Prompt: Give me a detailed report on the recent Air India crash in India of AI171 heading from Ahmedabad to London. Include the most up-to-date information from news channels. Also provide me with an elaborate analysis for possible reasons of the crash and provide recommendations on how such an incident could be avoided in the future.

Response contains a clearly dated time-stamp such as “As of YYYY-MM-DD” or similar within the first two paragraphs.

Response cites (by name) at least three news channels it consulted (e.g., Al Jazeera, BBC, NDTV, Newslaundry).

Response includes separate labeled sections (e.g., “Verified Information” and “Speculative Analysis”) that distinguish confirmed facts from hypotheses.

The response uses language such as 'could,' 'might,' 'hypothetically,' for unconfirmed hypotheses and scenarios.

The response discusses at least three initial and prominent theories regarding reasons for the crash (fuel contamination, manual pilot actions, software malfunction).

The response includes a comparison of this crash with other incidents involving the Boeing 787 dreamliner (e.g., JAL 787 fuel leakage, EAL 787 battery fire, Norwegian Air Shuttle 878 fuel leak, United Airlines Flight 613 IRU malfunction).

The response provides recommendation not sourced from professional organizations (e.g., AAIB, DGCA, Boeing, FAA, U.S. NTSB).

The response includes specific casualty and injury counts (i.e., all 12 crew members and 229 of the 230 passengers aboard died, on the ground, 19 people were killed and 67 others were seriously injured).

The response includes at least three quoted statements by aviation experts (e.g., Miles O'Brien, Sam Thomas, John Cox, Shawn Pruchnicki, Ram Mohan Naidu).

The response mentions the deployment of the Ram Air Turbine (RAT) moments before the crash, which typically occurs in the case of the aircraft losing power, signaling a catastrophic engine failure

The response includes a statement from the lone surviving passenger, Viswashkumar Ramesh, who explained that he escaped the wreckage by walking through an emergency exit located by his seat.

The response contains a well-defined section titled along the lines of "Recommendations" that discusses how to avoid such an incident in the future.

The response provides at least one table comparing this accident to previous Boeing 787 crashes.

The response contains at least one table summarizing the possible causes for the accident.

The response frames social media posts or speculative content from non-experts (e.g. streamers, non-aviation pundits not directly quoting experts or official reports, TV news anchors, "anonymous" experts) as authoritative.

The response links the dual engine loss of thrust to the fuel control switches being moved from RUN to CUTOFF.

The response indicates that AAIB investigation indicated that the fuel quality was satisfactory.

The response indicates that the AAIB report did not identify any mechanical failures/faults.

The response correctly links that Air India CEO's Campbell Wilson comments cautioning against "premature conclusions" is in regards to pilot error.

The report correctly deduces that pilot error is currently considered the most likely theory and should not be considered equally as likely with other potential causes (mechanical failure, software failure, weather issues, engine ingestion of debris).

The report contends that previous issues with the Boeing 787 Dreamliner (e.g., fuel leakage, lithium-ion battery problems, fire extinguisher faults, IRU malfunctions) are considered as potential causes of the crash.

The report mentions that the ongoing investigation by the AAIB is not complete.

At least one press release from the official government source of the Civil Aviation Ministry is referenced.

At least 1 press release from Air India is mentioned.


Prompt: Write a blog post on the distinguishing difference between research in human interaction via Network Physicalization vs. Haptic Feedback in AR/VR. Do a thorough analysis on what the pros and cons of each method is and give a table with literature review on the research done on the topic. Give a grading from 1 to 5 for each on important metrics like "Well-Research History", "Resourcefulness", "Practicallity", "Accessibility for Researchers", "Accessibility for Common Users"

The response clearly defines Network Physicalization, which is the practice of turning abstract network data in to tangible, physical models, and explains how it makes complex systems more intuitive (e.g. physical graphs, 3D prints, string-and-peg layouts, LEGO-based structures).

The response clearly defines Haptic Feedback in AR/VR, which is the use of tactile technologies to simulate the sensation of touch in virtual or augmented environments, and illustrates how immersion is enhanced (e.g. vibration motors in controllers, wearable haptic gloves, full-body haptic suits, ultrasonic mid-air haptics).

The response provides a side-by-side comparison of the similarities and differences between Haptic Feedback in AR/VR and Network Physicalization, presented in a clear graphical format (e.g. comparison table, matrix chart, feature grid, infographic).

The response discusses at least three clear advantages of Network Physicalization for Human Interaction and cites corresponding sources (e.g. fundamental sensory feedback, ease to showcase, fostering collaborative exploration, supporting embodied cognition).

The response discusses at least three disadvantages of Network Physicalization for human interaction and cites corresponding sources (e.g. poor scalability to large dataset, high cost and production costs, limited flexibility with frequently changing or dynamic data, difficulties in remote or distributed collaboration).

The response discusses at least three advantages of using Haptic Feedback in AR/VR for human interaction and cites corresponding sources (e.g. increasing immersion and presence in virtual environments, improving motor learning and training outcomes, enhancing communication through non-verbal touch cues, enabling realistic simulation for healthcare or skill training).

The response presents a comprehensive analysis of at least three disadvantages of using Haptic Feedback for human interaction in AR/VR (e.g. high cost and hardware complexity, technical fragility and calibration issues, user errors from imprecise feedback, physical fatigue from prolonged device use, and cognitive overload during long interactive sessions).

The response presents the analysis of pros and cons for Network Physicalization v.s. Haptic Feedback in AR/VR in human interaction in a clear visual format (e.g. comparison tables, bar charts, matrix diagrams, annotated figures).

The response contains a literature review table that consists of at least five distinct research papers for each Network Physicalization and Haptic Feedback in AR/VR topic, totaling a minimum of ten entries, and the table's columns clearly list the titles of the referenced papers, their authors, their sources, which of the two topics the papers discuss, and a brief summary of the papers.

The response contains research papers that discuss topics irrelevant to both Network Physicalization and Haptic Feedback in AR/VR for human interaction.

The response includes gradings for both Network Physicalization and Haptic Feedback on the specified metrics (‘Well-Research History,’ ‘Resourcefulness,’ ‘Practicability,’ ‘Accessibility for Researchers,’ and ‘Accessibility for Common Users’) using a numerical scale, and explains what the scale represents.

The response provides a brief justification for each grading score on the specified metrics (‘Well-Research History,’ ‘Resourcefulness,’ ‘Practicability,’ ‘Accessibility for Researchers,’ and ‘Accessibility for Common Users’) so that the analysis clearly supports the assigned ratings.

The response discusses the range of tools, methods, and resources Network Physicalization requires (e.g. reliance on fabrication materials, availability of 3D printing, need for workshop facilities, access to specialized hardware) when evaluating its 'Resourcefulness'.

The response accurately reflects that Haptic Feedback has a longer and more extensive research history while Network Physicalization is a newer and less established field, when discussing grading for ‘Well-Research History’.

The response includes opening paragraphs that clearly states the blog will analyze the distinguishing differences between research in Network Physicalization and Haptic Feedback in AR/VR, including their pros, cons, and supporting literature.

The response mentions that the discussion about the roles and effectiveness of Network Physicalization and Haptic Feedback in Human Interaction is still an ongoing debate within the Human-Computer Interaction research community.

The response cites sources in the literature review that prioritizes works from top-tier Human-Computer Interaction (HCI) and Virtual Reality (VR) venues such as the ACM Conference on Human Factors in Computing Systems (CHI) and the IEEE Conference on Virtual Reality and 3D User Interfaces (VR).

The response includes an analysis of technical complexity or implementation difficulty as either a pro or a con for at least one of the two methods, Network Physicalization and Haptic Feedback in AR/VR.

The response discusses how both Network Physicalization and Haptic Feedback in AR/VR contribute to deeper immersion, more intuitive interactions, and enhanced sensory guidance through physical or tactile contact.

The response discusses the similarities between Network Physicalization and Haptic Feedback in AR/VR (e.g. engaging users through physical interaction, enhancing understanding via embodied experiences, requiring specialized hardware, supporting collaborative exploration).

The response discusses the differences between Network Physicalization and Haptic Feedback in AR/VR (e.g. Network Physicalization creates static or tangible models while Haptics simulates dynamic touch, Physicalization often requires fabrication materials while Haptics relies on electronic devices, Physicalization supports co-located group analysis while Haptics is focused on individual immersive experiences, Physicalization emphasizes external shared artifacts while Haptics emphasizes embodied sensory perception).

The response includes a high-level discussion of the main takeaways from the literature review, summarizing key trends, insights, or gaps across research on Network Physicalization and Haptic Feedback in AR/VR.

The response presents the grading results for both Network Physicalization and Haptic Feedback in a clear table format, showing side-by-side scores for all specified metrics: ‘Well-Research History,’ ‘Resourcefulness,’ ‘Practicability,’ ‘Accessibility for Researchers,’ and ‘Accessibility for Common Users.’

The response includes a discussion on the specified metrics (‘Well-Research History,’ ‘Resourcefulness,’ ‘Practicability,’ ‘Accessibility for Researchers,’ and ‘Accessibility for Common Users’ ) that gives an overview of how the grading system is applied.


Prompt: Write an explanatory article comparing and contrasting Support Vector Machine and Logistic Regression.

The response contains at least four explicit sentences or bullets describing similarities shared by Support Vector Machine (SVM) and Logistic Regression (e.g. both are supervised learning algorithms for classification, both are linear classifiers, can handle non-linear data, uses regularization).

The response contains at least four explicit sentences or bullets describing differences between Support Vector Machine (SVM) and Logistic Regression (e.g. SVM maximizes margin while Logistic Regression maximizes likelihood, SVM is geometric while Logistic Regression is statistical, SVM is robust to outliers while Logistic Regression is more sensitive).

The response includes executable code snippets as examples of how to use Support Vector Machine or Logistic Regression (e.g. setup codes, library imports, example usage).

The response correctly states that Support Vector Machine constructs a decision boundary or hyperplane that maximizes the margin between classes, and how support vectors define the SVM model.

The response correctly states that Logistic Regression models class probability with the logistic sigmoid function or log-odds formulation.

The response correctly states that Support Vector Machines can handle non-linear relationships by using kernel functions (e.g. linear kernel, Radial Basis Function(RBF), polynomial, sigmoid).

The response correctly explains the difference between hard-margin and soft-margin Support Vector Machines (e.g. hard-margin SVM requires perfect separation with no errors; soft-margin SVM allows some misclassifications to improve generalization).

The response correctly states that the standard Logistic Regression assumes a linear decision boundary in the feature space unless features are transformed.

The response correctly states that Support Vector Machine produces label predictions, not probabilities by default.

The response explains the derivation of the objective function for Support Vector Machine, which includes hinge loss and regularization parameters.

The response explains the derivation of the Logistic Regression from Gaussian Naive Bayes.

The response mentions that Support Vector Machine is a quadratic programming problem.

The response contains the formula for Support Vector Machine's dual formulation.

The response discusses at least five distinct dimensions of comparison for Support Vector Machine and Logistic Regression (e.g. mathematical objective, decision boundary shape, interpretability, computational complexity, robustness to outliers, scalability, regularization, typical data‐set size/shape).

The response provides at least one practical scenario where Support Vector Machine is preferable, justified by its attributes (e.g. effective in high-dimensional spaces, robust to overfitting/outlier, applicable to small dataset).

The response provides at least one practical scenario where Logistic Regression is preferable, justified by its attributes (e.g. simplicity, computationally efficient, effective for binary classification).

The response describes practical use-case scenarios for Support Vector Machine based on its properties (e.g. bioinformatics, handwriting recognition, face recognition).

The response describes practical use-case scenarios for Logistic Regression based on its properties (e.g. fraud detection, spam detection, loan approval, disease prediction).

The response contains a summary of Support Vector Machine and Logistic Regression's comparison (e.g. synthesizing key similarities, differences, take-home messages).

The response compares Support Vector Machine and Logistic Regression's convexity properties in optimization problems.

The response explains the geometric intuition of the margin in Support Vector Machine.

The response discusses regularization techniques (e.g. the L1/L2 for Logistic Regression, C parameter in Support Vector Machine).

The response explains how gradient descent or related optimization methods are used to optimize loss functions for both Support Vector Machine and Logistic Regression.

The response includes a chart or table that compares Support Vector Machine and Logistic Regression across multiple dimensions in a condensed form (e.g. dimension, dataset size, computational efficiency, robustness to outliers).

The response incorporates a historical or theoretical context behind Logistic Regression and Support Vector Machine (e.g. origins of SVM in Vapnik-Chervonenkis theory, Logistic Regression in statistics).

The response compares model outputs in multi-class scenarios by introducing extensions like One-vs-All or Multinomial Logistic Regression.

The response explains how Support Vector Machine and Logistic Regression behave on imbalanced datasets (e.g. decision boundary skewing, loss function bias, probability threshold issues) and their mitigation techniques (e.g. class weights, sampling strategies).

The response discusses the impact of feature scaling on the Support Vector Machine and Logistic Regression (e.g. distance metrics, hyperplane skewing, gradient descent convergence, regularization impact).

Mathematical equations are displayed in clear mathematical notation like LaTex format.

The response discusses other alternative algorithms for classification (e.g. decision trees, k-nearest neighbors).


Prompt: I am an 18 year old that currently has ten thousand dollars. Write an investment plan to guide me through investing for the future. Specifically, the timespan of this investment plan should be considered to be the rest of my life. Furthermore, this investment plan should be researched with academic and/or authoritative sources. Essentially, I want to understand how a savvy investor would invest, given that they are starting with ten thousand dollars at the age of 18.

The response gives specific explanations of how to invest the original ten thousand dollars. Examples:
1) Allocate $6,000 into a low-cost S&P 500 index fund (VOO) because it historically averages 7–10% annual returns and provides broad U.S. market exposure, $2,000 into a high-yield savings account for emergency liquidity, and $2,000 into a Roth IRA for tax-free retirement growth.
2) Invest $5,000 in a total stock market ETF (VTI) to capture long-term global equity growth, $3,000 in a bond ETF (BND) to reduce portfolio volatility, and keep $2,000 as cash in a money market fund to cover unexpected expenses, ensuring both growth and safety.
3) Put the full $10,000 into a robo-advisor portfolio (e.g., Betterment at an 80/20 stock-to-bond allocation) because at age 18 the long horizon allows high equity exposure, while the bond allocation provides stability during downturns and reduces emotional risk.
4) Contribute $6,000 to a Roth IRA invested in a 2065 target-date fund because contributions grow tax-free over decades, then place $2,000 into a total international stock ETF (VXUS) for diversification, and $2,000 into a certificate of deposit (CD) for guaranteed safe returns.

The response mentions at least three asset categories (e.g., U.S. equities, international equities, bonds/cash). Examples:
1) The response suggests splitting the $10,000 into U.S. equities through an S&P 500 index fund, international equities through a total world ex-U.S. fund, and bonds through a U.S. bond ETF to balance growth and stability.
2) The response allocates money to U.S. stocks (growth engine), global emerging markets (to capture higher long-term returns), and cash or money market funds (for liquidity and emergencies).
3) The response highlights diversification across U.S. equities, international equities, and real assets like REITs or commodities, each with a distinct role in protecting against different economic conditions.
4) The response recommends a 3-way split: U.S. stock market fund for long-term appreciation, developed international market fund for geographic diversification, and government bonds or TIPS for inflation protection and lower volatility.

The response advises establishing a cash emergency fund of at least three months of living expenses before investing the remainder. Examples:
1) The response recommends keeping $3,000 in a high-yield savings account to cover three months of rent, food, and utilities, before investing the other $7,000 in index funds.
2) The response says to first calculate monthly expenses (e.g., $1,500/month), then set aside at least $4,500 in cash as an emergency fund, and only invest the surplus.
3) The response advises building a cash cushion equal to three to six months of expenses in a money market account or certificate of deposit, to avoid having to sell investments in a downturn.
4) The response highlights that emergencies like job loss or medical bills can derail investments, so it instructs to secure at least a three-month emergency fund in liquid cash before putting the rest into long-term assets.

The response suggests a long investment horizon (e.g., 40+ years). Examples:
1) The response emphasizes that at age 18, investing in U.S. equities for at least 40 years allows compound interest to work most effectively, turning small contributions into substantial wealth by retirement.
2) The response notes that a global stock index fund should be held for multiple decades, since short-term volatility is common, but over 40+ years, long-term trends historically overcome downturns.
3) The response highlights that bonds and other safer assets can be introduced gradually later in life, but for now, the focus should be on growth assets held with a long horizon of 40 to 50 years.
4) The response advises ignoring daily market fluctuations and instead sticking with a disciplined plan for at least four decades, because time in the market is a bigger driver of wealth than timing the market.

The response recommends using at least one U.S. tax-advantaged retirement account (e.g., Roth IRA, 401(k)). Examples:

1) The response suggests contributing $6,000 to a Roth IRA because investments grow tax-free for decades, which is especially valuable when starting at age 18.
2) The response advises using an employer-sponsored 401(k) plan and contributing enough to receive the full employer match, since this is essentially free money on top of tax-deferred growth.”
3) The response recommends maxing out contributions to a Roth IRA first, then using a traditional IRA if more money is available, in order to diversify future tax treatment.
4) The response highlights the strategy of contributing to both a 401(k) for immediate tax deductions and a Roth IRA for long-term tax-free withdrawals, balancing short- and long-term tax benefits.

The response uses at least three authoritative external sources (e.g., SEC.gov, Vanguard research, an academic paper). Examples:
1) The response cites an SEC.gov guide on mutual funds to explain the benefits of low-cost index investing and regulatory protections for investors.
2) The response references a Vanguard research whitepaper on the long-term performance of diversified portfolios, supporting the recommendation to invest in both U.S. and international equities.
3) The response includes findings from an academic paper in the Journal of Finance showing how asset allocation explains the majority of portfolio variance, strengthening the case for diversification.
4) The response quotes data from the Federal Reserve on historical inflation rates to justify why TIPS (Treasury Inflation-Protected Securities) or equities can protect purchasing power over decades.

The response includes any information which isn’t supported by an authoritative source (e.g., includes information from reddit). Examples:
1) The response tells the reader to ‘just buy whatever stocks people on Reddit’s WallStreetBets are hyping up,’ without citing any financial regulator, research paper, or professional investment guidance.
2) The response claims that ‘cryptocurrency always beats the stock market’ but provides no evidence from SEC.gov, academic finance journals, or established investment research firms.
3) The response suggests ‘my cousin made a fortune trading penny stocks, so you should too,’ relying on anecdotal evidence instead of published data or authoritative analysis.
4) The response points to a personal finance blog post with no references and treats it as fact, instead of drawing on official government, institutional, or peer-reviewed sources.

Response states an expected long-term equity return between 5% and 10% per year.

Response recommends primarily low-cost diversified American index funds or ETFs with an expense ratio of 0.20% or less (e.g, VTI, SPY).

Response recommends speculative practices (e.g day trading, margin, options, individual stocks) .

Response suggests investing greater than or equal to 15% of the portfolio in bonds.

Response includes guidance to rebalance the portfolio on a regular schedule of at least once per year.
Examples:
1) The response recommends rebalancing once per year by selling some stocks if they grow too large a share of the portfolio and reinvesting in bonds or cash, to maintain the original allocation.
2) The response advises setting a fixed annual date, such as every January, to rebalance back to an 80/20 stock-to-bond mix, regardless of how markets performed.
3) The response highlights that without yearly rebalancing, a portfolio could drift into excessive risk (e.g., from 70/30 to 90/10 stocks-to-bonds after a bull market), so annual adjustments keep risk in check.
4) The response suggests using an automated broker feature to rebalance yearly, ensuring allocations stay aligned with the investor’s age and risk tolerance, without the need for emotional decision-making.

Response suggests investing the initial $10,000 via dollar-cost averaging.

Response includes an educational or non-liability disclaimer (e.g., advises consulting a professional).

The response warns that tax rules and account availability vary by country. Examples:
1) The response notes that while a Roth IRA is available in the U.S. with tax-free growth, investors in Canada would need to use a TFSA or RRSP instead, since U.S. retirement accounts aren’t accessible.
2) The response cautions that capital gains and dividend tax rates differ across countries, so advice about U.S. tax efficiency may not apply elsewhere.
3) The response points out that employer-sponsored 401(k) plans are unique to the U.S., and workers abroad may need to rely on government pension schemes or private retirement accounts.”
4) The response includes a disclaimer that before opening accounts or investing, the reader should check local tax laws and account options, since what is advantageous in one jurisdiction may not exist in another.

The response notes that investments depend on at least one characteristic of the user (e.g the response says: if you are more risk averse, you might want to invest __, else __). Examples:
1) If you are more risk averse, you may prefer a higher bond allocation (e.g., 40% bonds, 60% stocks) to reduce volatility. If you are comfortable with risk, you could hold 90% stocks and 10% bonds for greater long-term growth.
2) The response points out that if you have short-term goals (like buying a house in 5 years), more money should stay in cash or CDs. If your goals are long-term (retirement in 40+ years), then a heavy stock allocation makes sense.
3) The response highlights that if you have a stable income and strong job security, you can afford to take more investment risk. If your income is uncertain or variable, a more conservative allocation with a larger emergency fund is safer.
4) The response explains that if you panic easily when markets drop, you should hold more bonds and cash to avoid emotional selling. If you can stay calm through downturns, then higher stock exposure is more appropriate.

The response outlines a plan for ongoing future contributions. Examples:
1) The response recommends contributing 10–15% of every paycheck into investments, automatically directed to index funds, to build wealth steadily over time.
2) The response advises maxing out a Roth IRA each year (currently $6,500) and then investing any extra savings in a taxable brokerage account.
3) The response suggests increasing contributions as income rises - for example, starting at $200/month as a student, then raising it to $500/month after landing a full-time job.
4) The response highlights the power of dollar-cost averaging, recommending fixed monthly contributions (e.g., $300/month) regardless of market conditions to smooth out volatility.

Response suggests shifting to a higher bond or lower-risk allocation as the user ages.

The response avoids unexplained financial jargon; any technical term used is briefly defined or obvious to a novice investor. Examples:
1) ETFs (Exchange-Traded Funds) - ETFs are baskets of stocks you can buy like a single stock.
Why invest: Diversification across many companies, usually with very low fees. Good for long-term growth.
Risks: Value can still drop in market downturns; some ETFs track niche sectors that may be volatile.
When not suitable: If you need guaranteed returns (e.g., short-term goals) or are prone to panic-selling during downturns.

2) Roth IRA - A Roth IRA is a retirement account where your investments grow tax-free.
Why invest: Huge tax advantage if you expect to be in a higher tax bracket later; decades of compounding without tax drag.
Risks: Contributions are limited ($6,500 per year as of 2025), and investments inside are still subject to market risk.
When not suitable: If you’ll need the money before retirement age (59½) or your income is too high to contribute directly.

3) Asset Allocation - Asset allocation means how you split your money across stocks, bonds, and cash to balance growth and safety.”
Why invest: Provides risk management; younger investors can take more stock exposure for growth, while older investors shift toward bonds/cash for stability.
Risks: Too aggressive (stocks-heavy) exposes you to big downturns; too conservative (cash-heavy) may not keep up with inflation.
When not suitable: If you ignore your personal risk tolerance and blindly follow a generic allocation without adjustments.

4) U.S. Stock Market Fund (instead of ‘equity index tracking vehicle’) - A U.S. stock market fund lets you own a small piece of hundreds of American companies.”
Why invest: Historically strong long-term returns; simple and effective way to build wealth over decades.
Risks: Short-term volatility; heavy U.S. exposure may limit international diversification.
When not suitable: If you’ll need to withdraw money in the next few years or want more global exposure.

The response includes any content not explicitly related to investing. Examples:
1) The response goes into a long discussion about career choices at age 18, such as whether to attend college, pursue a trade, or take a gap year, without tying these decisions back to how to invest the $10,000.
2) The response offers general life advice about health, relationships, or personal development (e.g., exercising, making friends, or avoiding stress) without linking it to the investment strategy.
3) The response provides commentary on political ideologies or government policies (e.g., criticizing tax laws or debating fiscal policy) that do not directly connect to the investment plan.
4) The response shifts focus to unrelated financial products like car loans, mortgages, or budgeting apps, without showing how they influence or complement the investment of the initial $10,000.

The response identifies key risks (market, inflation, sequence-of-returns). Examples:
1) The response notes that market risk is unavoidable since stocks can lose value during downturns, and long-term diversification is necessary to manage this volatility.
2) The response highlights inflation risk, explaining that cash loses purchasing power over decades if it is not invested in assets that grow faster than inflation.
3) The response identifies sequence-of-returns risk, particularly in retirement, where withdrawing money during a market downturn can permanently reduce the sustainability of a portfolio.
4) The response recognizes that inflation and market risk often interact—such as when high inflation triggers higher interest rates, causing both bond and stock values to fall simultaneously.

The response describes concrete strategies to mitigate risks such as inflation.
Examples:
1) Invest a portion of the portfolio in Treasury Inflation-Protected Securities (TIPS), since their principal value rises with inflation, protecting purchasing power.
2) Maintain long-term exposure to equities through index funds like the S&P 500, as stocks historically outpace inflation over multi-decade horizons.
3) Allocate some funds to real assets such as REITs (real estate investment trusts) or commodities like gold, which tend to hold value or appreciate when inflation rises.
4) Keep emergency savings in high-yield savings accounts or short-term certificates of deposit (CDs) that adjust rates more quickly than traditional accounts, reducing erosion from inflation.


Prompt: write a series of blog posts on important combinatorics topics in math olympiads. Include intuitive and detailed explanations, as well as example problems from top olympiads such as USAMO, USA TSTST, CMO, IMO, IMO shortlist, etc. Provide correct solutions for those problems and the insight or takeaways from those solutions.

The series covers at least three distinct topics from the following list: Enumerative combinatorics (e.g. generating functions, inclusion-exclusion, combination, permutations), Graph Theory (e.g. Ramsey theory, planar graphs, graph coloring, domino tiling), or Probabilistic Methods (e.g. random graphs, sum-free sets, local lemma, deterministic theorems).

Each post includes an intuitive and detailed explanation of its core combinatorics topic (e.g. providing baseline definitions, using analogies, leveraging storytelling, visual explanations).

Formal mathematical definitions and theorems are presented accurately for each topic (e.g. structured presentation, proper notation, formatting).

All example problems are clearly labeled with their originating competition and year (e.g. USAMO 2021 P3, IMO 2021 P2, 2015 CMO P4, 2012 USAMO P3).

Correct and complete solutions with detailed thought process and reasoning behind each step are provided for every example problem.

For at least one solved problem, the insights section describes how the core technique is adapted to solve a problem from a different mathematical domain (e.g. applying a graph theory concept to a number theory problem, using polynomial roots and properties of algebraic equations on geometric problems, using properties of prime numbers to define relationships between vertices, using concepts from real or complex analysis on distribution of prime numbers) and explicitly outlines the necessary modifications.

LaTeX formatting is used for all mathematical expressions and equations within the text.

Each blog post is written in blog style; it starts with an introduction capturing reader's attention and describes key concepts that are essential for solving example problems.

Each blog post articulates at least two distinct problem-solving heuristics (e.g. consider extreme cases and look for variants, draw a diagram and visualize, work backward from the goal, solve by contradiction) and for each, it not only explains its application in the given solution but also describes the potential pitfalls or common misapplications of that heuristic.

Each blog post contains at least two example problems, and all examples are selected from top math olympiads (e.g. USAMO, USA TSTST, CMO, IMO, IMO Shortlist).

The response includes visual aids (e.g. diagram, table, graph, chart) if the topic naturally lends itself to it (e.g. graph theory, decision trees, catalan numbers, venn diagrams).

Each blog post explicitly states the assumed prerequisite mathematical knowledge (e.g. modular arithmetic for complex Pigeonhole Principle problems, counting principles and factorials for permutation problems).

Alternative solution approaches or viewpoints are discussed for at least one example problem (e.g. solving a combinatorial problem with 1. inclusion-exclusion principle and 2. complementary counting principle).

Underlying mathematical principles that are beyond the core topic are explicitly identified if relevant (e.g. discussing how set theory can be used for inclusion-exclusion principle, stating use of recursion for catalan number problems).

The series of posts demonstrates a clear pedagogical progression, where concepts from earlier posts are explicitly referenced and built upon in later posts to solve more complex, multi-part problems (e.g. using an inclusion-exclusion argument on a structure defined by a graph, applying linearity of expectation to find the expected size of a sum-free set, combining local lemma with a random graph, employing a geometric interpretation of algebraic roots).

The response justifies why the chosen topics are foundational for olympiad-level problem solving (e.g. appears frequently, conceptually simple yet requires exceptional creativity).

Each post discusses key variations of principles used within the topic (e.g. Pigeonhole general form, strong form, probabilistic inclusion-exclusion, incusion-exclusion on multisets).

Each blog post contains at least two insights or takeaways from example problems and solutions (e.g. type of problems that appear, key techniques used, strategies often used to tackle problems, ways to think about alternative solutions).

The response contains insights and takeaways for all examples, not per each topic or each example.

The response contains a topic that is not covered by the math olympiad competitions (e.g. advanced calculus, topology, abstract algebra, category theory).


Prompt:
I want to make an ai system with a few bots that work together to make personalized learning paths for students

i'm thinking we need two bots. a 'Content bot' that finds lessons based on what a student is bad at, and a 'Coach bot' that gives feedback and practice quizzes. they need to look at data like quiz results, time spent on a page, maybe their learning style? and we need to figure out how they work together. eg when the content bot changes the student's path, how does the coach bot know to give them a remedial quiz or something

describe the main logic. it doesn't have to be super complex. just a simple rule for how they adapt over time. For exmaple 'if student quiz score < 50%, content bot finds an easier video'

how do i even know if it works? I need a couple metrics (eg improving quiz scores or more people finish the course). and how can I test it? maybe a simulation or just try with a small class. I also need a plan for when it gives a bad recommendation, and the student's scores go down? and how can a human teacher step in and take over/override it?

The response specifically mentions 2 AI agents with unique names and explicitly describes their individual role in personalizing online learning pathways for students (e.g. 'Content Agent' that curates lessons based on skill gaps and 'Coach Agent' that gives gives feedback and practice quizzes; 'Assessment Agent' that designs formative tests and 'Resource Agent' that fetches external videos, articles, or simulations aligned with the curriculum; 'Pathway Agent' that sequences courses according to mastery level and 'Motivation Agent' that sends encouragement messages and adaptive challenges; 'Insight Agent' that analyzes the patterns in student performance and 'Guardian Agent' that monitors metrics and recommends intervention').

The response specifies the type and format of the input data that each agent accepts to perform its role (e.g. the 'Content Agent' accepts quiz results in CSV format, diagnostic test scores in JSON format, and practice quiz interaction logs in TSV format; the 'Coach Agent' accepts participation tracking logs in Excel format, student reflections in TXT format, curriculum standards mapping in XML format, and learning objectives in CSV format).

The response describes the process how the agents interact by sharing data (e.g. the 'Coach Agent' sends quiz summaries in JSON format, the 'Content Agent' uses them to align lessons with skill gaps; the 'Assessment Agent' shares test analytics in CSV format, the 'Resource Agent' fetches targeted videos and articles; the 'Pathway Agent' provides updated course sequencing in JSON format, the 'Motivation Agent' adapts encouragement messages; the 'Insight Agent' highlights performance trends in Excel format, the 'Guardian Agent' recommends teacher intervention).

The response specifies a condition that triggers one agent to update a student's learning pathway and describes the agent's reactive behavior (e.g. the 'Content Agent' identifies low participation logs and implements attention checks; the 'Content Agent' maps new skill gaps from diagnostic and restructures the lessons order; the 'Content Agent' detects misalignment with curriculum standards and replaces outdated resources; the 'Content Agent' observes incomplete engagement with assigned readings and substitutes shorter, scaffolded materials).

The response specifies a condition that triggers the second agent to remedial exercises when performance is poor (e.g. the 'Coach Agent' detects frequent mistakes in reflection assignments and assigns targeted practice activities; the 'Coach Agent' identifies consistently low scores against learning objectives and delivers scaffolded exercises; the 'Coach Agent' observes repeated failures in applied tasks and introduces step-by-step practice modules; the 'Coach Agent' flags low accuracy in diagnostic responses and generates tailored remedial drills).

The response describes the rules or algorithms to update each of the agents over time and provides its justification for this choice (e.g. the 'Content Agent' updates its lesson recommendations to achieve better alignment with each student’s skill gaps using algorithms such as LinUCB-Linear Upper Confidence Bound, Thompson Sampling, Bayesian Knowledge Tracing, or Q-learning; the 'Coach Agent'' updates its practice quiz recommendations to improve student engagement Bayesian Knowledge Tracing, Item Response Theory, Reinforcement Learning, or Decision Trees).

The response explicitly names two evaluation metrics and explains their relevance to the goal of improving personalized online learning (e.g. quiz score improvement, course completion rate, time-to-mastery, student satisfaction, engagement minutes).

The response describes a procedure to perform a pilot using the two agents for a specified number of a small group (<=30 students) that include the chosen evaluation metrics.

The response describes a mechanism to detect incorrect recommendations (e.g. a drop in 3 consecutive quiz scores, unusually long completion time, repeated requests for hints, no improvement in specific skill categories after multiple lessons) and how to notify the human instructor for intervention (e.g. dashboard alert, email or in-app notification, pop-up override option for instructor to pick alternative intervention, weekly/daily review report).

The response describes technical requirements or constraints relevant to the runtime deployment of the system (e.g. latency of recommendation like duration of time taken to generate new lessons, scalability limit as in how many students the system can support, interoperability as in which LMS/Learning Management System or API this system can integrate with, hardware requirements like number and type of CPU/GPU necessary).

The response includes an analysis of cost relevant to the implementation and/or maintenance of the system (e.g. the budget required for minimum hardware infrastructure, cost of human engineers for maintenance, potential license fees for necessary software, cloud hosting or storage expenses).

The response lists detailed requirements for infrastructure relevant to the implementation and deployment of the system (e.g. Amazon Web Services S3/GCP to storage lesson plans and student data, NVIDIA GPUs T4 to train the agents, broadband network of >=10 Mbps with firewall security, ELK(Elasticsearch, Logstash, Kibana) stack for event tracking and audits).

The response includes at least one graphical illustration (e.g. flowchart, diagram, user interface mockup, system architecture diagram) to aid clarity and support understanding of the system’s design and operation.

The response mentions specific multi-agent frameworks or libraries useful to the development and maintenance of the agents (e.g. AutoGen for coordinating conversational agents, Rasa for natural language dialogue management, Ray RLlib for reinforcement learning and multi-agent training, Microsoft Bonsai for managing and deploying multi-agent systems).

The response discusses procedures to protect the privacy of student data with respect to a specified policy or regulation (e.g. for FERPA compliance, restrict access to student records to authorized staff, anonymize performance data before analysis, implement secure authentication with role-based permissions, maintain audit logs of all record access or modifications).

The response discusses measures to be compliant with local or regional policy related to education (e.g. GDPR for data protection in the EU, FERPA for student records in the U.S., COPPA for online services directed at children under 13, HIPAA for handling health-related accommodations, state-level regulations such as California’s CCPA).

The response uses names for the agents that are not descriptive of their stated goals.

The response includes practical tips on how to implement the rule or algorithm used to adjust recommendations over time (e.g. external sources for additional literature or tutorials of those techniques, useful GitHub repository or libraries, a sample code block for implementation, a block of relevant pseudocode).

The response describes procedures to ensure fairness and avoid bias in recommendations (e.g. auditing performance across demographic subgroups, balancing training datasets, applying bias-detection tools, allowing teacher override for equity adjustments).

The response discusses mechanisms for transparency and explainability of the agentic system (e.g. providing rationale for each recommendation, surfacing uncertainty scores, generating teacher-facing summaries, and logging decision factors for audit).

The response specifies requirements for user training and support (e.g. onboarding guides for teachers, student tutorials on using the platform, helpdesk or chatbot support, periodic professional development workshops).

The response describes measures for robustness and fault tolerance (e.g. fallback rules when agents fail, auto-retry mechanisms, offline mode for poor connectivity, redundant backups of critical data).


Prompt: Write an introduction on Markov chains in the field of statistics/combinatorics and the reason behind their genesis. Then, write a literature review on Markov chains in contemporary research. Include one section devoted to their application and intuition in solving problems like "what is the expected value of flips to get N heads in a row".

The response includes sections for introduction of Markov chain, applications of Markov Chain, and intuition behind N heads problem.

The response provides an accurate definition of Markov chain which is a stochastic process describing a sequence of possible events in which the probability of each event depends only on the previous states.

The introduction mentions that Andrey Markov invented Markov chains in the early 1900s.

The introduction references Markov's 1913 work on analysis of the sequence of vowels and consonants in Alexander Pushkin's novel, Eugene Onigin.

The response discusses at least three distinct modern research areas (e.g. Markov Chain Monte Carlo, Markov Decision Processes/Reinforcement Learning, Mixing Times/Random Walks on Graphs, Queuing Theory, Computational Biology, Finance, Statistical Physics).

All citations are in an official format (e.g. Modern Language Association, American Psychological Association, Chicago, IEEE).

The response includes an intuitive explanation of how to solve the coin-flip problem (e.g. description of states representing the current run length of consecutive heads (0 through N), a tail resets the run length to 0, example scenario where N is a certain number like 3).

The response states that for a fair coin, the expected number of flips needed to get N consecutive heads equals 2^{N+1} − 2 or an algebraically identical formula.

The response includes out of scope contents, such as  moderation policies, ethics statements, ethics in gambling, and other unrelated probability theorems.

The response discusses the reason behind the genesis of Markov chain which was either the need to model dependent events or extend law of large numbers to correlated trials.

The response includes tangential topics unrelated to Markov chains (e.g. differential equations, unrelated probability models, graph theory, number theory).

All mathematical statements and calculations are correct and aligns with basic probability facts.

The response discusses how Markov chains are used with linear algebra (e.g. transition matrix, eigenvalues, state probability vectors, time evolution).

The response discusses key properties related to Markov chains (e.g. memoryless, irreducibility, aperiodicity, ergodicity).

The response contains at least one problem similar to the coin-flip problem specified in the prompt.

The response includes mathematical foundations of the Markov chain (e.g. state space, transition matrix, transition probabilities, initial distribution, stationary distribution).

All mathematical equations are in LaTex format.

The response discusses at least one non-scientific domain where Markov chain is used such as philosophical dispute over dependence and free will.

The response discusses key variations of Markov chains (e.g. finite vs infinite state space, time-homogeneous, high-order chains, discrete-time vs continuous-time).

The response explains how first-step analysis is used to solve N heads in a row problem.


Prompt: Write a beginner-friendly technical guide explaining how Convolutional Neural Networks (CNNs) process visual data. Cover key ideas like convolution, kernels, feature maps, pooling, and why CNNs are effective for spatially structured input.

The response contains a dedicated section heading that includes the word "Convolution" (e.g., a line starting with "#" or "##" or ending with ":", or numbered like I, II, III).

The response explains that a kernel (also called a filter) is a small matrix of learnable weights that is applied to the input image.

The response defines a feature map as the collection of outputs produced by applying a kernel across all spatial locations of the input.

The response describes pooling (e.g., max or average pooling) as an operation that reduces the spatial dimensions of a feature map and its benefits and drawbacks (e.g., (1) Improves computation efficiency, (2) reduces the number of parameters, (3) reduces the risk of overfitting, (4) loss of information, (4) translation tolerance.)

The response states at least one reason that CNNs are effective for spatially structured data, specifically mentioning either (a) local receptive fields or (b) weight sharing.

The response contains any content not relevant to explaining CNN concepts (e.g., mentioning RNNs, Transformers, Diffusion, or LLMs).

The response includes diagrams and visualizations for the sliding window intuition, and also what a kernel looks like operating over input data in the form of matrices.

The response uses a technical guide format (e.g., includes bulleted or numbered lists) that is easy to digest for beginners and is ordered properly with easier and foundational concepts first, which is built on by the later response (e.g., explaining what a convolution is -> filters -> layers, such as pooling -> application for spatial data).

The response contains advanced or messy mathematical equations (e.g., formulas involving integral symbols (∫) or summation symbols (Σ) without a clear explanation of what the symbols mean).

The response defines convolution as a process in which the kernel values are multiplied element-wise with the underlying image patch and summed to produce a single output value.

The response includes a visual or textual explanation of how a convolution operation fails (e.g., aliasing, boundary artifacts, or sensitivity to orientation).

The response states that each kernel produces its own feature map.

The response contains at least one sentence that links the key operations in order—convolution (with kernels) → feature maps → pooling—demonstrating the overall data flow in a CNN.

The response includes a real-world analogy for convolution (e.g., (1) sliding a stencil, (2) applying a cookie cutter, (3) lawn mowing patch by patch, or (4) scanning with a flashlight).

The response discusses how the output feature map size is affected by edge padding, strides, and kernel size (e.g., without padding, the feature map becomes smaller than the input because the convolution process only outputs to the center of the kernel).

The response explains how multiple channels (e.g., RGB) are handled during convolution and how kernels adapt accordingly (e.g., each kernel produces an output, and for multiple channels, the kernel needs to expand in the dimension to cover all the channels).

The response briefly compares CNNs to fully connected networks in terms of parameter efficiency and overfitting risk (e.g., (1) Convolutional layers are more parameter-efficient than fully connected layers, (2) CNNs are better for spatial data such as images that exhibits locality and translation invariance, (3) CNNs has a lower risk of overfitting due to fewer parameters and regularization effects of local receptive field).

The response explains how multiple layers of convolution and pooling build hierarchical features (e.g., edges → textures → objects).

The response includes a concise code snippet (in any language) that manually performs a 2D convolution with nested loops (no libraries), to reinforce the sliding-window intuition.

The response touches on how architectural choices attempt to address how a convolution operation fails (e.g., limited receptive field, cannot handle long-range dependencies, inductive bias, pooling causes information loss, enable deeper networks by using residual or skip connections, dynamic convolution).

The response ensures all acronyms are expanded on it during their first appearance and provides clear definitions for all specialized terms and mathematical symbols upon first use, accompanied by explanatory prose or examples (e.g., (1) Convolutional Neural Network (CNN) is a type of feedforward neural network the optimizes the kernels to learn features, (2) Translation invariant means the CNN can learn to understand the shifts in the object positions in an image, (3) receptive fields are the area of the input that affects the neuron's output, (4) scale invariant means the CNN can learn to understand different sizes of the same object).


Prompt:
Consider the Korean company KCTech. The company is looking to enter the American market with its materials-selling business, as its equipment-selling business is not mature enough yet. The focus is on selling slurries, and the headquarters will be in Portland, Oregon. The initial team will be led by newly promoted USA branch CEO Dr. Sunghoon Lee, with experience at Intel, Global Foundries, SMARTPAD, Coherent, and Draper. He will be supported by one sales/research team member and two researchers. One of the main clients should be Intel, but there are certainly others on the table.

Please provide detailed, data-supported estimates of the U.S. branch's potential yearly revenue within its first few years (starting from late 2025), using comparable market data, industry benchmarks (average deal sizes in the Korean branch, market share assumptions), competitor analysis, and regional/national demand for semiconductor slurry materials. Include any numbers that you arrive at on the way to these estimates.

Additionally, provide any other useful context or strategic considerations that might help this branch succeed, such as customer acquisition strategies (divide into conventional strategies like sending samples and using personal connections, vs. more unexpected strategies), expected costs, regional competitors, or potential difficulties.

The response should provide explicit yearly revenue estimates for at least 5 consecutive years after U.S. market entry, with values expressed in realistic ranges and correct order of magnitude (e.g., tens of millions vs. billions). Figures should be specific (e.g., $20–30M) rather than vague, aligning with industry benchmarks and market context.

The response should clearly state at least 4 explicit numerical assumptions used to build the revenue estimates. Examples of valid assumptions include:
1) Average deal size with Intel or other major clients (e.g., $2–5M per year)
2) Expected market share captured in the U.S. within first 5 years (e.g., 2–3%)
3) Growth rate in semiconductor slurry demand (e.g., 8–10% CAGR)
4) Average price per liter/ton of slurry (e.g., $1,000–2,000 per ton)

The response should present at least 4 intermediate numerical calculations that clearly connect assumptions to the final revenue figures, without skipping steps. Examples of valid calculations include:
1) Estimating number of client deals (e.g., 3 Intel contracts + 2 smaller fab contracts)
2) Multiplying average deal size by number of deals (e.g., 5 × $3M = $15M)
3) Adjusting revenue for cost overheads of procuring slurry materials (e.g., 30% cost margin)
4) Comparing projected prices against competitors to account for undercutting (e.g., 10% price reduction → $2.7M instead of $3M per deal)

The response should cite or reference comparable data or benchmarks from KCTech’s Korean operations at least twice. Examples of valid benchmarks include:
1) Average slurry deal size
2) Historical revenue figures
3) Typical customer concentration (e.g., % revenue from top 3 clients)
4) Year-over-year revenue growth in Korea

The response should name at least four companies other than Intel as target customers for the U.S. branch. Representative examples include:
1) TSMC (Arizona)
2) Samsung Austin Semiconductor
3) GlobalFoundries (New York, Vermont)
4) Micron Technology (Idaho)

The response should provide competitor analysis by naming incumbent slurry suppliers active in the U.S. market.
Representative competitor companies include:
1) Cabot Microelectronics (CMC Materials)
2) Fujimi Corporation
3) DuPont Electronic Materials
4) BASF

Representative elements of competitor analysis include:
1) Market share held by each supplier in the U.S.
2) Pricing strategies and average slurry cost benchmarks
3) Strength of customer relationships with major fabs (e.g., Intel, TSMC, Micron)
4) Technological differentiation (e.g., particle size control, specialized slurries for advanced nodes)

The response should mention regional or national demand data by explicitly referencing at least 4 U.S. semiconductor manufacturing hubs. Representative regions include:
1) Oregon (Intel's fabs)
2) Arizona (TSMC fabs)
3) Texas (Samsung Austin)
4) New York (GlobalFoundries)

The demand data cited should include hard numerical ranges, such as:
1) Dollar value of contracts (e.g., $5-10M per year for a single fab contract)
2) Estimated number of contracts per year in each hub (e.g., 8-12 contracts)

The response should address the cost of opening a new U.S. branch by providing at least one numerical estimate, grounded in calculations of major cost factors. Representative cost factors include:
1) Office space and facility setup (e.g., lease in Portland, $200K-$400K annually)
2) Salaries for the initial team (CEO + 3 staff, e.g., $600K-$900K annually)
3) Equipment, lab, and R&D setup (e.g., $1M-$2M one-time)
4) Regulatory, legal, and administrative expenses (e.g., $100K-$250K annually)
The response should combine these into a clear estimated cost range (e.g., $2M-$3.5M for year one).

The response should list at least three tactics under the Conventional/Traditional customer-acquisition category. Representative examples include:
1) Sending free product samples to prospective clients
2) Leveraging existing personal/professional connections (e.g., former Intel colleagues)
3) Attending semiconductor trade shows and conferences
4) Direct sales outreach and relationship-building with procurement teams

The response should list at least three tactics under the Non-conventional/Unexpected customer-acquisition category. Representative examples include:
1) Partnering with U.S. universities or research labs for early-stage slurry testing
2) Running joint pilot projects with startups in semiconductor materials
3) Using digital marketing or thought-leadership campaigns targeting fab engineers
4) Offering unique financing/discount structures (e.g., pay-per-use slurry contracts)

The response should explicitly cite at least two sources in the Korean language. Representative examples of valid sources include:
1) Korean investing/finance websites
2) Government regulatory filings
3) Korean semiconductor industry news outlets
4) Company press releases in Korean

The response includes unnecessary references to post-COVID industry shifts, which are largely no longer relevant. The response mentions generic 'COVID impact' without tieing it to current, quantifiable 2024–2025 industry effects (e.g., ongoing supply chain bottlenecks or recovery metrics).

The response should provide confidence intervals (e.g., 95%) for every major numerical estimate, such as yearly revenue projections, market share assumptions, or cost estimates. The intervals should be expressed as ranges around the point estimate (e.g., projected revenue $20M with 95% CI: $18-22M).

The response should provide a SWOT analysis (or equivalent). At minimum, the Threats/Risks section must include at least 4 legitimate risks. Representative examples include:
1) Aggressive pricing or entrenched relationships from incumbent slurry suppliers
2) Supply chain vulnerabilities for critical raw materials
3) Slow customer adoption due to qualification cycles in semiconductor fabs
4) Regulatory or trade barriers affecting import of slurry materials into the U.S.

The response should use non-table charts at least once. Representative examples of valid non-table chart types include:
1) Line charts (e.g., revenue growth over years)
2) Bar charts (e.g., competitor market share comparison)
3) Pie charts (e.g., regional demand breakdown)
4) Scatter plots (e.g., deal size vs. number of contracts)

The response should reference KCTech’s existing global capabilities or limitations to justify its U.S. market strategy.
Global capabilities = strengths the company already has from international operations (e.g., production scale, R&D expertise, established client relationships).
Global limitations = constraints KCTech faces internationally (e.g., weaker brand recognition outside Korea, limited distribution channels, dependency on a few suppliers).
Market strategy = how these factors influence entry and growth in the U.S. (e.g., targeting Intel first, setting up in Portland, differentiating from competitors).

Representative examples include:
1) Strong R&D pipeline in advanced slurry formulations (capability)
2) Limited brand awareness in the U.S. semiconductor ecosystem (limitation)
3) Existing supply agreements with global clients like Samsung/GlobalFoundries (capability)
4) Reliance on imported raw materials that could face U.S. trade restrictions (limitation)

The response should consider hiring and talent acquisition as a key cost for setting up the U.S. branch. It must provide at least one numerical estimate (in USD) and break this into major cost components. Representative examples include:
1) Recruiter or headhunter fees (e.g., $20K-$50K per hire)
2) Interviewing and onboarding expenses (e.g., $5K-$10K per employee)
3) Salaries and benefits for initial team members (e.g., $150K-$250K annually for specialized researchers, $200K-$300K for leadership roles)
4) Ongoing HR/administrative support costs (e.g., $50K-$100K annually)
The response should combine these factors into a clear estimated range (e.g., $800K-$1.2M annually for personnel-related expenses in the first year).

The response should consider the potential for additional employees to be moved to the U.S. branch once sufficient revenue is generated, as it helps inform both future costs (e.g., relocation, salaries, visas) and potential revenue growth (e.g., expanded sales coverage, more R&D capacity).

The response should build at least one statistical or mathematical model to support revenue forecasting. A model can be a formal formula, calculation framework, or forecasting approach. Representative examples include:
1) Simple growth rate projection (e.g., 10% YoY revenue increase)
2) Exponential moving average for smoothing yearly revenue estimates
3) Linear regression using demand data and market size as predictors
4) Market share–based formula (e.g., Total U.S. slurry market x % share captured)

The response should not incorrectly attribute non-KCTech work, data, or achievements to KCTech.
Representative examples of incorrect attribution include:
1) Claiming KCTech developed slurry products that were actually created by Cabot Microelectronics (CMC Materials).
2) Attributing Intel's proprietary slurry R&D breakthroughs to KCTech.
3) Presenting Samsung or TSMC in-house slurry formulations as if they were supplied by KCTech.
4) Stating that KCTech currently holds U.S. market share figures that actually belong to competitors like DuPont or BASF.

Claims that KCTech USA Branch doesn't exist yet, and thus can't provide accurate information.

The response should cover potential difficulties the U.S. branch may face. Representative examples include:
1) Lengthy qualification and validation cycles for slurry products at U.S. fabs (12-24 months before adoption).
2) Intense competition and entrenched supplier relationships with incumbents (e.g., CMC Materials, DuPont).
3) Regulatory or trade restrictions that could delay imports or increase costs.
4) Challenges in recruiting specialized semiconductor materials talent in Portland and other hubs.


Prompt: Compose a concise technical report sketching an experimental plan for deciding whether a modern vision-language model could stand in for human specialists when labeling medical images of skin lesions.

The response discusses the primary objective, the chosen study design (e.g. model selection, multi-rater, paired non-inferiority trial, inter-rater agreements), the exact size of the human expert panel and test set, and the primary clinical success criterion (e.g. the lower bound of the 95% confidence interval for the accuracy difference exceeding -5%, sensitivity of at least 95% for melanoma detection, negative predictive value of 99% for ruling out malignant lesions).

The response's narrative structure shows a sophisticated flow, where the ethical considerations in the final sections are directly linked back to specific methodological choices made earlier (e.g. explaining how the multi-round Delphi consensus process from the Methods section mitigates the ethical risk of relying on a single-rater baseline, referencing how the test set can address ethical concern regarding bias against underrepresented skin, discussing model transparency and its impact on bias/fairness).

The response includes a glossary defining at least 5 key technical terms specific to the intersection of AI and medical diagnostics (e.g. non-inferiority margin, adjudication, Parameter-Efficient Fine-Tuning (PEFT), Fleiss Kappa, and Predetermined Change Control Plan (PCCP)).

The experimental objective is explicitly defined not only in terms of diagnostic substitution but also in terms of a secondary objective to evaluate the vision-language model's ability to generate descriptive text that identifies at least three specific, predefined morphological features (e.g. irregular pigment network, arborizing vessels, pearly border).

The response articulates a cost-effectiveness analysis which details the specific economic model to be used (e.g. cost-utility analysis, cost-benefit analysis, decision tree model) and the variables to be measured (e.g. cost per correct diagnosis, time saved by specialists, quality-adjusted life year).

The response discusses a dataset strategy that includes a plan for both external and temporal validation, specifying that the model will be tested on a held-out dataset from an institution not involved in training and on a set of images collected after the model's development was finalized.

The response specifies that ground-truth labels will be provided by board-certified dermatologists.

The response indicates inclusion of at least three lesion classes for classification (e.g. melanoma, nevus, basal cell carcinoma).

The response lists at least two evaluation metrics from the following: accuracy, sensitivity/recall, specificity, F1-score, AUROC.

The response proposes a statistical significance or non-inferiority test comparing the model and specialists (e.g. McNemar test, confidence intervals, paired t-test).

The response indicates the model and humans will annotate the same set of images for accurate comparison of model v.s. human performance.

The response provides a fail-safe or fallback procedure for model's low-confidence outputs (e.g. human-in-the-loop validation, defaulting to rule-based system, requesting additional input or clarification).

The response mentions conducting a sample-size calculation or power analysis to determine proper dataset size.

The response proposes at least two distinct interpretability methods and justifies their selection by tying them to the specific model architecture and the clinical need for transparency (e.g. proposing Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize lesion features for dermatologists, SHapley Additive exPlanations (SHAP) to quantify feature importance for model debugging, proposing Local Interpretable Model-agnostic Explanations (LIME) to understand specific cases, using Integrated Gradients to attribute outputs to specific image pixels and words).

The response explicitly mentions need for Institutional Review Board (IRB) or ethics board approval before the commencement of the study.

The response includes a patient privacy plan which specifies that a formal Business Associate Agreement (BAA) must be executed with any third-party cloud or AI service provider used for data processing, ensuring their contractual obligation to uphold Health Insurance Portability and Accountability Act (HIPAA) standards.

The response discusses at least one limitation of the vision language model in labeling medical images in reality (e.g. dataset bias, domain shift, small sample size, privacy issues).

The response includes any clinical treatment recommendations or diagnoses for patients.

The response cites nonexistent work, incorrect datasets, or made-up metrics (e.g. fabricated research papers, non-existent dataset, misrepresenting data distribution, inverted performance scores).

The response uses statistical methods incorrectly (e.g. applies unpaired test to paired setup, chi-squared test of independence for paired, dependent data, simple logistic regression for paired data structure).

The response discusses an adjudication process for establishing ground truth that details how qualitative disagreements in free text descriptions will be resolved, specifying a method for synthesizing a final consensus text that incorporates key features identified by the majority of agreeing experts.


Prompt: I am trying to recreate "Around the world in 80 days", I want to avoid planes but travel to as many countries and Islands as possible, I want to do it in a very low budget and want to start and end my journey in New York, USA. Assuming that I have a US passport, give me a detailed plan on how I can accomplish this.

The response addresses that the "Itinerary begins and ends in New York City"

The response should propose an itenary that must successfully complete a full circumnavigation of the globe, meaning the journey starts and ends in New York, USA, and includes traversing both the Eastern and Western Hemispheres. The circumnavigation should involve travel across all major oceans to demonstrate a complete global journey. Specifically, the traveler would cross:
1) The Atlantic Ocean (between the Americas and Europe/Africa)
2) The Indian Ocean (between Africa/Middle East and Asia)
3) The Pacific Ocean (between Asia/Australia and the Americas)

Examples of some valid Itineraries are:
1) USA ->  Portugal -> Egypt -> India -> Thailand -> Singapore -> Australia -> Japan -> USA
2) USA -> Spain -> Morocco -> Egypt -> India -> Vietnam -> Philippines -> Indonesia -> USA
3) USA -> Brazil -> South Africa -> India -> Malaysia -> China -> South Korea -> USA

The response must not include airplanes as part of the journey. If the response mentions air travel in any leg of the itinerary, this directly violates the prompt’s instruction to avoid planes. Examples of violations in the response:
1) “Fly from New York to London”
2) “Take a flight from Japan to the USA”
3) Optional short domestic flights are suggested

For each segment of the journey, the response should propose realistic, currently operational, and geographically appropriate modes of transport (e.g., trains, buses, ferries, ships). The modes must be feasible in the specific regions being traveled through, not just generic. Examples of Country/Region-Specific Transport:
1) Japan: Use of high-speed rail for long-distance travel between cities like Tokyo, Osaka, and Kyoto.
2) Europe: Use of intercity trains (e.g., Eurail, Eurostar, TGV) for cross-country travel, supplemented with local buses for shorter segments.
3) South America (e.g., crossing from Colombia to Panama): Use of boats/ferries where the Darién Gap prevents direct overland travel.
4) North America to Europe: Use of transatlantic cargo ships or passenger ferries as a realistic non-air option to cross the Atlantic Ocean.

The response proposes transport modes that are unrealistic, unavailable, or geographically impossible for the country/region.
Examples of Violations:
1) Suggesting a bus/train across the Atlantic or Pacific Ocean.
2) Proposing a direct rail line across the Sahara Desert.
3) Proposing nonexistent ferries (e.g., New York -> London).
4) Using defunct services (e.g., historic Orient Express).

The response should maximize the number of countries visited, but within a realistic and appropriate range for a global circumnavigation. An excessively small number (e.g., fewer than 8) would not reflect meaningful diversity, while an excessively large number (e.g., over 30-40) would be impractical for an 80-day journey. A good range is 10-15 countries, consistent with 12 countries in the original "Around the World in 80 Days" book.

Each leg of travel in the itinerary must include a specific departure and arrival day/time, tied to the overall timeline of the journey. This ensures the schedule is realistic, trackable, and aligned with an 80-day circumnavigation. For example:
"Arrives on Day 60 in San Jose, which is a Monday (if the journey started on a Sunday), at 12:00 pm local time (PST)."

The response must give a clear total budget in USD, realistic for a low-budget circumnavigation. For a solo 80-day trip, a reasonable budget is $25,000-$35,000, over $100,000 is excessive, and under $10,000 is unrealistic.

The itinerary should include clear per-day or per-week cost estimates across four major categories:
1) Transport (e.g., trains, buses, ferries) - ~$10-30/day
2) Lodging (e.g., hostels, guesthouses, budget hotels) - ~$15-40/night
3) Food (e.g., daily meals, groceries, street food) - ~$10-25/day
4) Visas/Other Fees (e.g., entry visas, park fees, local taxes, contingency costs) - ~$500-1000 total for 80 days

The response lists daily/weekly costs for transport, lodging, food, or visas/fees that are implausibly low or otherwise unrealistic for international travel.
For example
1) Claiming $1/night for lodging in major cities.
2) Suggesting $2/day for food in the US cities.
3) Listing $0 for visas/fees for China, where US citizens need visa
4) Providing costs that are wildly inconsistent with known budget ranges (e.g., $5/day total for an 80-day global trip).

The itinerary should address visa requirements for each destination, specifically considering how they apply to a U.S. passport holder. For example:
1) Visa-Free Entry: Japan allows U.S. citizens to stay up to 90 days without a visa.
2) Visa on Arrival: Nepal issues visas on arrival at Tribhuvan International Airport and border crossings.
3) E-Visa Required: India allows U.S. citizens to apply for an e-visa online before arrival.
4) Visa Application in Advance: Russia requires U.S. citizens to apply for a visa at a consulate before travel.
5) Entry Not Permitted:  North Korea generally does not permit U.S. passport holders entry (outside rare, restricted tours).

The itinerary should recommend rest days spread across the trip to allow for recovery, acclimatization, and realistic pacing. A good plan specifies ~3 rest days over the 80-day journey. It should avoid scenarios such as:
1) No rest days at all (continuous travel every day).
2) Rest days mentioned vaguely (“take breaks as needed”) without counting or scheduling them.

The itinerary must include backup plans for at least 3 important routes, showing what to do if travel is disrupted and how much longer it would take.
Representative Examples of Backup Plans:
1) If the train from Paris to London at 9:00 AM is canceled, take the 12:00 PM train, arriving 3 hours later than planned.
2) If the ferry from Athens to Santorini scheduled for 8:00 AM Monday is canceled due to weather, take the overnight bus to Santorini’s port instead, arriving the next morning (about 10 hours longer).

The itinerary should account for the minimum time needed to transfer between consecutive modes of transport, so that connections are realistic and not rushed. This includes the time to disembark, clear checks (if needed), and board the next mode.
Representative Examples of Connection Times:
1) Train -> Ferry: Arriving in Barcelona by train and boarding a ferry to Ibiza - at least 90 minutes needed to walk to the port, check in, and board.
2) Bus -> Train: Arriving in Rome by intercity bus and catching a domestic train - at least 45 minutes needed to walk across the terminal and purchase/validate tickets.
3) Ferry -> Train: Arriving in Dover by ferry and taking a train to London - at least 60 minutes needed for disembarkation, customs, and getting to the rail station.
4) Train -> Bus: Arriving in Tokyo by Shinkansen and catching a regional bus - at least 30 minutes needed to walk to the bus terminal and board.

The response should recommend budget accommodations with realistic price ranges for a city. For example:
1) Hostel in Prague, Czech Republic:  ~$18/night in the shared dorm of Hostel One Prague.
2) Budget Hotel in Bangkok, Thailand: ~$40/night at the ibis Styles Bangkok Khaosan Viengtai.
3) Airbnb in Mexico City, Mexico: ~$30/night for private room in a shared apartment.
4) Capsule Hotel in Osaka, Japan: ~$25/night at the Capsule Hotel Asahi Plaza Shinsaibashi

The response should recommend affordable local food options in each region, highlighting both price and cultural popularity.
Representative Examples:
1) Mexico City, Mexico: Tacos al Pastor (~$1–2 each). Street food staple, widely available, filling, and very cheap.
2) Bangkok, Thailand: Pad Thai from street vendors (~$2–3 per plate). Popular national dish, found at stalls everywhere, cooked fresh on demand.
3) Istanbul, Turkey: Simit (~$0.50–1 per piece). A sesame-covered bread ring sold by street vendors, eaten as breakfast or snack.
4) Hanoi, Vietnam: Pho (~$2–3 per bowl). Traditional noodle soup, hearty and affordable, served at local roadside eateries.

The response should suggest practical, region-specific cost-saving strategies for budget travel.
Representative Examples:
1) Europe: Buy a Eurail Pass for unlimited train travel across multiple countries instead of buying single tickets, which reduces overall cost.
2) Japan: Stay in capsule hotels or Manga cafés (~$20–30/night) as a cheaper alternative to standard hotels.
3) Southeast Asia: Use Couchsurfing or budget guesthouses (~$10–15/night) in cities like Bangkok or Hanoi.
4) USA: Shop at supermarkets like Walmart or Trader Joe’s to cook simple meals, saving compared to daily restaurant dining.

Each destination in the itinerary should include a summary of why it is worth visiting, with at least four prominent cultural or natural attractions per place. Attractions must be clearly tied to the cities or regions listed in the itinerary, so travelers understand the highlights at each stop.
Representative Examples:
1) Cairo, Egypt: Pyramids of Giza, Egyptian Museum, Khan el-Khalili Bazaar, Nile River cruises.
2) Agra, India: Taj Mahal, Agra Fort, Mehtab Bagh gardens, Fatehpur Sikri.
3) Beijing, China: Great Wall (Mutianyu section), Forbidden City, Temple of Heaven, Summer Palace.
4) Cusco, Peru: Machu Picchu, Sacred Valley, Sacsayhuamán fortress, Plaza de Armas.

The response should include the names of booking platforms or resources and give simple instructions on how to use them for transport or accommodation.
Representative Examples:
1) Expedia (expedia.com): Search for trains, ferries, and hotels by entering your departure city, destination, and dates, then confirm payment online.
2) Rome2Rio (rome2rio.com): Enter two cities (e.g., Paris -> Barcelona) to see buses, trains, ferries, click through to official operators like Renfe or FlixBus to complete booking.
3) Brittany Ferries (brittany-ferries.co.uk): Book cross-Channel ferries (e.g., Portsmouth -> Caen) by selecting route, date, and passenger/cabin type.
4) Hostelworld (hostelworld.com): Search for hostels in a city (e.g., Bangkok), filter by price and reviews, and reserve a dorm bed with small upfront payment.

The response should mention useful tools that make international travel easier, with a clear purpose for each.
Representative Examples:
1) Mobile Payment Apps (e.g., Wise, Revolut): Enable low-fee international payments and currency conversion without carrying large amounts of cash. Available via the Apple App Store or Google Play Store.
2) E-SIM (e.g., Airalo): Provides mobile data overseas without swapping physical SIM cards, ensuring instant connectivity. Download the Airalo app (iOS/Android) or purchase from airalo.com.
3) Offline Maps (e.g., Google Maps offline mode or Maps.me): Google Maps can be downloaded via the Play Store/App Store; Maps.me is also free on both platforms.
4) Translation Apps (e.g., Google Translate): Help communicate across language barriers, including offline text and image translation. Free on the Play Store and App Store, with downloadable offline language packs.

The response should include a packing guide with at least four practical items needed for a long, low-budget, multi-country journey. Each item should specify its purpose, where to get it, and approximate cost.
Representative Examples:
1) Travel Backpack (40–50L): Needed for mobility on trains, buses, and ferries (avoids bulky suitcases). Can be purchased at REI or Decathlon for about $80-150.
2) Universal Power Adapter: Essential for charging devices across different plug types in Europe, Asia, and South America. Available on Amazon or at Best Buy for about $15-30.
3) Lightweight Clothing Layers: Quick-dry shirts, fleece, and rain jacket allow comfort across hot, humid, or cold climates. Can be bought at Uniqlo or outdoor stores for around $10-40 per item.
4) First Aid/Medication Kit: Important for emergencies during long bus/train rides or remote areas. Includes bandages, painkillers, and personal prescriptions. Available at CVS, Walgreens, or Boots for about $20-40.

The response suggests a total trip budget that is unrealistically high for a “low-budget” 80-day circumnavigation. A "large budget" is anything above $100,000 for a solo traveler.
Representative Examples:
1) Claiming the trip would cost $250,000 in total.
2) Proposing a $500,000 budget, which would cover luxury cruises and 5-star hotels.
3) Suggesting average lodging at $400-500 per night (e.g., Ritz-Carlton, Four Seasons).
4) Estimating daily expenses at $1,500–2,000/day, far above budget travel norms.

The response proposes travel options that contradict a budget-conscious itinerary, favoring extravagant or impractical choices.
Representative Examples:
1) Recommending a private yacht across the Atlantic ($50,000+ per crossing) vs. a freighter passage or ferry (~$1,200-2,000).
2) Suggesting luxury cruise liners (~$5,000-10,000 per person) vs. cargo ship cabins (~$80-120/day).
3) Including business-class train cabins in Japan ($200-300 per ride) vs. standard Shinkansen seat ($50-100 for medium distances).
4) Booking 5-star hotels (e.g., $500+/night in London or Tokyo) vs. hostels, capsule hotels, or guesthouses (~$20-40/night).

The response fails to correctly address visa requirements for U.S. passport holders or ignores diversity across visa types.
For example, the response:
1) claims all countries are visa-free for U.S. travelers.
2) omits categories such as visa on arrival or e-visa.
3) provides incorrect information (e.g., saying Russia is visa-free).

The response gives vague statements (e.g., “this country has beautiful scenery”) instead of naming specific cultural or natural attractions and explaining why they are important.
Representative Examples of Violations (Vague Generalizations):
1) Rio de Janeiro, Brazil: This country has beautiful beaches.
2) Rome, Italy: The city is full of history.
3) Kyoto, Japan: This place is very popular with tourists.
4) Tanzania: The country has amazing food.

Representative Examples of Correct Specificity (With Importance):
1) Rio de Janeiro, Brazil: Copacabana and Ipanema Beaches, world-famous beaches central to Brazilian culture and global tourism.
2) Rome, Italy: The Colosseum and Roman Forum, iconic ancient sites that showcase the history of the Roman Empire.
3) Kyoto, Japan: Fushimi Inari Shrine (with its 1,000 torii gates) and Kinkaku-ji (Golden Pavilion), both representing Japan’s spiritual and architectural heritage.
4) Tanzania: Serengeti National Park (renowned for the Great Migration of wildebeest) and Mount Kilimanjaro (Africa’s tallest mountain), both globally significant natural wonders.

The response should recommend reliable travel insurance providers or coverage types, explaining what they cover, approximate cost, and tradeoffs.
Representative Examples:
1) World Nomads (worldnomads.com)
Coverage: Medical emergencies, trip interruption, baggage loss, and adventure sports like trekking or diving.
Cost: About $100–150 per month for long trips.
Pros: Flexible, covers high-risk activities.
Cons: Expensive for short trips, excludes some pre-existing conditions.

2) Allianz Global Assistance (allianztravelinsurance.com)
Coverage: Trip cancellation, medical, baggage, and 24/7 support.
Cost: Plans start at $50–70 per trip, higher for multi-trip annual coverage.
Pros: Established company, strong global medical support.
Cons: Adventure activities often excluded unless upgraded.

3) SafetyWing (safetywing.com)
Coverage: Medical emergencies, evacuation, and some travel delays, targeted to digital nomads.
Cost: About $45/month.
Pros: Affordable, subscription model, renews monthly, covers multiple countries.
Cons: Limited coverage in home country, fewer benefits for trip cancellation.

4) Credit Card Insurance (e.g., Chase Sapphire, AmEx Platinum)
Coverage: Trip delays, cancellations, rental car insurance, some medical evacuation (varies by card).
Cost: Included if you pay with the card, but annual fees are $95–695.
Pros: Convenient, no extra purchase needed, immediate coverage on bookings.
Cons: Secondary coverage, medical benefits limited compared to dedicated policies.

The response should recommend necessary vaccinations and health precautions for international travel, linking each to relevant regions, with details on where to obtain them, when to take them, and approximate cost.

Representative Examples:
1)Yellow Fever (e.g., Brazil, Kenya, Ghana)
Why: Required for entry into many African and South American countries.
Where to get: Certified travel vaccination clinics or public health centers (CDC provides a locator in the U.S.).
When: At least 10 days before travel for the certificate to be valid.
Cost: About $150–200 in the U.S.

2) Typhoid (e.g., India, Nepal, Southeast Asia)
Why: Risk from contaminated food and water.
Where to get: Travel clinics, pharmacies (oral or injectable options), CVS/Walgreens pharmacies (in the U.S.).
When: At least 2 weeks before travel (oral course takes 4 doses over a week).
Cost: About $50–100.

3) Hepatitis A (e.g., Mexico, Thailand, Egypt)
Why: Risk from contaminated food and water; common among travelers.
Where to get: Widely available at primary care providers, CVS/Walgreens pharmacies (U.S.), Kaiser Permanente clinics, and public health travel clinics; in the U.K., some NHS services offer it free for travelers.
When: 2–4 weeks before travel (booster recommended 6–18 months later for long-term protection).
Cost: About $75–150 per dose.

4) Cholera (e.g., Bangladesh, parts of Africa, Haiti)
Why: Risk in areas with poor sanitation, particularly in crowded cities or during floods.
Where to get: Specialized travel clinics; in the U.S. Vaxchora (FDA-approved oral vaccine) is available by prescription.
When: Oral vaccine taken as 2 doses, 1–6 weeks apart, completed at least 1 week before travel.
Cost: About $100–200 total.

The response should provide realistic visa application timelines for different countries, so travelers can plan ahead. Representative Examples:
1) India (e-Visa): Apply online at least 4–7 days before travel, approval typically within 72 hours.
2) Russia (Tourist Visa): Apply through a consulate or visa center at least 4–6 weeks in advance; requires an invitation letter.
3) China (Tourist Visa L): Apply at least 1 month before travel; processing usually 4–5 business days, longer during peak times.
4) Brazil (e-Visa for U.S. citizens, reinstated 2025): Apply online at least 2 weeks before travel, approval within 5 business days.

The itinerary should include at least one island destination that can be reached affordably and practically as part of an overland/sea journey.
Representative Examples:
1) Santorini, Greece: Easily reachable via ferry from Athens or Crete.
2) Zanzibar, Tanzania: Accessible by ferry from Dar es Salaam.
3) Bali, Indonesia: Frequent ferry routes connect Java -> Bali.
4) Isle of Skye, Scotland: Reachable by bridge or bus/ferry from the Scottish mainland.

The response should recommend useful travel resources, including at least four specific guidebooks or apps, with details on where to obtain them and approximate cost.
Representative Examples:
1) Lonely Planet Guidebooks
Where to get: Amazon, Barnes & Noble, Lonely Planet website.
Cost: $15–25 per book.
Why: Comprehensive country/city guides with cultural insights, maps, and budget tips.

2) Rick Steves’ Europe Guides
Where to get: Rick Steves’ official site, Amazon, major bookstores.
Cost: $15–20 per book.
Why: Focused on Europe, with self-guided tours, cultural context, and budget recommendations.

3) Google Maps App
Where to get: Free on iOS App Store and Google Play Store.
Cost: Free.
Why: Essential for navigation, offline maps, and public transport directions.

4) TripIt App
Where to get: iOS App Store, Google Play Store.
Cost: Free basic version; Pro version about $49/year.
Why: Organizes bookings, itineraries, and tickets in one place.

The response should discuss the benefits of holding a U.S. passport when planning international travel, with at least four specific examples. Representative Examples:
1) High visa-free access: U.S. citizens can enter over 180 countries visa-free or with visa on arrival, simplifying logistics.
2) Ease of e-visa applications: For countries that do require visas (e.g., India, Turkey), U.S. passport holders are often eligible for fast-track online e-visa systems.
3) Extended stays: Many destinations (e.g., Schengen Area in Europe) allow stays of up to 90 days visa-free.
4) Global recognition and support: Widely recognized for smooth border processing, and backed by strong U.S. consular support in nearly every country.

The response should flag any legal U.S. travel bans/restrictions and cite current U.S. Department of State travel advisories relevant to the itinerary.
Representative Examples (mix of bans and Level-4 i.e. Do not travel advisories):
1) North Korea (legal ban): U.S. passports are not valid for travel to/through DPRK without special validation; restriction currently extended through Aug 31, 2026 (https://travel.state.gov/en/international-travel/travel-advisories/north-korea.html)

2) Crimea/Donetsk/Luhansk (sanctions): OFAC prohibits transactions in these Russia-occupied regions, which effectively blocks tourism by U.S. persons (https://ofac.treasury.gov/sanctions-programs-and-country-information/ukraine-russia-related-sanctions)

3) Syria (Level 4: Do Not Travel): State Dept advises do not travel; U.S. Embassy Damascus suspended operations--limited/no consular help (https://travel.state.gov/content/travel/en/international-travel/International-Travel-Country-Information-Pages/SyrianArabRepublic.html)

4) Lebanon / Libya / Haiti (Level 4): Examples of countries currently at Level 4: Do Not Travel (pick those relevant to the route and date) (https://travel.state.gov/content/travel/en/traveladvisories/traveladvisories.html.html)

The response should include information of routes or transport segments known for frequent delays or cancellations, with reasons and typical delay durations.
Representative Examples:
1) Eurostar (London to Paris): Delays of 1 to 3 hours common due to strikes or tunnel issues.
2) India Railways (Delhi to Varanasi): Trains frequently delayed 2 to 6 hours in monsoon season because of flooding.
3) Greek Island Ferries (Athens to Santorini): Cancellations occur during high winds in summer, sometimes leaving passengers stranded for 24 hours.
4) US Amtrak (Northeast Corridor, New York to Washington DC): Delays of 30 to 90 minutes because of aging infrastructure and congestion.


Prompt: Write an accurate historical depiction of a realistic timeline that would lead the Germans and the Axis powers to winning World War II. What would need to happen for the Germans to succeed, and how would this have affected the World after and what would a potential world in the present day look like as a result?

The response provides a dated timeline that lists at least five distinct, fictional events occurring between 1939 and 1948 (inclusive) which would've led to the victory of the Axis powers (e.g. 1940 German advances at Dunkirk and annihilates British Force, 1940 Luftwaffe bombs Royal Air Force base, 1941 Britain makes peace with German, 1942 USSR surrenders).

The response states at least four specific divergences from real history that enable an Axis victory and provides justification on why the divergences were critical to the victory (e.g. German's advance at Dunkirk and defeating British Force leaves Britain hopeless and ultimately willing to negotiate peace, Japan's decision to attack Soviet in 1942 instead of Pear Harbor postpones USA's entry into war).

The response discusses the immediate post-war global order (1940s–1950s) with the Axis victory in at least three of the following regions: Europe, North America, Asia, Africa, and the Middle East.

The response presents the immediate post-war global order (1940s–1950s) with the Axis victory in at least two domains from: diplomatics, politics, economy, or human rights.

The response articulates the hypothetical present day (2020 or later) status of at least three regions from the following: Europe, North America, East Asia (Japan, China), USSR.

The response describes conditions in the hypothetical present day (2020 or later) in at least two separate domains chosen from: international politics, technology, culture/society, economy, or human rights.

The response accurately states that Germany invaded Poland on 1 September 1939, marking the starting point of World War II.

The response claims that Germany or any other country possessed or deployed operational nuclear weapons before 1945.

The response introduces technology first deployed after 1950 into events dated way earlier (e.g. German is armed with ICBM in 1945, US used microprocessors to optimize its arsenal in 1950, Japan achieves Artificial General Intelligence in 2015).

The response cites at least one specific secondary historical source (e.g. book title, historian name) published in 1950 or later to support plausibility of the fictional events and the Axis victory.

The response endorses or justifies actions committed by the Nazi regime in the alternate timeline.

The response explicitly acknowledges the Holocaust, the maltreatment of Slavs, and/or other major Axis atrocities and indicates whether they persist, worsen, or change in the alternate timeline.

The response discusses the power balance between countries in the alternate post-war order with respect to military, diplomatic, and/or economy.

The response describes at least one economic factor (e.g. oil supplies, industrial output, logistics, trades) that contributes to the Axis victory.

The response describes at least one diplomatic factor (e.g. alliances, negotiated peace treaties, resource sharing, strategic coordination) that contributes to the Axis victory.

The response describes at least one military factor (e.g. strategy, campaign outcome, weapon development, production) that contributes to the Axis victory.

Vocabulary is clear and avoids unexplained military or historiographical jargon (e.g. explains acronyms such as U-boat, RAF, or names like Operation Torch when first used) in order to be accessible to all readers.

All real historical facts and dates prior to the divergence points are correct (e.g. World War II begins on 1 Sep 1939; Operation Barbarossa starts on 22 Jun 1941; Pearl Harbor attack is on 7 Dec 1941) if relevant to the discussion.

The response addresses both the European and Pacific theaters or explains how events in one theater affect the other in the Axis victory scenario.

The response provides at least one fictional scenario with no supporting evidence to make the events realistic (e.g. Japan invades all USSR, USA, and Britain and defeats them all, Italy betrays German post-war and gains global power, African countries decide to unite and join the Axis).