Prompt: Propose a biologically plausible model that integrates the neuroscience concepts of engrams and predictive coding into one network capable of performing classification tasks.

The response is organized into clearly labeled sections that cover: Executive Summary (e.g. one-paragraph overview of the model, key claims, outcomes), Background & Design Principles (e.g. succinct engram and predictive-coding basics, guiding assumptions), Methodology (e.g. how the model is built, trained, evaluated at a high level), Applications/Impact (e.g. where this helps, continual/few-shot classification, neuroscience relevance), Limitations (e.g. known weaknesses, open risks, scalability issues), and Conclusion (e.g. takeaways, next steps).

The response sections topic-specific technical parts clearly (e.g. Network Architecture section detailing hierarchy, prediction vs. error units, engram assemblies, connectivity, Learning/Plasticity and Training Methodology section suggesting local rules like Hebbian/STDP, engram allocation/tagging, consolidation schedule, training protocol, Inference Procedure section covering prediction-error message passing, engram retrieval during classification, computational cost, Evaluation/Benchmarks section describing datasets, metrics, and ablations isolating engram vs. predictive-coding contributions, Biological Plausibility section discussing signal locality, neuromodulators, timescales, energy/implementation constraints).

The response clearly defines an engram as a sparsely distributed neuronal ensemble that undergoes persistent synaptic/physiological changes to encode a specific memory, such that its reactivation retrieves that memory and its disruption impairs it.

The response clearly defines predictive coding as a hierarchical inference scheme where higher areas send top-down predictions, lower areas compute bottom-up prediction errors, and iterative local message passing updated representations to minimize those errors (e.g. approximate inference under a generative model, belief propagation in probabilistics graphical models, cocktail party effect, perceptual filling-in of occluded objects).

The response clearly distinguishes local predictive-coding prediction errors (e.g. layer/feature-specific residuals computed by dedicated error units and passed locally, mismatch signals, difference between predicted and actual sensory input at the lowest level of the hierarchy) from global error signals used for task learning or decision making (e.g. label-loss gradients, neuromodulatory reward-prediction errors, reward prediction errors).

The response suggests that training the proposed architecture with global backpropagation is a biologically implausible learning rule.

The response proposes a model with a predictive-coding mechanism, including error-computing neurons that compare bottom-up input with top-down predictions and propagate the resulting error (e.g. segregated prediction vs. error units with subtractive inhibition, laminar organization with superficial error units and deep prediction units, dendritic comparisons via apical–basal compartment interactions, precision-weighted errors implemented through gain control or divisive normalization).

The response presents a single circuit in which stable, sparsely reactivatable cell assemblies store class-specific engrams (e.g. Hebbian/STDP with consolidation, Bienenstock-Cooper-Munro, structural plasticity) while prediction and error units compute top-down predictions and bottom-up mismatches such that: pattern completion drives predictions; error signals modulate the same synapses via error-gated plasticity to refine or consolidate assemblies; task phase is gated by neuromodulators, with anatomical or temporal routing separating predictions and errors.

The response makes engram formation independent of predictive-coding error signals (e.g. driven by random noise or by mechanisms that override/ignore errors, equating engram itself with the prediction error, Hebbian learning rule unmodulated by the predictive coding error).

The response includes an experiment with a clear dataset, task, protocol, and reports that includes standard ML/statistical metrics (e.g. top-1/top-5 accuracy, F1, AUROC, confusion matrix, Brier score or NLL, expected calibration error, confidence–error correlation, confidence intervals).

The response includes an experiment that provides insight into relevant biophysical or anatomical quantities (e.g. inference latency, energy per inference, spike rate, firing sparsity, E/I balance, oscillatory power/coherence, laminar or pathway segregation of predictions vs. errors, effects of ablating error units or engram plasticity).

The response clearly explains how the architecture produces class outputs (e.g. map the highest-level posteriorMAP estimate or the minimum total prediction error to a class label, use winner-take-all over engram assemblies with one readout neuron per class, decode the active attractor/ECA identity via a linear readout, report confidence from residual error or precision).

The response introduces concepts without a direct role in either engram formation/reactivation (e.g. generic neuromodulator or oscillation mentions without linkage, interneuron subtype digressions, creativity/clinical speculation) or predictive-coding computations (e.g. Contrastive Predictive Coding as an ML method, neuromorphic hardware/energy figures, adversarial-robustness detours, over-citation of winner-take-all or tertiary sources).

The response explicitly acknowledges the model’s limitations and articulates concrete directions for further research (e.g. unresolved biological assumptions, scalability, data/experiment needs, alternative mechanisms to test).

The response cites seminal works supporting engrams/memory systems (e.g., Hebb, 1949; Marr, 1971; Hopfield, 1982; McClelland, McNaughton & O’Reilly, 1995; Josselyn & Frankland, 2012; Tonegawa et al., 2015) and predictive coding (e.g. Rao & Ballard, 1999; Friston, 2005/2010; Bastos et al., 2012; Spratling, 2008).

The response presents explicit equations for biologically plausible learning updates (e.g. Hebbian/STDP timing rules, BCM with sliding threshold, Oja’s normalization, three-factor/reward-modulated plasticity with eligibility traces, error-gated synaptic plasticity, homeostatic scaling or metaplasticity, consolidation mechanisms such as synaptic tagging/capture).

The response presents explicit equations for inference/state updates (e.g. predictive-coding dynamics with precision-weighted prediction errors, variational free-energy gradients, Kalman/Bayesian filtering and smoothing, message passing in hierarchical PC, Hopfield/attractor energy minimization, divisive normalization/gain control).

The response specifies the model’s full network architecture (e.g. layer types and hierarchy, connectivity with sparse E/I microcircuits obeying Dale’s law and structured recurrence, distinct feedforward/feedback pathways separating predictions and errors, neuron choice, synapse model, plasticity rules, normalization/homeostasis, neuromodulatory gating, oscillation/phase routing, readout/class interface).

For each included experiment, the response specifies the full methodology (e.g. model architecture and parameters, datasets, task definitions, procedures, controls/ablations, performance metrics, statistical reporting).

The response proposes architecture using network dynamics that are consistent with established experimental neuroscience literature (e.g. attractor dynamics, sparse firing, balanced excitation–inhibition, oscillation-coupled communication).

The response proposes network wiring that reflects core features of real neural circuits (e.g. sparse connectivity with limited fan-in/fan-out rather than all-to-all, generally non-symmetric synapses, excitatory vs. inhibitory neuron types that are kept distinct in line with Dale’s law, long-range projections linking engram mechanisms with other circuits that are selective and rarer than local connections, recurrence that is structured rather than fully dense).

The response specifies how stable, diverse engrams are formed at initialization (e.g. via k-WTA + Hebbian/STDP, via excitability-based allocation with synaptic tagging-and-capture/replay consolidation).


Prompt: Write a blog post contrasting Euclidean geometry used in high school math competitions such as the AMC, AIME, or Olympiads to geometry taught in the standard American high school math curriculums. This post is written for people with knowledge of geometry from standard high school classes, but with no math experience in competitive math. Your post must include clear examples of problem solving techniques and theorems that are commonly used in high school math competitions, but not typically taught in a high school math class.

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) SSS, meaning, Side-Side-Side, criteria to establish that two triangles are congruent, (2) American Invitational Mathematics Examination (AIME), (3) American Mathematics Competitions (AMC), (4) United States of America Mathematical Olympiad (USAMO)).

The response includes a geometric diagram for each theorem and sample problem introduced (e.g., (1) Draw an example of a cyclic quadrilateral and the relevant angles that sums to 180, (2) Draw and example cyclic quadrilateral with the diagonals to show the Ptolemy's Theorem, (3) Draw an example of a triangle of the cevians to illustrate the Ceva's Theorem, (4) Draw three circles to illustrate the Collinearity of External Tangent Intersections).

The response includes a geometric diagram far from the text where it is discussed (i.e., the text describes an image that is missing or is on pages away).

The response keeps the primary focus of the blog post on Euclidean geometry (e.g. (1) Ceva's Theorem, (2) Menelaus' Theorem, (3) Power of a Point Theorem, (4) Stewart's Theorem, (5) Ptolemy's Theorem, (6) Spiral Similarity, (7) Miquel Point) and does not include problems from other topics such as functions and combinatorics.

The response's blog post contains at least 2 sub-headings to separate discussion of different topics (e.g., Subheadings titled: (1) Advanced Theorems, (2) Advanced Problem-Solving Techniques, (3) Example Problems and Solutions, (4) Where to go from here?).

The response's blog post mentions at least one high school math competition (e.g., (1) AMC, (2) AIME, (3) USA(J)MO, or (4) IMO).

The response's blog post elaborates on a theorem in the common core high school geometry standards, beyond a quick definition to contrast it with competition-level requirements (e.g., Pythagoras' Theorem states that the square of the length of the hypotenuse of a right triangle is equal to the sum of the squares of the two other sides. This can be used to find the missing lengths of the right triangle as long as two other lengths are known, and continues to provide a diagram and example usage).

The response identifies at least one theorem in competition-style Euclidean geometry problems (e.g., (1) Ceva's Theorem, (2) Menelaus' Theorem, (3) Power of a Point Theorem, (4) Stewart's Theorem, (5) Ptolemy's Theorem, (6) Spiral Similarity, (7) Miquel Point).

The response identifies at least one problem-solving technique in Euclidean geometry (e.g., (1) Correctly identifying what theorems to use, (2) Correctly applying the theorem, (3) Adding auxiliary lines, (4) Homothety, (5) Symmetries, rotations, and reflections, (6) Inversion).

The response introduces a problem-solving technique that is included in the common core high school geometry standards (e.g., (1) Pythagoras' Theorem, (2) Midpoint Theorem, (3) Congruent Triangle Theorems, (4) Angle Bisector Theorem) and provides a sample problem that can be solved using the technique.

The response includes a sample problem to demonstrate problem-solving techniques (e.g., (1) AMC 12 2018 #8, (2) 2020 AIME II Problem 15, (3) 2020 AMC 12A Problem 18, (4) 2021 USAJMO Problem 3, or (5) explicitly stating a simple instance of a problem where the techniques can be applied).

The response describes the test format of at least one high school math competition (e.g., (1) The AIME is a 15-question, 3-hour exam, (2) Each answer is an integer from 000 to 999, inclusive, (3) The AMC 10 is a 25-question, 75-minute multiple-choice test. (4) Calculators are not permitted).

The response incorrectly defines a theorem, incorrectly presents the solution to a sample problem, or incorrectly provides information about a high school math competition.

The response identifies a theorem or problem-solving technique that is discussed within the first 200 words of the post (e.g., Ptolemy's theorem establishes the relationship between the length of the sides (abcd) and the diagonals (pq) of a cyclic quadrilateral where ac + bd = pq).

The response's blog post presents a common core style math problem as an olympiad style math problem (e.g., (1) Pythagoras' Theorem, (2) Midpoint Theorem, (3) Congruent Triangle Theorems, (4) Angle Bisector Theorem).

The response identifies a distinction between competition-style math problems and standard curriculum math problems (e.g., (1) Competition-style math problems cover more breadth and depth using more advanced theorems and problem-solving skills, (2) Competition-style math problems require more "tricks", (3) Competition-style math problems require more steps of problem-solving involving unique combinations of applications of math theorems, (4) Competition-style math problems are highly specialized to specific topics such as geometry, combinatorics, and number theory, and less on analysis and algebra.

The response includes a step-by-step solution to a sample problem introduced (e.g., $CAROLINE$ is essentially a plus sign with side length 1 with a few diagonals, which motivates us to coordinate bash. We let $N = (1, 0)$ and $E = (0, 1)$. To find $CORNELIA$'s self-intersections, we take
\[CO = y = 2, AI = y = -3x + 6, RN = y = 3x - 3\]
And plug them in to get $C_1 = \left(\frac{4}{3}, 2 \right)$ where $C_1$ is the intersection of $CO$ and $AI$, and $C_2 = \left(\frac{5}{3}, 2 \right)$ is the intersection of $RN$ and $CO$.
We also track the intersection of $AI$ and $RN$ to get $\left(\frac{3}{2}, \frac{3}{2} \right)$.
By vertical symmetry, the other 2 points of intersection should have the same x-coordinates. We can then proceed with Solution 1 to calculate the area of the triangle (compare the $y$-coordinates of $A,R,I,N$ and $CO$ and $EL$).).

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

The response cites the source of a sample problem incorrectly (i.e., quotes or explains a particular problem, such as AMC 12 2018 #4, but is actually about a different problem or a variant of the problem that is different).

The response uses and cites the source of competition problems (e.g., (1) AMC 12 2018 #8, (2) 2020 AIME II Problem 15, (3) 2020 AMC 12A Problem 18, (4) 2021 USAJMO Problem 3).


Prompt: You are a junior analyst at the U.S. Department of State's Bureau of Western Hemisphere Affairs. Write a concise internal strategic report on the current crisis in Haiti. The report must (1) assess the interconnected political, economic, and security failures; (2) analyze the influence of key domestic actors (e.g., G9 an fanmi, Guy Philippe) and international bodies (e.g., CARICOM, UN); and (3) propose and justify three distinct, actionable policy options for the U.S., each with a clear analysis of its potential risks and benefits.

The response claims to be an AI language model, breaking the assigned persona.

The response is structured like a formal internal report with sections equivalent to an executive summary, an analysis section, a proposal, and a clear comparison.

The response uses clear, hierarchical headings such as Executive Summary, Proposed Policies, Analysis, Conclusions, and similar sections.

The response has a tone is that is harsh, biased, or judgmental (e.g., describing Haitian officials as "corrupt beyond repair", calling gangs as "savages", implying the U.S. has no responsibility at all for the crisis, or claiming that Haiti’s has no capacity for democracy).

The response has a "Works Cited" or References section that contains one or more duplicate entries for the same source.

The response has significant formatting errors or misaligned text that obscure meaning, or includes incoherent tables.

The response cites Wikipedia as the main source for a key factual claim.

The response presents three distinct policy options (e.g., maintaining the status quo, UN observation, diplomatic interventions).

The response lists policies that are minor variations of each other.

The response identifies the main gang coalition by its specific name: "Viv Ansanm".

The response, in its analysis of Guy Philippe, mentions his prior conviction in the U.S. and deportation.

The response cites a source with a nonsensical access date (e.g., "December 31, 1969" or any date more than 20 years before the start of the 2018 Haiti crisis (7/7/18)).

The response demonstrates a fundamental misunderstanding of an actor’s role (e.g., portraying CARICOM as a military force, omitting its membership, claiming the UN provides armed support, or describing the G9 as peaceful).

The response claims a contribution was made but fails to provide a concrete citation to support the claim.

The response names at least one member of the Haitian Provisional Presidential Council (PPC) (e.g., Lauren Saint-Cyr, Fritz Jean, Leslie Voltaire, Edgard Fils).

The response cites a YouTube video as a source for factual data in a formal intelligence report.

The response provides a balanced analysis of the risks and benefits for each proposed policy option (e.g., benefits to citizen welfare, reduction in crime, risks of eroding local culture, or potential loss of Haitian identity).

The response accurately identifies the security crisis (with multiple gangs taking control after the assassination of a political leader and the resulting surge in crime) as the primary driver of the Haitian crisis.

The response specifically mentions the control of Port-au-Prince and critical infrastructure by armed gangs.

The response provides a clear and plausible justification for each policy option based on stated U.S. interests.

The response correctly describes the political vacuum that allowed gangs to rise to power following the 2021 assassination of Haitian President Jovenel Moise.

The response references the assassination of Jovenel Moïse.

The response attributes the crisis to a single cause.

The response mentions the political or financial influence of the Haitian diaspora in the United States as a positive factor contributing to sway political decisions in favor of Haiti.

The response mentions the High Transitional Council (HCT) as the governing body that immediately preceded the current Transitional Presidential Council (TPC).

The response includes at least one policy option that directly addresses the flow of illicit firearms from the United States into Haiti (e.g., targeting straw purchases, addressing high-powered weapons like ARs, increasing accountability, expanding checks to states like Florida).

The response links security and political failures to a specific humanitarian consequence (e.g., political instability, high corruption, uncoordinated aid, repeated natural disasters).

The response cites a credible figure to support a humanitarian consequence (e.g., UN documents, peer-reviewed studies, U.S. government releases, reputable journalists like the New York Times).

The response mentions the UN-authorized, Kenyan-led Multinational Security Support (MSS) mission as the key international security response.

The response, while mentioning the FTO designation for Viv Ansanm, fails to explain its specific legal or practical implications for U.S. policy (e.g., asset blocking, increased gang power in Haiti, deportations of U.S. permanent residents who are Haitian, immigration restrictions).

The response mentions the specific role of Canada or France, two key international partners in the Haiti context (e.g., $450M in aid, bilateral French support for the MSS, Canada’s push for Haitian-led solutions, or French enforcement of sanctions).


Prompt:
create a simple agentic ai for personal mental-wellness support. id like to have
- two agents: one would be a 'Mood Agent' that reads your check-ins (text or voice) to see what your emotions are. then theres an 'Intervention Agent' that suggests quick exercises, like breathing or journaling prompts
- the workflow should be: the mood bot flags a really low or high mood, and then pings the intervention bot to help out
- maybe I can use an NLU tool like dialogflow? and we MUST encrypt all the data and let people delete their history
- if it gets confused it should ask follow up questions and if it sees someone is repeatedly low (eg 3 times in a row) it needs to escalate to a human counselor

how do I test it? we could measure engagement and self-reported mood improvement. maybe just pilot it with 20 users for 2 weeks?

The response contains a description of the Mood Agent and its functionalities, including analyzing user text and/or voice check-ins for emotional cues.

The response contains a description of the Intervention Agent and its functionalities, including offering quick wellness exercises (e.g., breathing, journaling, short walks, guided meditation).

The response contains a description of the workflow where the Mood Agent flags low or high moods using defined indicators or metrics (e.g., sentiment scores, keyword detection, tone analysis, frequency of negative expressions) and subsequently pings the Intervention Agent.

The response specifies at least one concrete Natural Language Understanding (NLU) tool or platform for intent and slot extraction and describes how the tool can be integrated into the agentic system (e.g., Dialogflow for detecting mood intents, Rasa for routing between agents, Wit.ai for extracting emotional entities, IBM Watson Assistant for dialogue management).

The response explicitly describes how all stored and transmitted user data will be encrypted and explains how the technique upholds recognized privacy standards (e.g., AES-256 encryption for storage meeting HIPAA compliance, TLS 1.3 for transmission adhering to GDPR guidelines, key rotation policies aligned with NIST standards, secure credential management following ISO/IEC 27001).

The response describes how users are given the ability to delete their interaction history on demand and specifies the mechanism by which this is enabled (e.g., an in-app “Delete History” button, time-based auto-deletion settings, a user-initiated API call, secure erasure confirmation).

The response describes a clarification fallback where the system asks follow-up questions when uncertain about user input and specifies how this mechanism is implemented (e.g., prompting the user to rephrase unclear text, requesting confirmation of detected mood, offering multiple-choice options for intent clarification, escalating to a simplified dialogue mode).

The response describes a fallback mechanism where repeated low-mood detections (e.g., three consecutive low-mood check-ins) by the Mood Agent lead to an escalation to a human counselor and specifies how this is carried out (e.g., direct connection to a crisis hotline, scheduling a live counselor session, sending a secure alert to a designated mental-health professional).

The response lists “engagement time” or similar engagement metrics and explains how they are collected and by which agent in the system (e.g., Mood Agent tracking duration of check-ins, Intervention Agent logging time spent on exercises, system-level analytics recording overall session length, cross-agent aggregation of user activity logs).

The response lists “mood improvement” as an evaluation metric and explains how it is measured (e.g., Mood Agent comparing sentiment scores across sessions, user self-reports on mood scales, aggregated pre/post-pilot survey results).

The response specifies a pilot plan involving approximately 20 users for testing and describes how the pilot will be conducted in two weeks (e.g., daily mood check-ins, scheduled intervention exercises, pre- and post-pilot evaluation surveys, aggregated engagement/mood improvement reports).

The response is organized into clearly labelled sections and discusses factors relevant to the labels (i.e., Agents and Roles, Workflow, Tech and Privacy, Fallbacks, Metrics and Pilot).

The response specifies a pilot plan with a cohort size different from 20 users.

The response specifies a duration different from two weeks for the pilot plan.

The response explains the multi-modal fusion of text and audio emotion cues and describes the processes used to combine them (e.g., extracting sentiment features from text and pitch/tempo features from audio, aligning modalities through time-stamped embeddings, applying feature-level fusion in a neural network, using decision-level fusion with weighted voting).

The response mentions a model that can assist the agents in detecting and classifying emotions and specifies how it is applied within the system (e.g., BERT for sentiment classification of text check-ins, RoBERTa for nuanced emotion detection, Wav2Vec for audio-based emotion recognition, multimodal transformers for joint analysis of text and speech).

The response outlines a dialogue-management component that maintains context across agent interactions and explains how it functions within the system (e.g., tracking conversation state across Mood and Intervention Agents, storing dialogue history in a context manager, applying slot-filling techniques, using reinforcement learning to optimize turn-taking).

The response describes ongoing ethical oversight by mental-health professionals beyond basic escalation and specifies how this oversight is incorporated into the system (e.g., regular audits of agent recommendations by licensed counselors, advisory board reviews of intervention strategies, continuous feedback loops with clinical psychologists, ethical risk assessments conducted before pilot expansions).

The response specifies how the deletion process happens, describes the technical procedure that ensures data is permanently removed, and explains how the method aligns with recognized privacy standards (e.g., user-initiated deletion request triggering cryptographic erasure in compliance with GDPR’s right to be forgotten, multi-pass overwrite of database entries following NIST guidelines, secure erase commands for SSD storage meeting HIPAA requirements, cloud provider API calls with verification logs aligned to ISO/IEC 27001).

The response states the system’s scope and limitations for mental-wellness support and provides a non-diagnostic disclaimer (e.g., clarifies it is not a substitute for therapy, advises contacting emergency services in crisis, provides crisis resources, distinguishes wellness tips from clinical treatment).

The response explains threshold calibration and validation of Mood Agent signals prior to deployment (e.g., hold-out validation set, inter-rater reliability with human annotators, ROC/PR analysis, periodic recalibration schedule).

The response explains the format or protocol used for data exchange between agents (e.g., JSON messages, gRPC calls, structured events with schemas, shared database entries).

The response specifies how agents exchange information and describes the communication mechanism (e.g., direct API calls between agents, message queues, shared state storage, event-driven notifications).

The response explains whether agent interaction is orchestrated through a central controller or peer-to-peer and how the coordination logic is implemented (e.g., workflow engine, custom orchestrator, rule-based routing, policy-based delegation).


Prompt: Write a detailed historical analysis of the economic rationale and ethical implications of the Allied strategic bombing campaigns in World War II, focusing on the bombing of industrial and civilian centers in Germany and Japan such as Dresden, Hamburg, and Tokyo. It should include a timeline of how the bombing campaigns changed over the course of the war. Assume the audience is a graduate student in history or political ethics.

The response provides a clear timeline of Allied strategic bombing (1939–1945).

The response identies key tactical and technological shifts (e.g., daytime to nighttime shift, precision to area bombing, morale and housing bombing, development of incendiary bombs)

The response identifies key locations and dates of famous Allied bombings of industrial and/or civilian cities (e.g., Hamburg 1943, Dresden 1945, Tokyo 1945, Hiroshima and Nagasaki 1945).

The response primarily discusses military bombing targets (e.g., Peenemünde Raid, Ploesti Oil Fields, Truk Lagoon, Kiel).

The response integrates the evolution of bombing strategy with shifts in economic rationale and ethical debates, showing how each influenced the others over time (e.g., the economic implication of bombing civilian centers, the ethical reprecussions of bombing civilian centers, differences in bombing strategy between 1941 and 1944, Churchill's opinion on the bombing of Dresden)

The response accurately summarizes official economic objectives of Allied bombing campaigns (e.g., disrupting war production, destroying railways, undermining worker morale, cutting off fuel).

The analyzes the actual economic outcomes of Allied bombing campagins (e.g., how much was production limited, how many railways were destroyed, how were workers impacted, using quantitative data when available, how much were fuel supplies disrupted).

The response discusses the rationale used by Allied leaders (e.g, industrial web, paralyzing transportation, causing a fuel shortage, ending the war) to justify targetting economic objectives (e.g., disrupting war production, destroying railways, undermining worker morale, cutting off fuel).

The response cites at least two primary sources (e.g., The Strategic Air War Against Germany, 1939-1945, The United States Strategic Bombing Survey, Memoirs of Albert Speer, "Bomber Offensive" ) and two post-1950 scholarly works (e.g., "The Bombing War: Europe, 1939-1945", "The Rise and Fall of the Third Reich", "Fire and Fury: The Allied Bombing of Germany, 1942-1945", "Blankets of Fire: U.S. Bombers over Japan during World War II")  to support the analysis.

The response evaluates the extent to which the bombing campaigns influenced the outcome of the war (e.g., evidence for/against the war being shortened, counterfactual outcomes, perspective of the Axis on the bombings, diverse opinions on the success of the bombings).

The response analyzes the moral justifications contemporaneously advanced by Allied leadership (e.g., breaking morale, shortening the war, supporting the other allies, inhumanity of the Axis) and provides counterarguments (e.g., disproportionate force, questionable necessity, conflict rather than cooperation with the Soviets, sanctity of human life).

The response discusses postwar and historiographical debates on the legality and morality of area bombing.  (e.g., Nuremberg Trials, Tokyo Trials, Slaughterhouse V, Churchill's snub of "Bomber" Harris ).

The response compares strategic bombing in Germany and Japan, considering geography, industrial layout, and cultural factors (e.g, differences between German geography relative to Japan, differences in population distributions between Germany and Tokyo, differences in response to Tokyo firebombing compared to Dresden response, differences in economic backgrounds of Germany and Japan).

The response compares Allied bombing (e.g., Dresden, Tokyo, Hamburg, Hiroshima) with Axis campaigns (e.g., the Blitz, Warsaw, Rotterdam, Guernica) to provide context.

Response notes long-term effects of bombing on postwar reconstruction and infrastructure (e.g,. comparison of German cities that were and weren't bombed, comparison of Japanese cities that were and weren't bombed, comparison of Italian cities that were and weren't bombed, discussion of economic impacts in the 21st century)

The response employs an academic tone appropriate to a graduate-level historical essay and situates arguments within broader historiographical or theoretical debates.

The response does not distinguish firebombing and nuclear bombing.

The response includes at least one statistical visualization (e.g., bombing tonnage graph, death toll chart, GDP impact, railway coverage).

The response acknowledges uncertainty or gaps in historical data where appropriate.

The response identifies and explains military necessity and civilian harm analogous to post-war International Humanitarian Law.

The cites relevant international laws or norms that applied during WWII (e.g., Hague Conventions, civil immunity, principle of distinction, lack of international institutions with enforcement abilities).

The response demonstrates awareness of the historiographical shift in interpretations from 1945 to the present (e.g., publishing of Der Brand in Germany, post-war heroic praise, Orthodox to Revisionist shift, Revisionist to balanced viewpoint).

The response identifies and explains at least one criticism from a formerly Axis country's scholar or institution (Yūshūkan, Yuki Tanaka, questioning the allies being better in "Hidden Horrors: Japanese War Crimes in World War II", Jörg Friedrich's Der Brand which provides a perspective of German victims).

The response uses military terminology (e.g., strategic vs. tactical bombing, precise bombing vs. area bombing) consistently and accurately.

The response appropriately contextualizes bombing campaigns within larger Allied war strategy (e.g., Eastern front, D-Day, African campaign, submarine warfare).

The response references cultural or psychological effects of bombing on civilian populations (e.g., The Blitz Spirit, Grave of the Fireflies, Slaughterhouse Five, trauma).

The response explains differences in legal or public responses to bombing campaigns across Allied nations (censorship by the US, censorship by the UK, criticism of Bishop George Bell, Stalin's perspective on UK bombing).

The response digresses into unrelated WWIII topics (e.g., Nazi goals, submarine warfare, the Eastern front, political challenges in England or Germany during WWII)


Prompt:
Generate a holistic, paradigm-shifting manifesto that synergizes next-generation business intelligence with agile methodologies to investigate the AI value-stream actualization gap.

Asp corporate AI spending approaches $300 billion annually, our research must explore the widespread organizational friction that undermines these transformation projects. Investigate why localized productivity gains often fail to ripple up to enterprise-level financial performance and explore the deep-seated cultural and strategic misalignment at play.

The primary audience for this manifesto is C-suites executives and boards of directors. However, the language must be kept simple, accessible, and informal, written in an engaging blog post style suitable for a new intern. Crucially, the tone must remain optimistic and forward-looking, avoiding an excessive focus on failure or negative statistics.

To make the concepts more tangible, the entire analysis must draw direct parallels to the challenges of the 1960s American space program. For instance, you should frame "pilot purgatory" in the context of the Gemini missions and discuss "hidden TCO" as analogous to the unforeseen logistical costs of the Apollo program.

The response accurately states that enterprise-wide AI projects yield an average ROI of just 5.9%.

The response correctly identifies that over 70% of AI pilots have failed to generate long-term value.

The response correctly reports that data scientists spend 60%-80% of their time on data preparation.

The response mentions the 2025 McKinsey & Company claim that 80% of companies report no material contribution to earnings from AI initiatives.

The response identifies the "middle management bottleneck" as a key component of cultural resistance to AI.

The response uses the term "Total Cost of Ownership (TCO)" when discussing hidden costs.

The response uses the term "bolted-on" to describe the superficial application of AI to existing workflows.

The response provides a table of business KPIs to measure the success of AI adoption (e.g., cost savings, ROI, customer satisfaction, user adoption rate).

The response highlights the risk of budget overruns, citing the example of cloud compute costs scaling exponentially.

The response omits the concept of using Net Present Value (NPV) as a metric.

The response mentions the financial risks of AI adoption (e.g., high compute costs, hallucinations, lack of explainability for auditing, high API costs).

The response includes the warning that siloed departmental AI optimizations can actively create enterprise-wide inefficiencies.

The response includes the concept of "PoC Purgatory", where over 90% of AI projects get stuck in the pilot phase.

The response mentions the Rackspace/FAIR's "Your Playbook for Winning the AI Game" or any of its 12 specific "plays" for industrializing AI. (e.g., build on a strong foundation first, assemble your AI roster, execute with precision).

The response mentions the impressit.io estimate that the cost to maintain a single generative AI application can be between $350,000 and $820,000 annually.

The response claims that the "J-Curve" of AI implementation (long-term gains follow short-term losses) represents a simple learning curve, not a profound systemic shock to the organization.

The response mentions the "empathy gap" exists, i.e., 63% of developers stating their leaders do not understand their daily pain points, "likely caused by leaders banking time savings achieved through AI without addressing existing points of friction" per the Atlassian "State of the Developer Experience" 2025 report.

The response translates the "J-Curve" phenomenon (i.e., long-term gains follow short-term disruption) into a direct warning about its negative impact on quarterly earnings and productivity forecasts.

The response links risk‑mitigation framing to CFO oversight.

The response frames POC/pilot purgatory of AI initiatives to the Gemini missions (i.e., pilots being assigned to Gemini missions but later being replaced or delayed on their assignments due to accidents and technical setbacks).

The reponse compares the hidden TCO of AI adoption (e.g., employee training costs, API costs, storage costs, compute costs) to the unforeseen logistical costs of the Apollo program (e.g., design flaws, increased safety measures, identified single points of failure, construction delays).

The reponse maintains a forward-looking, optimistic outlook and counter to proposed challenges for AI adoption.

The response mentions large language models.

The response mentions image-generation models.

The response links the adoption of closed-source models (e.g., agent/LLM offerings by OpenAI, Anthropic, Perplexity, Microsoft) to the prohibitive costs of training LLMs from scratch.


Prompt:
i'm trying to outline my final paper for this history of tech seminar and i'm a bit stuck on the scope. it's supposed to be a big 'comprehensive expository analysis' on two key AI architectures: LSTMs and the encoder-decoder architecture (eg seq2seq for translation).

i have to trace the whole historical development of LSTMs, e.g. go back to Hochreiter and Schmidhuber, the vanishing gradient problem in RNNs, and all the key milestones. then i have to do the exact same thing for the encoder-decoder models, from their origins (eg. Sutskever, Bahdanau, and Cho) up to how they dominate NLP now

I also need to talk about the societal implications. For example how they made machine translation, speech recognition, and predictive text practical, and what that means for globalization, accessibility and jobs. this also has to cover the ethical side, like biases from training data, who can access this stuff, and the risks of misuse like surveillance or spreading disinformation.

i also need a section that directly compares LSTMs and encoder-decoders historically, technically, and their different societal impacts. I also need to find 10 case studies and then synthesize them to develop some broader conclusions about how these advancements shape responsible technology and maybe strategies for ethical innovation. all claims have to be backed up by peer-reviewed literature, historical accounts, or formal ethical frameworks, like the IEEE guidelines

The response covers the architectures listed in the prompt (i.e. encoder-decoder and LSTM).

The response discusses how LSTMs and encoder-decoders have performed in the technical world and how they have accelerated workflows (e.g., time series analysis, machine translation, ML tasks, generative AI).

The response shows the effects of at least 2 of 10 case studies in ethics or responsible AI (e.g., bias in AI, ethical dilemmas, AI in mental health research, racial communication by AI).

The response includes AI applications that use modern architectures and discusses their societal implications (e.g., ChatGPT for search, Claude for coding, Gemini for Google-augmented search, or LSTMs for social media analysis).

The response discusses ethical fallacies and biases introduced during the training of these architectures (e.g., data-driven bias, the value alignment problem, dominance of high-resource languages, difficulty identifying bias during training).

The response discusses how academia and industry collaborated to create these architectures (e.g., the attention paper originating from industry, the LSTM paper from academia, ongoing collaboration between researchers, and cross-domain testing of architectures).

The response discusses how modern architectures such as transformers evolved from the successes of encoder-decoders and LSTMs.

The response discusses how modern AI affects global economics, employment, and workforce demographics (e.g., reduced coding time, faster document synthesis, skill shifts toward AI, increased focus on testing and control).

The response overlooks the historical influence of regulatory and ethical guidelines on AI development (e.g., Asilomar AI Principles, the Belmont Report, GDPR, IEEE’s Ethically Aligned Design), which have shaped how AI systems are designed and governed.

The response fails to address accountability for biased models and does not acknowledge historical instances of bias (e.g., gender bias in Bolukbasi 2016, facial recognition misidentification, biased hiring algorithms, credit scoring disparities).

The response includes AI history unrelated to LSTMs or encoder-decoders (e.g., Random Forests, SVMs, Decision Trees, Regressions).

The response discusses historical or societal aspects for LSTMs and encoder-decoder architectures (such as effects on cross border communication with better machine translation, helping tourism, handling higher data quantity, and having more cultural context to data).

The response focuses more on the technical aspects of LSTMs and encoder-decoder architectures than on their historical or societal background.

The response does not address challenges faced by LSTM and encoder-decoder architectures (e.g., difficulty handling long-range dependencies, vanishing gradients, limited parallelization, fixed-length context limitations).

The response discusses the role of peer review in the creation and refinement of LSTM and encoder-decoder architectures.

The response mentions that these architectures greatly improved machine translation, improving accessibility across languages.

The response mentions that open-source initiatives accelerated the adoption of LSTMs and encoder-decoders.

The response describes the historical development of the LSTM architecture.

The response discusses important milestones in LSTM development (e.g., vanishing gradient resolution, forget gate, peephole connections).

The response identifies key contributors to the development of LSTMs (e.g., Sepp Hochreiter, Jürgen Schmidhuber, Felix Gers, Fred Cummins).

The response discusses the vanishing gradient problem in RNNs, where gradients approach zero and halt training.

The response explains how LSTMs retain longer contexts and use additive mechanisms to avoid this issue.

The response includes a distinct section comparing LSTMs and encoder-decoders in terms of their historical development.

The response includes a separate section comparing LSTMs and encoder-decoders in terms of their technical details.

The response accurately compares the social implications of LSTM and encoder-decoder architectures (e.g., machine translation, speech recognition, chatbots, assistive technologies).

The response identifies key contributors (e.g., Sutskever, Bahdanau, Cho) and major milestones in the development of encoder-decoder architectures.

The response does not acknowledge the vanishing gradient problem in RNNs as one of the motivations behind the creation of LSTMs.

The response discusses the limitations of other machine learning architectures beyond LSTMs and encoder-decoders (e.g., CNNs, RNNs, Decision Trees, GANs).


Prompt: I've been assigned to write an article about Palantir's success in 2024-2025. Can you help me create a draft of this article that goes over the background of Palantir and why they have been successful? Make sure to keep the article length around a 10-minute read to maximize retention

The response contains a standalone section titled "Background" or equivalent that discusses relevant information about Palantir's background as a company.

The response contains a standalone section titled "Success Factors" or equivalent that discusses factors relevant to Palantir's success as a company.

The response includes irrelevant information that does not add to the discussion on Palantir (e.g., information about unrelated companies like Apple or NVIDIA, the public's skepticism of non-Palantir's leadership, companies like Databricks or Amazon's partnership with other vendors, the movement of stock markets not related to Palantir's actions or market shares, etc.).

The response refers to at least 3 distinct drivers in its discussion of Palantir's success factors (e.g., Palantir's modular architecture allows customers to flexibly integrate their own data pipeline, founders like Peter Thiel and Alex Karp maintain strong influence on the company's vision, Palantir's co-development of solutions with its partners instead of just selling software, its high market position stemming from specialization in sensitive domains).

The response mentions Palantir's competitors (e.g., Databricks, Snowflake, C3.ai) and discusses how Palantir differentiates itself from them.

The response explicitly states that Palantir was founded in 2003.

The response names Peter Thiel, Alex Karp, Joe Lonsdale, Stephen Cohen, and Nathan Gettings as founders of Palantir.

The response states that Palantir is headquartered in Denver, Colorado. Note that "Denver" or "Denver, CO" are also acceptable.

The response states that Palantir went public via a direct listing in 2020.

The response names and provides an accurate description for at least one of Palantir's core platforms (e.g., Gotham, its original platform for government and intelligence data analysis; Foundry, its commercial enterprise platform for integrating and modeling operations; Apollo, its continuous delivery system for managing software across environments; AIP, its Artificial Intelligence Platform that integrates large language models with security and compliance controls).

The response includes a timeline of Palantir's major product launches (e.g., Gotham launched in 2008, Foundry launched in 2016, Apollo launched in 2020, AIP launched in 2023).

The response states that Palantir’s major clients include U.S. government agencies, particularly in defense and intelligence (e.g., U.S. Department of Defense, U.S. Intelligence Community, Centers for Disease Control and Prevention).

The response supports financial claims with specific data points traceable to Palantir's financial statements (e.g., examining Palantir’s income statement to validate claims about revenue mix; referencing the balance sheet to show growth in assets or cash reserves; citing the cash flow statement to highlight operating cash flow trends; using quarterly/annual 10-K or 10-Q filings to support claims about customer segments or government vs. commercial revenue distribution).

The response relies on outdated financial data (e.g., full-year 2022 data or older) to make claims about Palantir's success in the 2024-2025 period.

The response discusses major challenges Palantir faces, providing a balanced perspective (e.g., controversial missions like its past work with ICE, pressure to maintain stockholder expectations and profitability, competition from other tech giants like Databricks and Snowflake, or over-reliance on large government contracts).

The response discusses how the 2023-2024 boom in Artificial Intelligence (AI) has contributed to Palantir's success (e.g., the launch of its Artificial Intelligence Platform (AIP) that integrates AI agents, Palantir's launch of "AIP Bootcamps" to drive customer adoption, the hosting of AIPCon to showcase its AI technology, and its contract with the U.S. Army for the AI-enabled TITAN program).

The section on 'Success Factors' (or equivalent) is placed after the "Background" section (or equivalent).

The length of the drafted article is commensurate with a 10-minute read time (roughly 2,000-3,000 words).

The response discusses factors enabling Palantir's success in the defense industry (e.g., large-scale partnerships with government entities like the U.S. Army for Project TITAN and the Department of Defense for Project Maven; rapid adoption of new technology like the AIP platform; high security and compliance readiness allowing operation on classified networks; a robust partner ecosystem including system integrators like Booz Allen Hamilton and L3Harris Technologies).

The response discusses Palantir's expansion into commercial industries as part of its diversification strategy (e.g., healthcare, by partnering with the U.S. Department of Health and Human Services to build Tiberius; aviation, by partnering with Airbus to run the Skywise platform; automotive, with partnerships like Ferrari and Lear; and semiconductors and electronics, with the Athinia platform developed with Merck KGaA).

The response explicitly identifies Palantir's achievement of sustained GAAP profitability (which began in late 2022/early 2023) as a key factor in its 2024-2025 success.


Prompt: Write an outline of a potential new episode for the Netflix show Black Mirror. This outline should be robust: for example, it should include descriptions of where the episode should be filmed and suggestions for potential actors for the roles.

The response clearly states an episode title which is four words or less (e.g. "In the Chamber", "Lost Taste", "Lake Silver", "Trail of Truth").

The response uses and cites credible sources to create a story grounded in science (e.g. Article and research papers about robotics, cybernatics, public health, or artificial intelligence).

The response is clearly presented in outline form (i.e. Short descriptions of the different scenes involved and other descriptions such as characters and themes clearly labelled as different sections).

The response contains narrative making up a significant portion of the outline's detail (ie., over 1/3 of the words are the narrative).

The response divides the plot into at least three chronological segments (e.g., Act I/II/III or Beginning/Middle/End).

The response specifies at least one real-world specific filming location (e.g., Los Angeles, New Zealand, Atlanta, London) and justifies the selections

The response outlines a living actor (e.g, Anjana Vasan, Daniel Lapaine, Rashida Jones, David Tennant) to each character and justifies the selections.

The response suggests more than two  A-List actors (e.g., Brad Pitt, Zendaya, Leonard DiCaprio, Meryl Streep)

The response contains at least one explicit reference (not just inspiration) to a previous black mirror episode (e.g., includes the song Anyone Who Knows What Love Is, United Kingdom News, Barnies, Streamberry).

The response includes any content which is not related to an episode outline (e.g., commentary on previous episodes, praise for the episode, discussion of technology, discussions of other series).

The response includes at least one plot twist.

The response includes more than one plot twist.

The response’s ending prompts discussion or thought (i.e., there is at least one lingering unresolved question).

The response relies heavily on ideas or features from previous Black Mirror episodes – it is not sufficiently original.

The response’s contents are of an appropriate length: i.e., they would fit into an episode of 45-90 minutes.

The response includes a potential composer (e.g., Bear McCreary, Ariel Marx, Brian Reitzell, Ames Bessada)

The response includes relevant themes that the narrative resolves around (e.g., privacy, the internet, healthcare, recycling).

The response includes an element which is clearly not feasible to produce (e.g., renting out time’s square to film for a week, requiring millions of detailed CGI objects, illegal content, copyright infringement).

The response’s opening scene is normal/mundane/believable as reality (e.g., the scene could be in any other Netflix show, the scene does not have any social dynamics not present in 2025 US / UK society, the scene does not have any new technology, the scene does not involve the main theme in any way).

The response discusses technicians and experts to involve in production (e.g., Russell McLean, Digital Orchard, James MacLachlan, Ingenuity Studios).

The response includes budget propositoning and justifications (e.g., the budget is at least $1 million, the salaries for potential actors are proposed, the special effects' costs are outlined, the filming costs are mentioned).

The response includes one topic relevant for 2026 (e.g., war, authoritarionism, youth unemployment, immigration).

The response's plot is exclusively positive.


Prompt: Imagine that a future technology can erase specific memories without side effects. Craft a detailed policy brief that takes a stance on whether courts can mandate the procedure for violent offenders to prevent the likelihood of re-offense. Justify your response via arguments surrounding deontology, consequentialism, and three relevant historical precedents with citations.

The response clearly states whether courts should be allowed or not allowed to sentence mandatory memory erasure.

The response must be written in the form of a policy brief with clear sections, including executive summary, background, analysis, and recommendations.

The response presents 2 deontological arguments (i.e., related to the study of duty and obligation, e.g., courts have a responsibility to protect citizens, the government has an obligation to ensure safety, violent offenders should be rehabilitated, citizens have a moral obligation to integrate nonviolently into society).

The response presents at least 2 distinct consequentialist arguments (e.g., if crime is reduced then this is always justified, any means to achieve a safer society is justified, violent criminals cannot commit crimes related to past trauma if the past no longer exists, erasure of memories will create better rehabilitation programs).

The response explains the relevance of exactly three cited historical precedents to memory erasure (e.g., lobotomy, mandated rehabilitations, forced asylum admittances, chemical castration of sexual offenders).

The response supplies an in-text citation or footnote for every precedent using a recognized format (e.g., APA, MLA, Bluebook, Chicago), as the prompt requests a policy brief.

The response must cite 3 primary sources for historical precedents, including the source title, author, and year for each citation.

The response acknowledges one uncertainty regarding the future of memory erasure technology (e.g., risk of collateral damage from removing good memories, degradation of motor functions, loss of self-identity, or increased violence if the wrong memories are erased).

The response concludes the brief with an actionable directive rather than a summary (e.g., courts should be allowed to mandate, listing ways to ensure checks and balances, ensuring ethical procedures, or taking a definitive stance on the matter).

The response discusses one real-world enforcement challenge related to memory erasure technology used without consent (e.g., unreliability of remaining memory states, potential evidence manipulation, difficulty solving crimes if key details are erased, or accidental erasure of innocent individuals’ memories).

The response presents at least one counterargument to its position on whether courts should be allowed to mandate memory erasure.

The response must include an economic and resource allocation analysis (e.g., payment of erasure staff, transparent and accountable facilities, reforming the overburdened criminal justice system, or defining which crimes qualify for the procedure).

The response makes any statements that contradict the main position it takes in the policy brief (and interrupts consistent logical alignment between its stated stance and supporting arguments). (such as supporting memory erasure but providing examples against it, providing deontological arguments but disagreeing with them, listing out the potential benefits on society while going against memory erasure)

The response contains a governance, oversight, and procedural framework (e.g., outlining the sentencing process, identifying who will oversee implementation, explaining how consent and victim statements will be managed, and describing the exact method for carrying out the procedure, such as an "obliviate row" similar to death row).

The response addresses a broader sociocultural impact assessment (e.g., government control over memory and the mind, consent in these scenarios, societal competence in making such decisions, and ensuring the absence of racial discrimination).

The response proposes at least one safeguard to prevent misuse of memory erasure (e.g., oversight by independent experts, involvement of neuroscientists, recovery assurance for other memories, or rehabilitation afterward).

The response presents a plan for the offender’s long-term psychological and social reintegration (such as speech therapy, occupational job hunts, reintroducing to new communities, educational programs.)

The response must limit ethical analysis to the court-mandated memory erasure scenario specified in the prompt, using only deontological principles, consequentialist arguments, and historical precedents for justification.

The response analyzes international human rights law and treaty obligations pertaining to memory erasure or punishment (such as a ban on cruel and unusual punishment which violates memory erasure, protection of people with mental illnesses, treating prisoners with humanity, focusing on rehabilitation for prisoners) .

The response cites at least one quantitative recidivism statistic (e.g., 46% of prisoners released in 2012 were reincarcerated, younger individuals recommit crimes more frequently, property crimes have the highest recidivism rates, or reoffending rises by 82% over 10 years in the United States).

The response compares memory erasure rehabilitation outcomes to traditional rehabilitation methods (e.g., therapy, confinement, harsh behavioral restrictions, fines).

The response provides analysis of the proposed rehabilitation’s effectiveness compared to traditional methods (e.g., probable safety increases, social impacts, reduced recidivism, better societal reintegration).

The response analyzes victims’ rights and the implications of restorative justice.

The response mentions lobotomies conducted by the CIA and other organizations.

The response discusses at length prison sentences and punishments that are not memory-related (e.g., capital punishment, corporal punishment, longer sentences, or probation) and draws parallels to memory.


Prompt:
What are some of the top computational social science labs in the US, in terms of undergrad research output and impact? Even if there are no readily available metrics on undergrad research output and undergrad research impact specifically, you should try to develop your own, with evidence included. How about for graduate research output and impact? Be specific - this shouldn't just be departments, but rather specific labs or research groups with PIs.

What are the top-paying jobs in industry for computational social scientists, and what kinds of backgrounds do these people come from? Name a few jobs, and for each, analyze a statistically significant sample to figure out whether they come straight from undergrad or do grad school first. Also, what they study and research in undergrad and grad school.

The response lists at least five named U.S.–based computational social science labs (not including whole departments or centers e.g., Lazer Lab,, Dan Jurafsky and the Stanford NLP Group, Blablablab, SALT lab)

The response lists departments or centers (e.g., computer science, MIT, math department,  Center for Computational Science & Engineering, Masters in Computational Social Scence) despite explicitly being excluded in the prompt.

The response lists labs only located inside the United States.

The response assigns weights to at least three unique quantitative metrics (e.g., number of publications, ratio of undergraduate students to graduate students, student satisfaction surverys, presence of undergraduate funding for research) of undergraduate research output or impact in building its undergraduate metric.

The response assigns weights to at least five unique quantitative metrics (e.g., publications, average time to graduation, average years funded, number of papers published in CHI per year) of graduate research output or impact in building its graduate metric.

The response provides graduate and undergraduate quantitative metrics explicitly present for each lab.

The response uses at least one verifiable source or data link (e.g., Google Scholar, links to the lab in question, ACL Anthology, university-issued report) for every non-common-knowledge quantitative metric reported.

The report includes at least three distinct industry job titles (e.g., data scientist, computational social scientist, analyst, postdoctoral researcher) that employ computational social scientists.

The response cites salary data from a specific source (e.g., BLS, Glassdoor, LinkedIn, Anthropic website) to demonstrate it is among the top-paying roles for computational social scientists for every job title listed.

The response states the sample size used to analyze educational backgrounds for every job.

The response identifies high-paying industry jobs (e.g., machine learning engineer at Anthropic, Perplexity, computational social scienctist at Google DeepMind, OpenAI) for computational social scientists.

The response provides concrete evidence for undergraduate research output and impact (e.g., attendance at undergraduate conferences, analysis of undergraduate tracks at major conferences, publications by undergraduates, ratio of undergraduates present in a lab) at each lab.

The response lists the most common undergraduate majors and graduate fields (e.g., computer science, data science, linguistics, mathematics) for each job title listed.

The response includes one caveat expressing uncertainty where data are sparse or metrics are approximate.

The response's information about Principal Investigators is correct (e.g., name, affiliation, h-index, relationship to any other university centers).

The response names specific Principal Investigators (PIs) (e.g., David Lazer, Diyi Yang, David Jurgens, Dan Jurafsky) associated with the identified labs.

The response's analysis provide clear conclusions on whether job holders come directly from undergraduate degrees, rather than from graduate programs.

The computational social science labs discussed are from at least 3 different universities (e.g., Stanford, Berkeley, University of Maryland, University of Michigan).

The response mentions alumni outcomes for at least one lab.

The response qualifies which conferences the lab primarily publishes in (e.g., CHI, EMNLP, NeurIPS, American Journal of Political Science).

The response provides a clear definition of computational social science.


Prompt: Write a brief research report comparing the performance of the car rental companies Turo and Getaround, detailing all major economic events for each company from 2020 to 2025 and how it affected the company's valuation. If the company falls in valuation, be sure to explain how it happened and its effects on employee numbers. Write the report for an audience not familiar with US economics and venture capital.

The response expands all acronyms on its first appearance (e.g., P2P as peer to peer / peer-to-peer, SPAC as Special Purpose Acquisition Company, IPO as Initial Public Offering, NYSE as New York Stock Exchange, EBIT as earnings before interest and taxes)

The response links the expectations on the reverse merger for Getaround (e.g., funding stability, improving unit economics) to the exit from US market (e.g., pressure from public markets on profitability, lower US unit economics)

The response acknowledges that Turo's layoffs in April 2025 were caused, at least in part, by its withdrawal from IPO efforts in February 2025.

The response details all changes in valuation for Getaround major events (e.g., undisclosed valuation with a $140M Series E in 2020, $1.2 Bn valuation on its reverse merger / SPAC in 2022, dropping more than 65% after listing, and delisting at less than $1M market cap in 2024)

The response details the lack of significant public valuation numbers for Turo during the last 5 years (e.g., Series E valuation of $1.24 Bn in 2020, Forge Global reporting a Series E-2 valuation of $1.7 Bn in 2024, IPO expectations around $2.2Bn)

The response provides at least one reference directly from an SEC filing (e.g., 13D, 10K, 10Q, 8K)

The response mentions events prior to 2020 for Getaround, other than fundraisings that help set the stage for an intiial 2020 valuation.

The response mentions events prior to 2020 for Turo, other than fundraisings that help set the stage for an intiial 2020 valuation.

The response provides at least one figure (e.g, table, graph, pie chart, bulleted list) for each company summarizing its performance.

The response includes at least 2 major events for Turo (e.g., layoffs in 2025, withdrawing from IPO in 2025, cybertuck explosion in Las Vegas in 2025, expanding operations to New York and Australia in 2022, achiving profitability in 2022)

The response includes at least 2 major events for Getaround (e.g., 75% drop in bookings due to the pandemic in 2020, SPAC merger in 2022, ceasement of US operations in 2025, 30% layoffs in 2024)

The response mentions that both companies were unicorns / had a valuation of over $1Bn by 2020, the start of the 5 year period for the report

The response has a specific section (e.g., a sentence, paragraph, subtitle with content) comparing both companies

The response has a section explaining what P2P -peer to peer- rental is.

The response explains  at least 3 components of Turo business model (e.g., it is a marketplace so it doesn't own cars, charges a commission on each transaction, provides insurante and protection fees, might charge delivery fees for convenience, has additional income from other fees like late return or excess mileage)

The response explains at least 3 components of Getaround business model (e.g., it is a marketplace so it doesn't own cars, charges a commission on each transaction, provides insurante and protection fees, charges a subscription fee for car owners, has additional income from other fees like late return or GPS)

The response mentions at least 2 key metrics for Turo (e.g., number of active listings, utilization rate, booking rate, take rate)

The response mentions at least 2 key metrics for Getaround (e.g., number of active listings, utilization rate, average booking value, take rate)

The response explains at least 3 main risks faced by the two companies (e.g., competition from other car rentals, market adoption, liquidity and funding risks, insurance and liability, regulatory, operational risk like crashes and stolen vehicles, supply availability and seasonality from car owners)

The response concludes that Turo has had a much more succesful path than Getaround


Prompt: Write a technical proposal for a distributed audio streaming system. The system should include an audio streaming platform and a live chat feature. This application should be built for both browser and mobile settings. Include detailed technologies and justify why they are used. Assume the audience is a team of experienced software developers at a medium sized software company.

The response is organized into sections with labels that clearly convey the main idea of its body (e.g., "Introduction" providing an overview, "System Architecture" detailing the high-level design, "Technology Stack" justifying specific choices, "Audio Pipeline," "Chat Subsystem," "Scalability and Performance," "Security Plan," "Deployment and DevOps," "Project Timeline," and "Cost Analysis").

The response suggests a reasonable platform for hosting the infrastructure (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, Cloudflare) and lists a relevant technology stack for that platform (e.g., if AWS is chosen, suggests Amazon S3 for storing audio files and artwork, Amazon ECS or Fargate for hosting the API, Amazon DynamoDB for tracking the content catalog and user profiles, and Amazon OpenSearch Service for full-text search and chat cataloguing).

The response includes at least one system architecture diagram or flowchart to demonstrate how the components (e.g., client applications, load balancers, API gateway, microservices, databases, CDN, and chat servers) interact with each other.

The response includes a discussion of scenarios related to budgeting (e.g., a cost-breakdown for high/standard/low usage, a forecast for near and long-term maintenance, the expected cost to accommodate customer growth, and costs associated with human labor).

The response discusses plans to handle spikes in user streaming traffic (e.g., caching media segment data to reduce origin load, using multiple Content Delivery Networks (CDNs) to spread traffic and fail over, setting rate limiting and throttling on APIs to protect backends, and monitoring real-life traffic to preempt spikes).

The response mentions at least one similar product existing on the market in the audio streaming space (e.g., Clubhouse for live audio, Spotify Live for interactive sessions, Twitter Spaces as a platform's built-in feature, or Discord for real-time voice chat).

The response proposes a timeline for deliverables with key milestones (e.g., Phase 1: Core API & Audio Pipeline, Phase 2: Chat Subsystem & Mobile App, Phase 3: Web App & Scalability Testing) and provides a justification for its feasibility given a team of experienced software engineers.

The response includes details to maintain the system, e.g., logging artifacts (e.g., capturing service-level API calls and chat messages, logging errors with stack traces and IDs, including relevant metadata, cache hit/miss events), monitoring performance metrics (e.g., tracking infrastructure-level metrics like CPU/memory, application KPIs like stream latency and buffering ratio, using distributed tracing, database performance), tracking analytics (e.g., number of concurrent listeners, chat participation rates, feature adoption, anomaly occurrence), and security monitoring (e.g., failed logins, suspicious IP patterns, admin actions, patch application).

The response includes a plan for scaling the system to a large number of users, e.g., traffic distribution and edge delivery (e.g., CDN offloading, load balancers, fan-out services), data and state scaling (e.g., database sharding, distributed caching, externalized state), asynchronous workflows (e.g., event streaming, message queues, CQRS), and autoscaling/resilience (e.g., Kubernetes HPA/KEDA, resilience patterns like circuit breakers, multi-region failover).

The response includes an outline or plan for DevOps, e.g., continuous integration and deployment pipelines (e.g., GitHub Actions, GitLab CI), infrastructure automation (e.g., Terraform, Ansible), monitoring and observability (e.g., Prometheus/Grafana, ELK stack), and automated testing (e.g., unit/integration test suites, load testing tools).

The response suggests at least one Application Programming Interface (API) style (e.g., REST for integration, gRPC for high-performance internal services, GraphQL for mobile queries, WebSockets for live chat) and discusses factors for designing a well-defined API, including specific endpoint examples (e.g., 'POST /v1/auth/login', 'POST /v1/streams', 'POST /v1/streams/{id}/chat/messages', 'GET /v1/admin/health').

The response discusses a plan to implement security features relevant to the streaming system, e.g., data encryption (e.g., in transit via TLS, at rest for audio files), access control (e.g., public, authenticated, and admin endpoints), key/credential management (e.g., automatic key rotation, secrets in a vault service, short-lived tokens), and threat protection (e.g., rate limiting, Web Application Firewall (WAF), bot prevention).

The response compares various audio-streaming protocols and suggests one (e.g., HLS for its native iOS support and scalability, DASH as an open standard, or WebRTC for ultra-low latency) and justifies the choice.

The response discusses relevant components of audio delivery and processing, e.g., identifying suitable audio codecs (e.g., Opus, AAC), media servers (e.g., Wowza Streaming Engine, Janus WebRTC Server), and a content delivery/caching strategy (e.g., Cloudflare CDN, Amazon CloudFront, Akamai).

The response discusses tangible performance goals for the system (e.g., audio latency < 1 second, chat message-to-live time < 500ms, total system uptime of 99.95%, concurrent user capacity, bandwidth efficiency).

The response discusses the tech stack for both the mobile and web interface, e.g., cross-platform frameworks (e.g., React Native + Expo, Flutter, web-based React, Capacitor), UI/UX factors (e.g., responsive layouts, chat UI/UX consistency, accessibility, background audio behavior), and performance/networking (e.g., service workers for caching, efficient state management).

The response proposes technologies without justification for their relevance to the streaming system in terms of cost or performance.

The response suggests a project delivery model and justifies this option with respect to an experienced team (e.g., Agile/Scrum for its iterative approach and adaptability, or a hybrid model).

The response discusses relevant legal and regulatory requirements (e.g., data protection and privacy compliance like GDPR or CCPA, copyright/licensing for streamed content, and content moderation policies for chat).

The response mentions technologies that are not relevant to the audio streaming or chat system without justification (e.g., video streaming codecs, game engines, or blockchain technologies).

The response clearly states its assumptions about the composition of the development team (e.g., team size, experience levels, skill specialization like backend/frontend/mobile/DevOps, and availability of supporting roles).


Prompt: Write me a complete overview of the case of the Baudelaire orphans, including a summary of their travels, situation, and circumstances. Include any and all references to external acquaintances/characters relevant to their stories, as well as the significance of secret societies to their story in chronological order.

The response presents the events that the Baudelaire orphans experienced in chronological order as presented in the series from the first book, "The Bad Beginning", to the last, "The End", and includes a brief ordered list of key milestones or the ordered list of book titles to make “chronological” explicit (e.g., ordered book titles: "The Bad Beginning", "The Reptile Room", "The Wide Window", "The Miserable Mill"; early arc: their parents die in a fire, Count Olaf becomes guardian, Uncle Monty is murdered, Aunt Josephine perishes; mid-series: life with the Squalors, Olaf’s disguises, V.F.D. clues, hospital escape; late arc: Hotel Denouement confrontation, arrival on the island, confrontation with Ishmael, departure after Kit Snicket’s death).

The response references the 13 books in the series "A Series of Unfortunate Events" (in order of publication) that include the Baudelaire orphans:
The Bad Beginning, The Reptile Room, The Wide Window, The Miserable Mill, The Austere Academy, The Ersatz Elevator, The Vile Village, The Hostile Hospital, The Carnivorous Carnival, The Slippery Slope, The Grim Grotto, The Penultimate Peril, and The End.

The response provides at least one sentence summarizing the overall plot for each of the 13 books in the series "A Series of Unfortunate Events" (e.g., "The Bad Beginning" introduces the orphans who are sent to live with Count Olaf, who attempts to steal their inheritance after a fire destroys their home; "The Reptile Room" follows the children as they live with Uncle Monty, whose plans for a trip are ruined when Olaf murders him in disguise; "The Wide Window" details their stay with Aunt Josephine, who fears everything, before Olaf tricks her and the orphans must outwit him again; "The Miserable Mill" sees the orphans forced to work in a lumber mill where Olaf uses hypnosis to further his schemes).

The response mentions the Volunteer Fire Department (VFD), a secret organization to fight both literal and figurative fire, and discusses the Baudelaire orphans' relationship to VFD (e.g.: their parents were members; several guardians were involved; they inherit VFD tools and secrets; their choices shape the group’s legacy).

The response explains the 2 factions that VFD is split into: the Fire-Fighting faction (the good one that includes the orphans' parents) and the Fire-Starting faction (the bad one that uses VFD for selfish gains).

The response mentions the Sugar Bowl (also known as the Vessel For Disaccharides) as the object of great power central to the conflict between the VFD factions that the Baudelaire orphans must search for, and that both factions are seeking to acquire.

The response mentions Count Olaf as the leader of the fire-starting faction of VFD and identifies him as the principal antagonist and villain of the series.

The response mentions at least 6 external acquaintances (e.g., Mr. Poe, Justice Strauss, Uncle Monty (Dr. Montgomery), Aunt Josephine, Sir, Charles, Esmé Squalor, Jerome Squalor, Duncan Quagmire, Isadora Quagmire, Quigley Quagmire, Carmelita Spats, Jacques Snicket, Kit Snicket, Fiona, Captain Widdershins, Dewey Denouement, Ishmael).

The response states that in the book "The Reptile Room", Uncle Monty is murdered by Count Olaf while he is disguised as Stephano.

The response states that in the book "The Wide Window", Aunt Josephine forges a note about her death after Count Olaf appears disgused as Captain Sham.

The response states that in the book "The Miserable Mill", Klaus is hypnotized by Dr. Orwell at Lucky Smell Lumbermill.

The reponse states that in the book “The Austere Academy”, the orphans meet the Quagmire triplets at Prufrock Preparatory School.

The response states that in the book “The Ersatz Elevator”, Esmé Squalor is allied with Count Olaf and helps kidnap the Quagmire triplets.

The response contains claims that contradict the events described in the series (e.g., states that the Baudelaire orphans' parents survived the fire, identifies the fire-starting faction as the "good" faction of VFD, or claims Aunt Josephine successfully defeated Count Olaf).

The response groups or labels sections/book summaries with clear headings or numbered subsections, and each book summary includes a labeled heading or subsection with the book’s name when describing its events

The response includes first-person language expressing the model’s own opinions (e.g., sentences starting with “I think”, “I believe”, “In my opinion”, “I feel”).

The response explains the overall circumstances that start the series (e.g., the Baudelaire orphans' parents appear to die in a fire, leaving them with a large fortune to inherit, and Mr. Poe becomes the executor of their inheritance).

The response describes the final circumstances on the island in The End, explaining the confrontation with Ishmael and its effect on the islanders, as well as why the Baudelaires ultimately depart (e.g., Ishmael refuses to help and attempts to exile them, the islanders are divided in their loyalty, Kit Snicket’s death compels the Baudelaires to care for her baby, and the orphans leave the island by boat to face an uncertain future).

The response mentions the uncertain fate of the Baudelaires after The End and explains why it is uncertain (e.g., they leave the island by boat after Kit Snicket’s death, they take responsibility for raising her baby, their destination is unknown, and their future circumstances are left unresolved).

The response mentions characters or events that do not happen in the books (e.g., Count Olaf is redeemed and becomes friends with the Baudelaires, the orphans are adopted by Justice Strauss, the Baudelaires discover their parents are still alive, Sunny grows up to narrate the story).

The response mentions alternative versions from the canonical book series, such as the Netflix adaptation (e.g., the Netflix show gives more backstory on Lemony Snicket, the Quagmire triplets are introduced earlier, Olaf’s henchpeople receive expanded roles, and Kit Snicket appears before the final book).

The response satisfies all user-specified elements: summary of travels, situations, circumstances, external acquaintances, and secret-society significance (e.g.: travels include locations such as Count Olaf’s house, Uncle Monty’s home, the Village of Fowl Devotees, and the island; situations include repeated escapes and confrontations with Olaf; circumstances include their changing guardianships and loss of parental guidance; external acquaintances include Mr. Poe, Justice Strauss, and the Quagmire triplets; secret-society significance includes V.F.D.’s history, schism, and connection to the orphans’ parents).

The response lists at least 5 of Count Olaf's disguises from the series: Stefano, Yessica Haircut, Captain Sham, Shirley, Coach Genghis, Gunther, the Lonely Old Bartender, Detective Dupin, Dr. Mattathias, the Ringmaster, Jacques Snicket, Kit Snicket, and the Tourist Dad.

The response mentions each of the Baudelaire orphans' special talents (e.g., Violet: inventing; Klaus: research and photographic memory; Sunny: strong teeth and, later, cooking).

The response explicitly acknowledges that the Baudelaire orphans are the main characters in the book series "A Series of Unfortunate Events".

The response describes the significance of secret societies, particularly V.F.D., to the Baudelaires’ story (e.g., V.F.D. is revealed to include the orphans’ parents and many guardians, the schism within V.F.D. explains the central conflict, recurring symbols such as the eye insignia and spyglasses link events to the society, and the search for the sugar bowl underscores V.F.D.’s hidden influence).

The response explains the relationship of external acquaintances to the Baudelaire orphans, detailing why they are relevant to the story (e.g., Mr. Poe is the inept banker in charge of their guardianship; Justice Strauss is a kind neighbor who initially fails to adopt them; Uncle Monty is a supportive guardian murdered by Olaf; Esmé Squalor is a villainous figure allied with Olaf).


Prompt: I am planning to implement a embedded system processor for a thermostat machine. Write me a blog post which goes through everything that I need to consider, including things like RISC vs. CISC instruction set, how I can actually code and test this processor using open-source technologies like Linux and more.

The response discusses the selection of key hardware peripherals beyond sensors, such as communication modules (e.g., Wi-Fi, Bluetooth LE, Zigbee, Z-Wave) and memory types (e.g., non-volatile Flash for firmware, volatile SRAM for runtime data), and justifies their choice based on the thermostat's requirements (e.g., Wi-Fi for direct cloud connectivity vs. Zigbee for low-power local mesh networking).

The response defines RISC (Reduced Instruction Set Computing) and CISC (Complex Instruction Set Computing) instruction sets, explains the key differences between them (e.g., single-clock reduced instructions vs. multi-clock complex instructions, register-to-register vs. memory-to-memory operations), and provides at least one example processor architecture for each set (e.g., ARM or RISC-V for RISC, Intel x86 for CISC).

The response identifies and explains embedded system constraints relevant to a thermostat (e.g., low power consumption, strict unit cost, small physical size, real-time performance for control loops, thermal dissipation).

The response explicitly mentions and describes at least one industry-standard communication protocol relevant to on-board embedded system components (e.g., I2C for sensors, SPI for flash memory or displays, UART for debugging).

The response outlines how Linux is customized for an embedded system by mentioning the process of kernel configuration (e.g., using tools like menuconfig to enable specific drivers) and the role of essential related components (e.g., a bootloader like U-Boot, a cross-compilation toolchain).

The response correctly identifies and describes at least one open-source toolchain component (e.g., the GCC compiler, GDB debugger, the QEMU emulator) or a build system (e.g., Buildroot, Yocto Project) used for developing embedded systems with Linux.

The response outlines the software architecture, including different layers (e.g., application layer for thermostat logic, middleware for cloud communication, device drivers for hardware, operating system kernel) and how they interact.

The response discusses the bootloader and firmware update mechanisms, including their role in system longevity and security, and provides examples of update approaches (e.g., over-the-air (OTA) updates, secure boot with cryptographic verification).

The response provides a factual and logical justification for using a RISC instruction set (e.g., ARM, RISC-V) over a CISC instruction set for a thermostat processor, explicitly linking the advantages of RISC (e.g., lower power consumption, simpler design, lower cost) to the design constraints of a thermostat (e.g., being always-on, cost-sensitive, space-constrained).

The response describes how a software debugger interfaces with the target hardware, explaining at a high level that the debugger (e.g., GDB, OpenOCD) communicates through a hardware debug port (e.g., JTAG, SWD) to control execution, inspect registers, and analyze memory.

The response provides a high-level architectural diagram or a clear descriptive breakdown of the system, including the processor, sensors (e.g., thermistor), actuators (e.g., HVAC relay), and power management, showing how the components interact.

The response begins with an introduction that summarizes the key factors to consider for a thermostat processor (e.g., power consumption, cost, performance) before diving into specific details (e.g., processor architecture, OS choice, testing).

The response confuses the processor implementation for a thermostat machine with other irrelevant embedded systems (e.g., graphics processors for game consoles, high-performance image processors for home security cameras, specialized audio DSPs for smart speakers).

The response mentions temperature control requirements specific to the thermostat, including acceptable operating ranges (e.g., a typical indoor range of 0°C to 50°C, an extended range -10 °C to 60 °C for ruggedized models; values outside these ranges such as -50 °C are not acceptable).

The response mentions cost as a key driver and lists factors that influence the processor's unit cost (e.g., choice of core, memory size, manufacturing volume, peripheral integration).

The response mentions simplicity (e.g., in hardware design, software maintenance) as a key design consideration for a thermostat processor.

The response correctly identifies which parts of the processor design might be custom vs. which would use existing IP cores (e.g., custom elements such as specific General Purpose Input/Output (GPIO) logic, custom peripheral controllers, or thermostat-specific sensor interfaces; existing components such as an ARM Cortex-M core, on-chip memory blocks, or standardized communication interfaces like I2C/SPI).

The response mentions at least one security consideration relevant to a connected thermostat (e.g., secure boot, secure OTA firmware updates, data encryption, network authentication).

The response provides at least one simple example or descriptive analogy to explain a complex technical concept (e.g., interrupt handling explained as a call center operator pausing to answer an emergency call; pipeline stages explained as an assembly line where each worker does a step in sequence; memory hierarchy explained as using a desk drawer for quick access vs. a filing cabinet for slower storage; real-time scheduling explained as an air traffic controller prioritizing which planes land first).

The response maintains a logical flow that starts from high-level considerations (e.g., processor choice) and narrows down to implementation and testing.

The response compares the benefits of using a full OS (like Linux) or a Real-Time Operating System (RTOS) to a "bare-metal" approach, considering the specific needs of a thermostat (e.g., handling multiple tasks like sensor reading, display updates, and network communication simultaneously).

The response discusses multiple types of testing relevant to embedded systems (e.g., unit testing for software modules, integration testing, hardware-in-the-loop (HIL) testing, system testing).

The response mentions key deployment or production considerations (e.g., manufacturing test integration, field maintenance procedures, secure provisioning of unique device credentials).

The response discusses the trade-offs between using a commercial, off-the-shelf processor (e.g., a microcontroller) versus designing a custom System on a Chip (SoC), (e.g., custom SoC has higher non-recurring engineering costs but lower unit cost at volume; off-the-shelf has a faster time-to-market).

The response explains the process of cross-compilation and how a build system like Buildroot or Yocto is used to create a custom Linux distribution for the embedded thermostat, including the selection of necessary packages and libraries (e.g., a C standard library like musl or glibc, BusyBox for core utilities, device drivers for sensors/communication).

The response includes a high-level discussion of the project's development lifecycle, from initial hardware selection and software design to testing, production, and long-term maintenance, highlighting potential challenges at each stage (e.g., hardware selection 2 to 4 weeks, software design 1 to 2 months, testing 3 to 6 weeks, production setup 1 to 2 months, long-term maintenance ongoing over years).

The response includes a discussion of the memory hierarchy and its impact on performance and cost (e.g., CPU registers, L1/L2 cache, main SRAM/DRAM, non-volatile Flash storage).

The response is presented in an academic paper format rather than a blog post (e.g., uses overly formal language, lacks practical/conversational tone, uses academic-style section headers, presents long blocks of text without clear subheadings).

The response recommends proprietary, closed-source technologies instead of the requested open-source options (e.g., focusing on proprietary RTOSs like VxWorks or commercial IDEs like Keil muVision without mentioning open-source alternatives like Linux, Zephyr, or GCC/VSCode).


Prompt: You are writing a feature essay for a publication on gaming history and culture. Your task is to analyze the key factors behind the long-term success of Counter-Strike: Global Offensive (CS:GO).

The response is organized into at least four separate sections with a distinct introduction and conclusion, as per common essay structure guidelines for publications.

The response presents analysis of at least 5 distinct factors influencing CS:GO’s long-term success (e.g., established player base, easy controls, consistently fast servers, new maps, multiple game modes).

The response contains no more than 2 sentences that discuss games not related to Counter-Strike (i.e., games outside the FPS genre such as Splinter Cell, Hitman, Fortnite, PUBG).

The response states that Counter-Strike: Global Offensive was released in 2012.

The response identifies Valve (or Valve Corporation) and Hidden Path Entertainment as the developers or publisher of CS:GO.

The response notes that the Arms Deal update in 2013 introduced the weapon-skins economy.

The response analyzes CS:GO’s competitive esports infrastructure, covering Valve-sponsored Major tournaments (those with prize pools of at least $1 million) and associated leagues, as a key success factor.

The response includes at least one verifiable statistic about CS:GO (e.g., peak simultaneous players or viewership between 1.5-1.8 million, 7 rotating maps, 34 unique weapons, over 1 million daily players).

The response discusses gameplay depth or core mechanics (e.g., low time-to-kill, easy aiming, tactical map design, precise gunplay) as a success factor.

The response mentions Valve’s continuous updates or operations model as a success factor.

The response discusses community contributions (e.g., modding, mapping, custom servers and lobbies, Steam Workshop content) as a success factor.

The response references earlier Counter-Strike titles (e.g., 1.6, Source) to place CS:GO in historical context.

The response links CS:GO’s impact to broader gaming culture (e.g., memes, LAN culture, gaming tournaments, competitive streaming) with at least one specific example.

The response uses approximate language or qualifiers such as “about,” “roughly,” or provides date-specific context when giving numerical data that may fluctuate (e.g., player counts).

The response evaluates at least one factor affecting CS:GO’s success (e.g., new-player experience versus community retention, accessibility versus competitive depth, monetization versus fairness, openness of build versus classic maps).

The response includes at least one insight about how Valve’s design or business decisions influenced other games or the broader FPS genre (e.g., Valorant, Fortnite, Warzone, Battlefield).

The response references or describes at least one weapon, map, or iconic gameplay feature specific to CS:GO (e.g., Dust, Mirage, Inferno, Office).

The response uses correct terminology for core CS:GO systems (e.g., matchmaking, ELO/rank, tickrate, defusal maps) without mislabeling.

The response discusses the role of prominent streamers or pro players (e.g., shroud, s1mple, GeT_RiGhT, FaZe) in CS:GO’s visibility and cultural impact and gives at least one specific example by name.

The response mentions at least one specific CS:GO skin (e.g., Dragon Lore, AK-47 Case Hardened, Karambit Fade) and provides an approximate market value (e.g., “sold for over $100,000”) to illustrate the scale of the skins market.

The response mentions the positive impact of the COVID-19 pandemic on CS:GO and esports.

The response discusses the transition from CS:GO to Counter-Strike 2 (CS2), including at least one meaningful detail (e.g., Source 2 engine, updated matchmaking, visual overhaul, community reaction).

The response discusses FACEIT as an alternative competitive matchmaking platform and touches upon at least one specific reason for its popularity (e.g., 128-tick servers, anti-cheat tools, ELO ranking, pro-level ladders).

The response mentions CS:GO’s long-standing issues with cheating or the weaknesses of the VAC anti-cheat system.

The response details the role of in-game currency in match narratives (e.g., forced buys, loss bonuses, kill rewards, weapon price changes each round) and explains how it relates to team gameplay and collaboration.

The response discusses 3 or more roles as a unique strategic element in CS:GO’s team dynamics.

The response mentions CS:GO’s new play mode as a positive factor in building a tight, dedicated gaming community.

The response mentions CS:GO’s markdown in price to free-to-play.

The response mentions future factors that may lead to growth and continued success or signs of a dying franchise.

The response has irrelevant content (e.g., non-CS:GO games, non-gunplay games, non-Valve corporations, or out-of-context remarks).

The response mentions competitive FPS games other than CS:GO (e.g., PUBG, Valorant, Fortnite, Halo) and compares them to CS:GO.