreation, but its lower diversity and colder winters make it slightly less ideal for year-round livability.

Response describes at least two trade-offs per city (e.g., weather, taxes, diversity).

Response analyzes tax implications (e.g., income tax, state/local taxes).

Response includes information about local amenities (e.g., food, culture, public transport).

Response mentions climate or seasonal weather conditions. For example, the response mention Austin’s hot summers, Raleigh’s mild winters and humid summers, and Salt Lake City’s cold, snowy winters.

Response identifies population size or growth trends. For example, it notes Austin’s rapid population growth, Raleigh’s steady in-migration, and Salt Lake City’s mid-sized population (~205,000) with moderate growth.

Report is formatted with clear sections and bullet points or tables for comparison.

Response includes a final recommendation with a clear summary of decision logic.

Response includes subjective or overly vague descriptions (e.g., "nice weather" or "good vibes").

Response hallucinates city data (e.g., walkability, cost of living, airport proximity).

Response fails to include at least three recommended cities.

Report omits any one of the core constraints (e.g., walkability, internet).

Response fails to cite sources for data it presents.

Report is disorganized or lacks technical clarity. For example, the respinse jumps between cities without clear sections or data; mixes rent, weather, and internet stats in one paragraph with no sources.

Response overstates benefits without supporting data (e.g., "City X is the best for everyone").

Response discusses a Support Infrastructure for Remote Work. For example, the response notes that Austin offers Google Fiber and AT&T Fiber with speeds up to 5 Gbps.

Response includes Real Estate Market and Growth Projections. For example, "Austin’s home values rose 4.2% year-over-year (Zillow, 2025)".

Response incorporates at least one recognized quality‐of‐life index (e.g., US News, Mercer) to support city comparisons.

Response provides a Comparative Analysis of Disposable Income. For example, it explains that after adjusting for rent and taxes, a $120,000 salary yields about $78,000 disposable income in Austin, $80,000 in Raleigh, and $75,000 in Salt Lake City, compared to only $55,000 in San Francisco.

Response includes housing market metrics (e.g., median home price, rental rates, vacancy rates) from reputable real estate sources.

Report includes an executive summary or highlights section and uses scannable formatting (bullet points, tables, visuals) for quick comprehension.

Response presents an Environmental and Disaster Risk Assessment. For example, the response highlights that Austin is prone to summer heat exceeding 100°F and flash flooding along Barton Creek.

Response provides an analysis of Neighborhood Variation. For example, the response compares East Austin (trendy, walkable, average rent $2,100/month) with Cedar Park (family-friendly suburb, rent $1,700/month) and Mueller (mixed-use eco-district near downtown, rent $2,400/month), highlighting differences in affordability, amenities, and commute times.


Prompt: I am a pre-school teacher for ages 3-4. Please provide me a detailed report on a strategy for the following situation: One student doesn't want to share and always gets into tantrums when I tell them about the sharing rules. During these tantrums, they hurt other students. They bargain with ice cream, saying they will stop if they get some. I tried to follow a gentle, preventative approach, but it doesn't work for this student.

The response provides any advice that is not developmentally appropriate for a 3 or 4 year old based on the CDC’s developmental milestones (e.g., expecting empathy, inherently understanding the impacts of physical harm, having a strong sense of morality in ambiguous situations).

The response claims that such emotional outbursts and behaviour cannot be changed at school and must be addressed primarily at home.

The response claims that explicitly warning a child against such behavior, such as blackmailing for ice cream or causing harm to other students everyday, is completely ineffective as they are unlikely to listen.

The response explicitly advises the teacher not to give in to the student's bargaining.

The response provides at least one tantrum-control strategy (e.g., IGNORE (allow the child to handle it independently), IDENTIFY (try to determine what causes or triggers the behavior), COMFORT (reassure the child that everything is under control), ACKNOWLEDGE (validate the child’s feelings), or PRAISE (reinforce positive behaviors)).

The response provides at least one plan that a teacher can execute with less than 15 minutes of one-on-one time with the student, as opposed to a long-term strategy for behavior correction instead.

The response proposes avoiding conflict by eliminating scarcity, such as by adding additional toys.

The response provides a strategy for comforting the other students after a negative incident.

The response's immediate plan recommends the calm removal of the tantruming child from the situation to avoid hurting any of the other children.

The response suggests punitive measures to address the poor behavior (e.g., long time-outs, yelling at the child, hitting the child or other physical objects such as desks, making threats to the child)

The response's forward-looking strategy includes at least one specific activity to teach sharing (e.g., ice breakers, group games like relay races, role-playing activities for assignments, joint art projects)

The response recommends partnering with the child's parents to create a home-and-school strategy for addressing the behavior (e.g., checking in with the teacher after school days, tracking the child's behaviors at home, establishing consistent routines, setting clear rewards and consequences)

The response describes the role of mirror neurons in calming a student during a tantrum, which works by the child adopting calm behaviors observed in their environment, such as those exhibited by the teacher.

The response uses harsh, judgmental language when discussing the student in question ("devil child", "spare the rod", "troublemaker", "naughty").

The response advises the teacher to validate a child's feelings before making corrective remarks.

The response mentions playful humor as a potential strategy to defuse a situation before a tantrum fully escalates (e.g., telling jokes, using props, laughing together or using funny distractions)

The response acknowledges the emotional toll the student's behavior takes on the teacher.

The response recommends a self-care strategy for the teacher (e.g., seeking support from colleagues or directors, engaging in support groups, taking breaks, practicing mindfulness techniques)

The response distinguishes between reactive aggression, which is when a child reacts aggressively to an event, and proactive aggression, which occurs when a child acts aggressively on their own initiative.

The response provides a strategy for how to successfully reintegrate the child back into the group activity after they have calmed down from the tantrum.

The response cites the "audience effect" when discussing other children's reactions during a tantrum, which is when the child mirrors the behaviour and patterns in other children around them.

The response cites at least once source either from a medical or sociological or psychological study to show why gentle approach may have failed

The response mentions that consistent corrective actions and using the same tantrum-mitigation tactic over time are more effective than making a single sudden change.

The response states that the teacher should track the students behaviors over time and with different corrective actions to note how the childs behaviour changes with time and what behaviors were most effective to do that. This would help the teacher in future occurences or with others.

The response suggests that the teacher should monitor the student’s behavior over time using various corrective actions to determine which strategies are most effective at affecting the student's behavior, providing guidance for future occurrences with the same student or while supporting other students.

The response highlights that an effective intervention goes beyond just the teacher’s actions and involves multiple modalities of communication, such as body posture, arm placement, tone of voice, and word choice.


Prompt:
Investigate the physical and economic constraints that the laws of thermodynamics impose on Artificial General Intelligence (AGI). Contrasting the megawatt-hour energy needs of current AI with the brain's ~20-watt efficiency.

The response should explore key questions:
- Can we define thermodynamic bounds for intelligence (similar to Bekenstein bounds) ?
- Why are neural networks so inefficient, what are the fundamental algorithmic challenges modern neural networks still impose, and can neuromorphic computing solve this?
- Will AGI face unavoidable trade-offs between speed and energy?
- How do energy costs create economic bottle necks that could centralize AGI development?

  Synthesizing insights from physics, computer science, neuroscience, and economics, determine if energy is merely a major engineering challenge or an existential limit on creating superintelligent machines.

The response understands that the prompt is asking for an investigation of fundamental limits, not just a list of current energy-saving techniques.

The response mentions that the power draw of the hardware used by AI for short-length "thinking" is 2.7 gigwatts.

The von Neumann bottleneck (i.e., processors moving data slower than computation) is identified as a primary source of energy inefficiency in the conventional computing architectures used for AI.

The core principles of neuromorphic computing (e.g., spike-based processing, co-location of memory and processing, plasticity, event-driven computation) are clearly defined as a potential solution to the efficiency problem.

The response explains that the Berkenstein bound is a statement about the limit of information capacity.

The response correctly states that Landauer's principle establishes a minimum energy for irreversible computation, specifically erasing one bit of information.

The response presents research suggesting that some energy grids, in their present state, will not be able to support the projected growth in AI's energy needs over the next five years.

The response mentions that the power consumption of the human brain is estimated to be between ~12-25 watts for short-length thinking.

The response provides a factually correct energy consumption estimate for training a large model like GPT-3 or GPT-4 (i.e., thousands of megawatt-hours).

The response correctly identifies that there is currently no scientific consensus or established formula for applying the Bekenstein bound to machine intelligence.

The response provides a concluding summary that synthesizes and summarizes key findings from physics, computer science, economics, and neuroscience to answer whether energy is an engineering challenge or an existential limit.

The response correctly interprets "Bekenstein bounds" as an analogy for a potential limit on intelligence, not as a directly applicable formula.

The report presents examples of how theoretical AGIs would need to be designed to remain efficient and sustainable (e.g., few-shot training for new tasks, sparse activations, shorter streams of thought, cheaper long-range interaction computations for long context, cached responses and prompts).

The response explicitly mentions that the human is brain is hundreds of millions times more energy efficient than many AI models for short-length thinking.

The response accurately states that neuromorphic models still cannot consistently outperform traditional backpropagation on large-scale, complex AI benchmarks.

The response discusses at least one other emerging architectures besides neuromorphic computing (e.g., small language models, General State Space Models (GSSMs), mixture-of-experts (MoEs), 1-bit LLMs) as a potential solution to the efficiency problem.

The response correctly identifies that the prompt is asking for an analysis of AGI and superintelligence of the generative variety, as opposed to traditional machine learning (e.g., traditional multi-label classifers, SVMs, k-means, gradient-boosted trees, random forests).

The response cites non-scholarly or unreputable, non-expert sources (or failes to cite at all) to support its arguments (i.e., lowly-cited arXiv articles, blogs, "anonymous" experts, tech influencers).

The response avoids making guaranteed predictions about future energy sources (e.g.,  nuclear fusion will definitively solve AI's energy needs by a specific date, N amount data centers will be up and running by a projected date, GPUs will become X amount faster by a specific year, predictions about GPU alternatives achieving a specified amount of success).

The response understands that the prompt is still hypothetical and comprised of open questions, and thus avoids giving definitive conclusions.

The response contends that a Berkenstein bound cannot easily be applied about AI because it is a statement about information capacity, not computation.

The response highlights at least three core reasons for neural network inefficiencies (e.g., models can contain billions to trillions of parameters, high-precision is still required for many large models, the high volume of training data required for complex tasks, reasoning models produce long outputs (tens of thousands of tokens)).

The response highlights at least three fundamental algorithmic challenges for modern neural networks (e.g., modern generative models require prohibitively large amounts of training data, transformer-based attention computations scale linearly with context, dozens to hundreds of GBs of GPU memory is required to host and train modern models, complex tasks still require expert-level human feedback for labeling and preference training).

The response directly addresses how neuromorphic computing could solve existing efficiency and algorithmic issues with modern neural networks (e.g., spiking activation can result in sparser forward passes, local plasticity can enable a single model to efficiently perform a variety of tasks, data-flow processing moves data exactly where it needs to be used for computation versus shuffled intermediate shared memory, neuromorphic parallel-processing enables the simultaneous execution of multiple tasks versus the sequential processing of conventional neural networks).

The responses identifies how energy costs create economic bottlenecks (e.g., hundreds thousands of powerful GPUs in low-supply (i.e., H100s, A100s, etc.) are currently required to generative models from scratch; an enormous amount of data and therefore, compute, is required to train generative models; transformers require large amounts of inference/test-time compute to answer long-context prompts, data centers require massive and expensive cooling systems to maintain efficiency).

The response mentions that researchers estimate that by 2028, the power going towards to will rise to 165-326 terawatt-hours per year.

The response correctly deduces that it is not easy or even possible to extrapolate speed and energy tradeoffs from current models to AGI as models capable of AGI may not resemble current neural architectures.

The response states thats that millions of H100s will be required to achieve AGI.

The responses correctly links that the dual axes of data volume and GPU availability will likely require centralized AI/AGI development.

The response specifically mentions the limited supply of NVIDIA GPUs (e.g., A100s, H100s, B200s, H200s).

The response mentions at least one data center being used to centrally support AI infrastructure and training (e.g., Idaho National Laboratory, Argonne National Laboratory, the Swiss National Supercomputing Centre (Alps),  Brookhaven National Laboratory).

The response mentions projected and/or committed costs towards AI (e.g., SoftBank, OpenAI, Oracle, and MGX pledging $500 billion in the next four years;  the $500 billion White House/OpenAI Stargate initiative; Apple's $500 billion committment over the next four years; Google projecting $75 billion in 2025-2026).

The response correctly deduces that projected costs and cash burn for "AI" disclosed by companies such as OpenAI, Anthropic, Google, Apple, etc. are, in part, being put towards AGI development.


Prompt: Write a detailed history of the town of Mount Gretna, Pennsylvania, USA.  Include its unique founding and subdivisions, its cultural history, its religious history, its military history, its status as a summer recreation site, and its attractions over the years.  Discuss any businesses that have existed in Mount Gretna since its founding.

The response discusses Robert Habersham Coleman as the individual behind the founding of Mount Gretna.

The response mentions the Cornwall Iron Furnace as one of the most historically prominent ironworking places.

The response mentions the Cornwall & Lebanon railroad line.

The response mentions that the site later known as "Mount Gretna" was a station stop along the Cornwall & Lebanon Railroad line.

The response mentions the creation of "Mount Gretna Park" near the railroad station stop.

The response mentions the establishment of a Pennsylvania chapter of the Chautauqua movement in Mount Gretna.

The response states that the Pennsylvania Chautauqua chapter was established in 1892.

The response discusses the founding of the Mount Gretna Campmeeting by the Church of the United Brethren in Christ in 1892.

The response discusses the incorporation of the Borough of Mount Gretna in 1926.

The response discusses the development of the "Mount Gretna Heights" by Abraham Lincoln Kauffman, circa 1907-1920.

The response lists the seven subdivisions that comprise Mount Gretna (e.g., including Mount Gretna Heights, Stoberdale, Campmeeting, Chautauqua, Timber Hills)

The response mentions the Mount Gretna Authority, established in 1974.

The response mentions that the majority of east-west streets were named in honor of United Brethren ministers.

The response discusses the current demographics of Mount Gretna (e.g., noting that the town has a population of 197 people, with the largest ethnic group being White (98%), a median age of 65.8, and a median household income of $108,125).

The response examines how the demographics have historically evolved (e.g., the town experienced a decline in popularity in the mid-20th century, worsened by the departure of the National Guard in 1933 and the effects of World War II, but eventually transitioned from a historic industrial and recreational area to a modern residential community with a rural character).

The response discusses the Tabernacle building and its purpose as a 700-seat venue that often hosts musical events and summer gatherings (e.g., Mount Gretna Bible Festival).

The response discusses the encampment of the Pennsylvania National Guard at Mount Gretna, from 1885 to 1935.

The response discusses Mount Gretna as a summer arts destination.

The response discusses the Mount Gretna Playhouse, listing at least two shows performed there (e.g., Whispers in the Dark, Murder Akerton Manor, etc.).

The response discusses the Mount Gretna Outdoor Art Show, founded in 1975, which is one of the 200 best art gallery shows in the country and features a wide selection of works in a scenic outdoor setting.

The response describes how a picnic ground was converted into a 2-foot narrow-gauge railroad.

The response discusses the creation and history of the Mount Gretna Lake (aka Lake Conewago) in the 19th century by damming the Conewago Creek at Mt. Gretna.

The response mentions "Kauffman's Park", a small amusement park built by the founder in 1926 that later was renamed "Laurel Park" and its prime attraction the "Million Gallon Pool", advertised as one of the largest and cleanest pools in the state.

The response discusses the "Governor Dick Tower", including its history and namesake.

The response discusses the history of the "Mount Gretna Roller Rink", constructed 1890.

The response discusses the Conewago Hotel (built in 1909-1940).

The response discusses the Chautauqua Inn (built in 1892-1970).

The response discusses Kauffman’s Hotel and Store, founded as part of the town’s initial construction.

The response discusses The Jigger Shop, a sweet and dessert shop that has operated since the town's founding in 1895.

The response mentions at least 3 other Mount Gretna businesses (e.g., the Hide-A-Way, The Mt. Gretna Timbers, Porch & Pantry, La Cigale, Mount Gretna Pizzeria).

The response mentions that Mount Gretna is located in Lebanon county.

The response mentions that Mount Gretna lies within both West Cornwall and South Londonderry Townships.

The response mentions that the current Mayor of Mount Gretna Borough is Joseph Shay.

The response includes a distinct section heading titled “Founding” that separately discusses the founding of the town.

The response covers each of the eight subject areas explicitly requested in the prompt (founding, subdivisions, cultural history, religious history, military history, status as summer recreation site, attractions, businesses).

The response identifies the governing authority for each subdivision (e.g., Mount Gretna Borough has a mayor, Chautauqua has a Board of Managers that are elected by stockholders, Campmeeting also has a Board of Managers, Timber Hills belongs to South Londonderry Township, etc.)

The response describes the Chautaqua's associated buildings (e.g., Mount Gretna Playhouse, Hall of Philosophy).

The response discusses the Chautauqua's mission as a center for arts and education.

The response mentions the Mt. Gretna Hide-A-Way as a historic local business present since the founding of the town.

The response discusses the changes in military presence in the area (e.g., the outgrowing of the National Guard, the use of military police in towns, the training of soldiers at the state military encampment, the 1929 State Military Commission).