scan-v5.json (30047B)
1 { 2 "scan_version": 5, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "Generative Agents: Interactive Simulacra of Human Behavior", 6 "authors": [ 7 "Joon Sung Park", 8 "Joseph C. O'Brien", 9 "Carrie J. Cai", 10 "Meredith Ringel Morris", 11 "Percy Liang", 12 "Michael S. Bernstein" 13 ], 14 "year": 2023, 15 "venue": "ACM Symposium on User Interface Software and Technology", 16 "arxiv_id": "2304.03442", 17 "doi": "10.1145/3586183.3606763" 18 }, 19 "checklist": { 20 "claims_and_evidence": { 21 "abstract_claims_supported": { 22 "applies": true, 23 "answer": true, 24 "justification": "The abstract claims agents produce believable individual and emergent social behaviors; these are directly tested via a controlled ablation study (TrueSkill d=8.16) and end-to-end simulation (information diffusion, relationship formation, coordination).", 25 "source": "haiku" 26 }, 27 "causal_claims_justified": { 28 "applies": true, 29 "answer": true, 30 "justification": "The ablation study systematically removes memory, reflection, and planning components and measures believability with statistical tests; the paper acknowledges that giving ablated conditions the same memories yields conservative estimates.", 31 "source": "haiku" 32 }, 33 "generalization_bounded": { 34 "applies": true, 35 "answer": false, 36 "justification": "The title claims 'simulacra of human behavior' broadly, but evaluation is limited to 25 agents in a single Sims-like sandbox over 2 game days; the discussion extrapolates to social computing, VR, and ubiquitous computing without supporting evidence.", 37 "source": "haiku" 38 }, 39 "alternative_explanations_discussed": { 40 "applies": true, 41 "answer": false, 42 "justification": "The paper does not consider whether longer prompt context (rather than the specific architectural components) drives the believability improvement; failure modes are discussed but competing interpretations of main results are not.", 43 "source": "haiku" 44 }, 45 "proxy_outcome_distinction": { 46 "applies": true, 47 "answer": true, 48 "justification": "The paper consistently frames 'believability' as the dependent variable from prior work and makes claims at that level; the limitations section explicitly acknowledges the crowdworker baseline does not represent maximal human performance.", 49 "source": "haiku" 50 } 51 }, 52 "limitations_and_scope": { 53 "limitations_section_present": { 54 "applies": true, 55 "answer": true, 56 "justification": "Section 8.2 is titled 'Future Work and Limitations' and contains a substantive multi-paragraph discussion.", 57 "source": "haiku" 58 }, 59 "threats_to_validity_specific": { 60 "applies": true, 61 "answer": true, 62 "justification": "Specific threats are named: evaluation limited to short timescale, crowdworker baseline not maximal human performance, ablation confound (ablated conditions share same memory path), and robustness to prompt/memory hacking is unknown.", 63 "source": "haiku" 64 }, 65 "scope_boundaries_stated": { 66 "applies": true, 67 "answer": true, 68 "justification": "The paper explicitly states assessment 'was limited to a relatively short timescale,' that robustness testing is future work, and that the crowdworker condition 'did not represent the maximal human performance that could serve as the gold standard.'", 69 "source": "haiku" 70 } 71 }, 72 "conflicts_of_interest": { 73 "funding_disclosed": { 74 "applies": true, 75 "answer": true, 76 "justification": "Acknowledgments disclose Microsoft Research PhD Fellowship, Stanford HAI, Google Research, HPDTRP, Siegel Family Endowment, and OpenAI as funders.", 77 "source": "haiku" 78 }, 79 "affiliations_disclosed": { 80 "applies": true, 81 "answer": true, 82 "justification": "Author affiliations are disclosed on the title page: Stanford University (Park, O'Brien, Liang, Bernstein), Google Research (Cai), and Google DeepMind (Morris).", 83 "source": "haiku" 84 }, 85 "funder_independent_of_outcome": { 86 "applies": true, 87 "answer": false, 88 "justification": "OpenAI provided funding support while the paper evaluates OpenAI's ChatGPT (gpt-3.5-turbo) as the underlying model; the funder has a direct commercial interest in the evaluated system.", 89 "source": "haiku" 90 }, 91 "financial_interests_declared": { 92 "applies": true, 93 "answer": false, 94 "justification": "No competing interests statement or declaration of financial interests (patents, equity, consulting) appears beyond the acknowledgment of funding sources.", 95 "source": "haiku" 96 } 97 }, 98 "scope_and_framing": { 99 "key_terms_defined": { 100 "applies": true, 101 "answer": true, 102 "justification": "'Generative agents' are defined in the introduction; 'believability/believable agents' is defined with reference to the Disney-character tradition from prior literature; the three architectural components (memory stream, reflection, planning) are each formally defined.", 103 "source": "haiku" 104 }, 105 "intended_contribution_clear": { 106 "applies": true, 107 "answer": true, 108 "justification": "The introduction explicitly lists four contributions: the generative agent concept, the novel architecture, two evaluations, and a discussion of ethical/societal risks.", 109 "source": "haiku" 110 }, 111 "engagement_with_prior_work": { 112 "applies": true, 113 "answer": true, 114 "justification": "Section 2 covers four decades of believable agent research (rule-based, learning-based, cognitive architectures) and explicitly situates the contribution relative to each tradition, noting where prior approaches fall short and how this work extends them.", 115 "source": "haiku" 116 } 117 } 118 }, 119 "type_checklist": { 120 "empirical": { 121 "artifacts": { 122 "code_released": { 123 "applies": true, 124 "answer": true, 125 "justification": "Footnote 2 provides a public GitHub link to the simulation code: https://github.com/joonspk-research/generative_agents.", 126 "source": "haiku" 127 }, 128 "data_released": { 129 "applies": true, 130 "answer": false, 131 "justification": "Human evaluation rankings from 100 participants and simulation memory stream logs are not released as a public dataset.", 132 "source": "haiku" 133 }, 134 "environment_specified": { 135 "applies": true, 136 "answer": false, 137 "justification": "The paper mentions gpt-3.5-turbo and the Phaser web framework but provides no requirements.txt, Dockerfile, or equivalent dependency specification.", 138 "source": "haiku" 139 }, 140 "reproduction_instructions": { 141 "applies": true, 142 "answer": false, 143 "justification": "The paper provides architectural descriptions and sample prompts but no step-by-step reproduction instructions sufficient to recreate the simulation or evaluation without consulting the external GitHub repository.", 144 "source": "haiku" 145 } 146 }, 147 "statistical_methodology": { 148 "confidence_intervals_or_error_bars": { 149 "applies": true, 150 "answer": true, 151 "justification": "TrueSkill ratings report both μ and σ for each condition (e.g., full architecture: μ=29.89, σ=0.72), providing uncertainty estimates for the main results.", 152 "source": "haiku" 153 }, 154 "significance_tests": { 155 "applies": true, 156 "answer": true, 157 "justification": "A Kruskal-Wallis test (H(4)=150.29, p<0.001) and Dunn post-hoc tests with Holm-Bonferroni correction are applied to the rank data.", 158 "source": "haiku" 159 }, 160 "effect_sizes_reported": { 161 "applies": true, 162 "answer": true, 163 "justification": "Cohen's d=8.16 is reported for the comparison between the full architecture and the no-memory baseline, derived from TrueSkill N(μ,σ²) model.", 164 "source": "haiku" 165 }, 166 "sample_size_justified": { 167 "applies": true, 168 "answer": false, 169 "justification": "100 evaluators were recruited from Prolific but no power analysis or justification for this sample size is provided.", 170 "source": "haiku" 171 }, 172 "variance_reported": { 173 "applies": true, 174 "answer": true, 175 "justification": "TrueSkill σ is reported for all five conditions in the controlled evaluation; though the end-to-end emergent behavior results lack variance, the primary quantitative claims include spread.", 176 "source": "haiku" 177 } 178 }, 179 "evaluation_design": { 180 "baselines_included": { 181 "applies": true, 182 "answer": true, 183 "justification": "Three ablation baselines and a human crowdworker baseline are included; the fully ablated condition represents prior LLM-only state of the art.", 184 "source": "haiku" 185 }, 186 "baselines_contemporary": { 187 "applies": true, 188 "answer": true, 189 "justification": "The no-memory condition represents the then-current LLM-as-agent state of the art, citing 2023 contemporaries (Binz & Schulz, Horton, Park et al. 2022).", 190 "source": "haiku" 191 }, 192 "ablation_study": { 193 "applies": true, 194 "answer": true, 195 "justification": "Three ablations systematically remove reflection, planning, and observation components; monotonically decreasing believability across ablations establishes each component's contribution.", 196 "source": "haiku" 197 }, 198 "multiple_metrics": { 199 "applies": true, 200 "answer": true, 201 "justification": "Controlled evaluation uses believability rankings (TrueSkill); end-to-end evaluation measures information diffusion (%), network density, and coordination (party attendance), covering multiple dimensions.", 202 "source": "haiku" 203 }, 204 "human_evaluation": { 205 "applies": true, 206 "answer": true, 207 "justification": "100 participants recruited from Prolific evaluated agent interview responses by ranking believability of 5 conditions in a within-subjects design.", 208 "source": "haiku" 209 }, 210 "held_out_test_set": { 211 "applies": false, 212 "answer": false, 213 "justification": "Not applicable; this is not a prediction task with a train/test split.", 214 "source": "haiku" 215 }, 216 "per_category_breakdown": { 217 "applies": true, 218 "answer": false, 219 "justification": "The five interview categories (self-knowledge, memory, plans, reactions, reflections) are analyzed qualitatively but quantitative TrueSkill scores are reported only in aggregate, not broken down by category.", 220 "source": "haiku" 221 }, 222 "failure_cases_discussed": { 223 "applies": true, 224 "answer": true, 225 "justification": "Section 6.5.2 details memory retrieval failures and hallucination types; Section 7.2 describes three modes of erratic behavior (location selection errors, norm misclassification, instruction-tuning overcoopertiveness).", 226 "source": "haiku" 227 }, 228 "negative_results_reported": { 229 "applies": true, 230 "answer": true, 231 "justification": "The crowdworker condition performed statistically indistinguishably from the fully ablated baseline—a negative finding; 7 of 12 invited agents did not attend the party, reported without spin.", 232 "source": "haiku" 233 } 234 }, 235 "setup_transparency": { 236 "model_versions_specified": { 237 "applies": true, 238 "answer": true, 239 "justification": "The paper specifies 'gpt3.5-turbo version of ChatGPT' as the underlying model.", 240 "source": "haiku" 241 }, 242 "prompts_provided": { 243 "applies": true, 244 "answer": true, 245 "justification": "Multiple complete prompts are provided in the paper body: importance scoring, reflection generation, daily planning, reaction/replan, dialogue generation, and environment traversal.", 246 "source": "haiku" 247 }, 248 "hyperparameters_reported": { 249 "applies": true, 250 "answer": false, 251 "justification": "Decay factor (0.995), reflection threshold (150), and α weights (all 1) are reported, but LLM generation parameters (temperature, top-p) are not reported.", 252 "source": "haiku" 253 }, 254 "scaffolding_described": { 255 "applies": true, 256 "answer": true, 257 "justification": "The agent architecture is described in detail across Section 4 (memory stream with retrieval scoring formula, reflection mechanism, planning/reacting modules) and Section 5 (sandbox server implementation).", 258 "source": "haiku" 259 }, 260 "data_preprocessing_documented": { 261 "applies": true, 262 "answer": true, 263 "justification": "Preprocessing of initial agent descriptions (semicolon-delimited phrases entered as seed memories) and environment representation (tree structure converted to natural language) are documented.", 264 "source": "haiku" 265 } 266 }, 267 "data_integrity": { 268 "raw_data_available": { 269 "applies": true, 270 "answer": false, 271 "justification": "Raw human evaluation rankings from 100 participants and simulation memory stream logs are not released for independent verification.", 272 "source": "haiku" 273 }, 274 "data_collection_described": { 275 "applies": true, 276 "answer": true, 277 "justification": "Human evaluation procedure is described in detail: Prolific platform, 100 US participants, within-subjects design, ~30-minute sessions watching agent replays, ranking believability of 5 conditions.", 278 "source": "haiku" 279 }, 280 "recruitment_methods_described": { 281 "applies": true, 282 "answer": true, 283 "justification": "Participants recruited from Prolific, required to be US resident, English-fluent, 18+, paid at $15/hour, and provided IRB-approved consent.", 284 "source": "haiku" 285 }, 286 "data_pipeline_documented": { 287 "applies": true, 288 "answer": true, 289 "justification": "The pipeline from simulation → agent interview responses → evaluator ranking → TrueSkill conversion → Kruskal-Wallis testing is fully described, including qualitative coding procedures (two-phase open coding).", 290 "source": "haiku" 291 } 292 }, 293 "contamination": { 294 "training_cutoff_stated": { 295 "applies": false, 296 "answer": false, 297 "justification": "Not applicable; the paper evaluates agent architecture believability via human raters, not model performance on a held-out benchmark where training contamination is the primary concern.", 298 "source": "haiku" 299 }, 300 "train_test_overlap_discussed": { 301 "applies": false, 302 "answer": false, 303 "justification": "Not applicable; evaluation task is human judgment of agent behavior, not benchmark completion.", 304 "source": "haiku" 305 }, 306 "benchmark_contamination_addressed": { 307 "applies": false, 308 "answer": false, 309 "justification": "Not applicable; no standard benchmark is used where pre-training contamination would be a concern.", 310 "source": "haiku" 311 } 312 }, 313 "human_studies": { 314 "pre_registered": { 315 "applies": true, 316 "answer": false, 317 "justification": "No mention of pre-registration on OSF, AsPredicted, or any registry.", 318 "source": "haiku" 319 }, 320 "irb_or_ethics_approval": { 321 "applies": true, 322 "answer": true, 323 "justification": "Participants 'provided consent by agreeing to a consent form approved by our institution's IRB.'", 324 "source": "haiku" 325 }, 326 "demographics_reported": { 327 "applies": true, 328 "answer": true, 329 "justification": "Age (median category 25-34), gender distribution (25 female, 73 male, 2 non-binary), education level breakdown, and race/ethnicity (73% Caucasian, etc.) are all reported.", 330 "source": "haiku" 331 }, 332 "inclusion_exclusion_criteria": { 333 "applies": true, 334 "answer": true, 335 "justification": "Inclusion criteria stated: US resident, English-fluent, 18+; quality control excluded 4 crowdworker-authored responses not meeting coherence/voice criteria.", 336 "source": "haiku" 337 }, 338 "randomization_described": { 339 "applies": true, 340 "answer": true, 341 "justification": "Evaluators were assigned a randomly chosen agent's life to watch, and one randomly chosen question from each of the five categories was displayed.", 342 "source": "haiku" 343 }, 344 "blinding_described": { 345 "applies": true, 346 "answer": false, 347 "justification": "No description of blinding is provided; the paper does not state whether condition labels were masked from evaluators or whether presentation order was counterbalanced.", 348 "source": "haiku" 349 }, 350 "attrition_reported": { 351 "applies": true, 352 "answer": false, 353 "justification": "No mention of participant attrition or dropout from the 100 evaluator sample.", 354 "source": "haiku" 355 } 356 }, 357 "cost_and_practicality": { 358 "inference_cost_reported": { 359 "applies": true, 360 "answer": true, 361 "justification": "Section 8.2 states the 25-agent 2-day simulation required 'thousands of dollars in token credits and taking multiple days to complete.'", 362 "source": "haiku" 363 }, 364 "compute_budget_stated": { 365 "applies": true, 366 "answer": true, 367 "justification": "Both monetary cost ('thousands of dollars in token credits') and wall-clock time ('multiple days') are reported for the simulation.", 368 "source": "haiku" 369 } 370 } 371 } 372 }, 373 "claims": [ 374 { 375 "claim": "The full generative agent architecture (memory + reflection + planning) produces the most believable behavior, with a large effect size over the no-memory baseline (Cohen's d=8.16).", 376 "evidence": "TrueSkill ratings: full μ=29.89 vs. no-memory μ=21.21; Kruskal-Wallis H(4)=150.29, p<0.001; all pairwise differences significant at p<0.001 except crowdworker vs. fully ablated.", 377 "supported": "strong" 378 }, 379 { 380 "claim": "Each architectural component (observation, reflection, planning) independently contributes to believability, with monotonically degrading performance as components are removed.", 381 "evidence": "Full (μ=29.89) > no-reflection (μ=26.88) > no-reflection/planning (μ=25.64) > no-memory/reflection/planning (μ=21.21); all pairwise differences significant at p<0.001.", 382 "supported": "strong" 383 }, 384 { 385 "claim": "Generative agents spontaneously diffuse information through the community without user intervention.", 386 "evidence": "Sam's candidacy spread from 1 to 8 agents (32%), Isabella's party from 1 to 13 (52%) over 2 game days; all knowledge claims verified against memory streams with 0 hallucinations.", 387 "supported": "moderate" 388 }, 389 { 390 "claim": "Agents form new social relationships over time, with network density increasing from 0.167 to 0.74.", 391 "evidence": "Undirected graph analysis of mutual agent knowledge before and after 2-day simulation; 1.3% (n=6/453) of relationship claims were hallucinated.", 392 "supported": "moderate" 393 }, 394 { 395 "claim": "Agents coordinate group activities across multiple autonomous steps from a single seed instruction.", 396 "evidence": "5 of 12 invited agents attended Isabella's Valentine's Day party; the full chain (intent → invitation → acceptance → planning → attendance) ran without user scripting.", 397 "supported": "moderate" 398 }, 399 { 400 "claim": "The simulation architecture is resource-intensive, requiring thousands of dollars in API credits and multiple days for 25 agents over 2 game days.", 401 "evidence": "Explicitly stated in Section 8.2: 'costing thousands of dollars in token credits and taking multiple days to complete.'", 402 "supported": "strong" 403 } 404 ], 405 "methodology_tags": [ 406 "case-study", 407 "qualitative", 408 "benchmark-eval" 409 ], 410 "key_findings": "A generative agent architecture combining a memory stream, reflection, and planning significantly outperforms LLM-only baselines and all ablations in producing believable behavior (Cohen's d=8.16), with each component contributing independently and monotonically. In a 25-agent sandbox simulation, agents exhibit emergent social behaviors—information diffusion, relationship formation, and event coordination—without direct scripting. The architecture inherits LLM failure modes including hallucination, over-formal dialogue, and instruction-tuning biases toward excessive agreeableness, and requires prohibitive compute (thousands of dollars, multiple days for 2 game days) at even small scale.", 411 "red_flags": [ 412 { 413 "flag": "OpenAI conflict of interest", 414 "detail": "OpenAI provided funding support while the paper evaluates OpenAI's ChatGPT (gpt-3.5-turbo) as the core model; no competing interests statement is included." 415 }, 416 { 417 "flag": "Single simulation run, no variance", 418 "detail": "All end-to-end emergent behavior claims (information diffusion %, network density, party attendance) come from a single 2-day simulation run with 25 agents; no replication or variance across runs is reported." 419 }, 420 { 421 "flag": "No blinding in human evaluation", 422 "detail": "The paper does not describe whether condition labels were masked from evaluators or whether presentation order was counterbalanced, raising order-effect concerns in a within-subjects design." 423 }, 424 { 425 "flag": "Ablation confound acknowledged but not resolved", 426 "detail": "Giving ablated architectures the same memories as the full architecture is acknowledged to yield conservative estimates; in practice ablated agents would have accumulated different memories, making the comparison difficult to interpret cleanly." 427 }, 428 { 429 "flag": "LLM generation hyperparameters unreported", 430 "detail": "Temperature, top-p, and other sampling parameters for gpt-3.5-turbo are not reported, preventing reproduction and obscuring the role of output stochasticity." 431 }, 432 { 433 "flag": "No power analysis", 434 "detail": "100 human evaluators chosen without power analysis or justification for sample size adequacy in a within-subjects 5-condition ranking study." 435 }, 436 { 437 "flag": "No statistical tests for emergent behavior", 438 "detail": "End-to-end results are reported as descriptive percentages with no statistical tests, confidence intervals, or variance estimates." 439 } 440 ], 441 "cited_papers": [ 442 { 443 "title": "Social Simulacra: Creating Populated Prototypes for Social Computing Systems", 444 "relevance": "Direct precursor work using LLMs to generate stateless user personas for social system prototyping; this paper explicitly extends it to persistent, memory-equipped agents." 445 }, 446 { 447 "title": "Using cognitive psychology to understand GPT-3", 448 "relevance": "Establishes LLM baselines for human behavioral simulation; cited as representing the prior state of the art that the no-memory ablation condition emulates." 449 }, 450 { 451 "title": "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?", 452 "relevance": "Closely related work using LLMs to replicate social science studies; cited as prior art for LLM-based human behavior simulation without persistent memory." 453 }, 454 { 455 "title": "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models", 456 "relevance": "Foundational prompting technique underlying the reflection and planning components of the architecture." 457 }, 458 { 459 "title": "Inner Monologue: Embodied Reasoning through Planning with Language Models", 460 "relevance": "Related work on LLM-based action planning for robotics, cited as prior art for LLM-driven hierarchical planning." 461 }, 462 { 463 "title": "On the Opportunities and Risks of Foundation Models", 464 "relevance": "Provides foundational context for why LLMs encode human behavior from training data, motivating the core architectural assumption." 465 }, 466 { 467 "title": "Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study", 468 "relevance": "Related work on LLM-generated synthetic human behavioral data for HCI research, directly adjacent to this paper's evaluation methodology." 469 }, 470 { 471 "title": "Training language models to follow instructions with human feedback (InstructGPT)", 472 "relevance": "Explains instruction tuning, which the paper identifies as a source of erratic agent behavior (over-formality, excessive cooperativeness) in the deployed system." 473 } 474 ], 475 "engagement_factors": { 476 "practical_relevance": { 477 "score": 3, 478 "justification": "Directly spawned AI-town and numerous downstream implementations; the architecture is immediately applicable to games, social prototyping tools, and training simulations with released code." 479 }, 480 "surprise_contrarian": { 481 "score": 2, 482 "justification": "Emergent social coordination from a single seed instruction (Valentine's party) challenges assumptions about how much scaffolding LLM agents need; the crowdworker baseline performing no better than the fully ablated LLM was a notable negative result." 483 }, 484 "fear_safety": { 485 "score": 1, 486 "justification": "Discusses parasocial relationship risks, deepfake concerns, and tailored persuasion, but these are framed as future concerns with proposed mitigations rather than demonstrated harms." 487 }, 488 "drama_conflict": { 489 "score": 1, 490 "justification": "No significant controversy; paper is well-received and results are largely positive; minor ethical concerns acknowledged without generating substantial community debate." 491 }, 492 "demo_ability": { 493 "score": 3, 494 "justification": "A live demo is explicitly linked in the paper and GitHub code is publicly released, enabling direct experimentation and replication." 495 }, 496 "brand_recognition": { 497 "score": 3, 498 "justification": "Stanford (Percy Liang, Michael Bernstein), Google Research, and Google DeepMind affiliations; became one of the most-cited LLM agent papers of 2023." 499 } 500 }, 501 "hn_data": { 502 "threads": [ 503 { 504 "hn_id": "37128293", 505 "title": "Show HN: AI-town, run your own custom AI world SIM with JavaScript", 506 "points": 429, 507 "comments": 115, 508 "url": "https://news.ycombinator.com/item?id=37128293", 509 "created_at": "2023-08-14T23:46:02Z" 510 }, 511 { 512 "hn_id": "35517649", 513 "title": "Generative Agents: Interactive Simulacra of Human Behavior", 514 "points": 391, 515 "comments": 252, 516 "url": "https://news.ycombinator.com/item?id=35517649", 517 "created_at": "2023-04-10T21:32:13Z" 518 }, 519 { 520 "hn_id": "36230750", 521 "title": "I'm afraid I can't do that: Prompt refusal in generative language models", 522 "points": 179, 523 "comments": 166, 524 "url": "https://news.ycombinator.com/item?id=36230750", 525 "created_at": "2023-06-07T18:03:25Z" 526 }, 527 { 528 "hn_id": "34702988", 529 "title": "Discovery of an Exceptionally Rare Nearby and Energetic Gamma-Ray Burst", 530 "points": 90, 531 "comments": 32, 532 "url": "https://news.ycombinator.com/item?id=34702988", 533 "created_at": "2023-02-08T01:44:54Z" 534 }, 535 { 536 "hn_id": "40212925", 537 "title": "Show HN: LLM-powered NPCs running on your hardware", 538 "points": 24, 539 "comments": 4, 540 "url": "https://news.ycombinator.com/item?id=40212925", 541 "created_at": "2024-04-30T16:34:46Z" 542 }, 543 { 544 "hn_id": "35511843", 545 "title": "Generative Agents: Interactive Simulacra of Human Behavior", 546 "points": 13, 547 "comments": 2, 548 "url": "https://news.ycombinator.com/item?id=35511843", 549 "created_at": "2023-04-10T13:11:19Z" 550 }, 551 { 552 "hn_id": "36232330", 553 "title": "Show HN: GalenAI – An AI Powered Search Engine for Clinicians", 554 "points": 2, 555 "comments": 2, 556 "url": "https://news.ycombinator.com/item?id=36232330", 557 "created_at": "2023-06-07T19:37:30Z" 558 }, 559 { 560 "hn_id": "40481871", 561 "title": "Exploring Autonomous Agents Through the Lens of Large Language Models", 562 "points": 2, 563 "comments": 0, 564 "url": "https://news.ycombinator.com/item?id=40481871", 565 "created_at": "2024-05-26T13:08:05Z" 566 }, 567 { 568 "hn_id": "39214022", 569 "title": "Exploring Encrypted Keyboards to Defeat Client-Side Scanning in E2EE Systems", 570 "points": 2, 571 "comments": 0, 572 "url": "https://news.ycombinator.com/item?id=39214022", 573 "created_at": "2024-02-01T09:04:19Z" 574 }, 575 { 576 "hn_id": "35512936", 577 "title": "CrossCode: Multi-Level Visualization of Program Execution", 578 "points": 1, 579 "comments": 0, 580 "url": "https://news.ycombinator.com/item?id=35512936", 581 "created_at": "2023-04-10T14:44:26Z" 582 } 583 ], 584 "top_points": 429, 585 "total_points": 1133, 586 "total_comments": 573 587 } 588 }