scan-v4.json (29825B)
1 { 2 "scan_version": 4, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "From Gains to Strains: Modeling Developer Burnout with GenAI Adoption", 6 "authors": [ 7 "Zixuan Feng", 8 "Sadia Afroz", 9 "Anita Sarma" 10 ], 11 "year": 2025, 12 "venue": "ICSE-SEIS '26", 13 "arxiv_id": "2510.07435", 14 "doi": "10.1145/3786581.3786934" 15 }, 16 "checklist": { 17 "claims_and_evidence": { 18 "abstract_claims_supported": { 19 "applies": true, 20 "answer": true, 21 "justification": "Abstract claims ('GenAI adoption heightens burnout by increasing job demands, while job resources and positive perceptions mitigate these effects') are directly supported by the PLS-SEM results in Table 3 with significant path coefficients.", 22 "source": "opus" 23 }, 24 "causal_claims_justified": { 25 "applies": true, 26 "answer": true, 27 "justification": "The paper explicitly states in Section 7 that hypotheses 'propose associations between different constructs rather than causal relationships, as the present study is a cross-sectional sample study.' The language throughout uses 'associated with' rather than causal language.", 28 "source": "opus" 29 }, 30 "generalization_bounded": { 31 "applies": true, 32 "answer": true, 33 "justification": "Section 7 acknowledges 'no single sample can capture the entire global software workforce' and describes the sample boundaries (442 practitioners from 56 organizations). The 90% male skew is noted. Cross-sectional limitation is stated.", 34 "source": "opus" 35 }, 36 "alternative_explanations_discussed": { 37 "applies": true, 38 "answer": true, 39 "justification": "Section 7 discusses specific confounds: 'participants who were already experiencing higher burnout (e.g., due to upcoming deadlines) might adopt AI differently,' and 'trust in AI systems or perceived productivity, may have influenced results but were not directly measured.'", 40 "source": "opus" 41 }, 42 "proxy_outcome_distinction": { 43 "applies": true, 44 "answer": true, 45 "justification": "The paper is explicit that burnout is measured through 4 Likert-scale indicators capturing different facets (workload manageability, exhaustion, cynicism, reduced efficacy) as reflective indicators of the latent burnout construct. The measurement model is thoroughly validated. The paper frames its measurement at appropriate granularity.", 46 "source": "opus" 47 } 48 }, 49 "limitations_and_scope": { 50 "limitations_section_present": { 51 "applies": true, 52 "answer": true, 53 "justification": "Section 7 is a dedicated 'Limitations' section with substantive discussion of cross-sectional design, method choice (PLS-SEM vs CB-SEM), sample representativeness, and confounding factors.", 54 "source": "opus" 55 }, 56 "threats_to_validity_specific": { 57 "applies": true, 58 "answer": true, 59 "justification": "Section 7 discusses specific threats: pre-existing burnout could bias responses, trust in AI was not directly measured as a confound, the choice of PLS-SEM over CB-SEM is justified for their model structure, and the cross-sectional design prevents causal inference.", 60 "source": "opus" 61 }, 62 "scope_boundaries_stated": { 63 "applies": true, 64 "answer": true, 65 "justification": "The paper states results should be 'interpreted as a theoretical starting point,' acknowledges cross-sectional design prevents causal claims, notes PLS-SEM was chosen because the model includes formative and reflective constructs, and bounds generalization to the sample characteristics.", 66 "source": "opus" 67 } 68 }, 69 "conflicts_of_interest": { 70 "funding_disclosed": { 71 "applies": true, 72 "answer": true, 73 "justification": "Acknowledgments section states: 'This work was supported by NSF Grant No. 2303043.'", 74 "source": "opus" 75 }, 76 "affiliations_disclosed": { 77 "applies": true, 78 "answer": true, 79 "justification": "All three authors are from Oregon State University. The study does not evaluate any product from their institution, so no product-related conflict exists.", 80 "source": "opus" 81 }, 82 "funder_independent_of_outcome": { 83 "applies": true, 84 "answer": true, 85 "justification": "NSF is an independent government funding agency with no financial stake in whether GenAI causes burnout or not.", 86 "source": "opus" 87 }, 88 "financial_interests_declared": { 89 "applies": true, 90 "answer": false, 91 "justification": "No competing interests or financial disclosure statement is present in the paper.", 92 "source": "opus" 93 } 94 }, 95 "scope_and_framing": { 96 "key_terms_defined": { 97 "applies": true, 98 "answer": true, 99 "justification": "Burnout is defined ('chronic state of exhaustion, emotional distance, cynicism, and diminished professional efficacy'), JD-R model components are defined, and survey constructs are operationalized with specific Likert items.", 100 "source": "haiku" 101 }, 102 "intended_contribution_clear": { 103 "applies": true, 104 "answer": true, 105 "justification": "The introduction explicitly enumerates three contributions: an empirical link between GenAI adoption and burnout, an explanatory JD-R extension, and actionable implications for workload design and workforce development.", 106 "source": "haiku" 107 }, 108 "engagement_with_prior_work": { 109 "applies": true, 110 "answer": true, 111 "justification": "The paper substantively engages with prior JD-R research, prior burnout studies in SE, and contrasting productivity findings (GitHub 55.8% speedup vs. Becker 19% slowdown), explaining how this work fills the well-being gap.", 112 "source": "haiku" 113 } 114 } 115 }, 116 "type_checklist": { 117 "empirical": { 118 "artifacts": { 119 "code_released": { 120 "applies": true, 121 "answer": false, 122 "justification": "No source code or analysis scripts are released. The paper references a supplementary document on Zenodo (ref [3]) but this contains the questionnaire and additional tables, not analysis code.", 123 "source": "opus" 124 }, 125 "data_released": { 126 "applies": true, 127 "answer": false, 128 "justification": "No survey response data is released. Only aggregated results are presented. The Zenodo supplement contains the questionnaire and supplementary tables, not raw data.", 129 "source": "opus" 130 }, 131 "environment_specified": { 132 "applies": true, 133 "answer": false, 134 "justification": "No environment specifications provided. The paper mentions using SmartPLS 4 and JASP but does not provide versions or environment details for reproduction.", 135 "source": "opus" 136 }, 137 "reproduction_instructions": { 138 "applies": true, 139 "answer": false, 140 "justification": "No step-by-step reproduction instructions are provided. The methodology is described in detail but there are no scripts or commands to replicate the analysis.", 141 "source": "opus" 142 } 143 }, 144 "statistical_methodology": { 145 "confidence_intervals_or_error_bars": { 146 "applies": true, 147 "answer": true, 148 "justification": "95% confidence intervals are reported for all path coefficients in Table 3 (e.g., H1: 95% CI (0.313, 0.483)).", 149 "source": "opus" 150 }, 151 "significance_tests": { 152 "applies": true, 153 "answer": true, 154 "justification": "Bootstrapping with 5,000 subsamples for PLS-SEM p-values (Table 3), and Benjamini-Hochberg corrected p-values for the OLS regressions (Table 4).", 155 "source": "opus" 156 }, 157 "effect_sizes_reported": { 158 "applies": true, 159 "answer": true, 160 "justification": "Standardized path coefficients (β) are reported in Table 3 and regression coefficients in Table 4. R²=0.398 for burnout is reported with context that 0.30-0.40 represents moderate explanatory power. Power analysis assumed medium effect size f²=0.15.", 161 "source": "opus" 162 }, 163 "sample_size_justified": { 164 "applies": true, 165 "answer": true, 166 "justification": "Power analysis using G*Power is reported in Section 5.1 (Step 2): minimum required N=119 for 3 predictors, medium effect size f²=0.15, α=0.05, power=0.95. Actual N=442 far exceeds this.", 167 "source": "opus" 168 }, 169 "variance_reported": { 170 "applies": true, 171 "answer": true, 172 "justification": "Standard deviations are reported for path coefficients in Table 3 (e.g., SD=0.044 for H1). The bootstrapping approach with 5,000 subsamples provides variance estimates.", 173 "source": "opus" 174 } 175 }, 176 "evaluation_design": { 177 "baselines_included": { 178 "applies": true, 179 "answer": true, 180 "justification": "The study compares against the theoretical JD-R model framework and uses AI-perception as a control variable. The model is assessed against established thresholds for reliability, validity, and fit (SRMR, R², Q²).", 181 "source": "opus" 182 }, 183 "baselines_contemporary": { 184 "applies": false, 185 "answer": false, 186 "justification": "Not applicable — this is a survey-based empirical study testing a theoretical model, not a system evaluation with competing baselines.", 187 "source": "opus" 188 }, 189 "ablation_study": { 190 "applies": false, 191 "answer": false, 192 "justification": "Not applicable — the system is a theoretical model (JD-R), not a multi-component technical system. The formative HOC/LOC structure serves a similar analytical purpose.", 193 "source": "opus" 194 }, 195 "multiple_metrics": { 196 "applies": true, 197 "answer": true, 198 "justification": "Multiple evaluation metrics used: R², Q² (predictive relevance), SRMR (model fit), AVE, Cronbach's α, composite reliability (ρa, ρc), HTMT, VIF, outer loadings, and outer weights.", 199 "source": "opus" 200 }, 201 "human_evaluation": { 202 "applies": false, 203 "answer": false, 204 "justification": "Not applicable — this is a survey study about human experience, not a system producing outputs that need human evaluation.", 205 "source": "opus" 206 }, 207 "held_out_test_set": { 208 "applies": false, 209 "answer": false, 210 "justification": "Not applicable — this is a survey-based study, not a predictive modeling task requiring train/test splits.", 211 "source": "opus" 212 }, 213 "per_category_breakdown": { 214 "applies": true, 215 "answer": true, 216 "justification": "Results broken down by construct (Organizational Pressure, Workload, Autonomy, Learning Resources, Burnout) and by developer characteristics (role, org size, seniority) in Table 4. Learning resource types broken down in Table 5.", 217 "source": "opus" 218 }, 219 "failure_cases_discussed": { 220 "applies": true, 221 "answer": true, 222 "justification": "Hypotheses H4a and H4c are explicitly reported as unsupported (no significant associations found). The paper discusses where the model's predictions were not confirmed.", 223 "source": "opus" 224 }, 225 "negative_results_reported": { 226 "applies": true, 227 "answer": true, 228 "justification": "H4a (developer characteristics → burnout) and H4c (characteristics → workload) found no significant associations, reported transparently in Section 6.2.", 229 "source": "opus" 230 } 231 }, 232 "setup_transparency": { 233 "model_versions_specified": { 234 "applies": false, 235 "answer": false, 236 "justification": "Not applicable — this paper does not use any AI/LLM models in its methodology. It studies GenAI adoption's effects on humans.", 237 "source": "opus" 238 }, 239 "prompts_provided": { 240 "applies": false, 241 "answer": false, 242 "justification": "Not applicable — the paper does not use prompting. It is a survey study.", 243 "source": "opus" 244 }, 245 "hyperparameters_reported": { 246 "applies": true, 247 "answer": true, 248 "justification": "PLS-SEM parameters reported: 5,000 bootstrap subsamples, significance level α=0.05, medium effect size f²=0.15. SmartPLS 4 used with blindfolding for Q². JASP used for EFA.", 249 "source": "opus" 250 }, 251 "scaffolding_described": { 252 "applies": false, 253 "answer": false, 254 "justification": "Not applicable — no agentic scaffolding is used in this study.", 255 "source": "opus" 256 }, 257 "data_preprocessing_documented": { 258 "applies": true, 259 "answer": true, 260 "justification": "The paper describes receiving 688 responses, excluding invalid responses to reach N=442. EFA was used to verify factor structure. Composite scores were computed using outer weights from the measurement model. Coding procedures for qualitative data described with IRR (Jaccard index 90%, 92%).", 261 "source": "opus" 262 } 263 }, 264 "data_integrity": { 265 "raw_data_available": { 266 "applies": true, 267 "answer": false, 268 "justification": "Raw survey responses are not made available. Only aggregated statistics and model outputs are presented.", 269 "source": "opus" 270 }, 271 "data_collection_described": { 272 "applies": true, 273 "answer": true, 274 "justification": "Section 4 describes the survey instrument development (one month, three researchers), IRB approval, 5-point Likert scales, survey duration (5-8 minutes), two-week availability window, and the complete questionnaire is in the supplementary document.", 275 "source": "opus" 276 }, 277 "recruitment_methods_described": { 278 "applies": true, 279 "answer": true, 280 "justification": "Section 4 describes recruitment from 56 OSS communities spanning diverse domains (Microsoft, Google, Netflix, Red Hat, IBM, Kubernetes, Hugging Face, Python, TensorFlow). Email invitations were sent with consent forms, GDPR compliance, and IRB approval.", 281 "source": "opus" 282 }, 283 "data_pipeline_documented": { 284 "applies": true, 285 "answer": true, 286 "justification": "The pipeline is documented: 688 responses → exclusion of invalid responses → 442 final participants. EFA validated factor structure. PLS-SEM measurement model assessed. Qualitative coding: two authors independently coded 20% for IRR, then split remaining. However, criteria for 'invalid responses' are not detailed.", 287 "source": "opus" 288 } 289 }, 290 "contamination": { 291 "training_cutoff_stated": { 292 "applies": false, 293 "answer": false, 294 "justification": "Not applicable — this paper does not evaluate a pre-trained model's capability on any benchmark. It is a survey study of developer burnout.", 295 "source": "opus" 296 }, 297 "train_test_overlap_discussed": { 298 "applies": false, 299 "answer": false, 300 "justification": "Not applicable — no pre-trained model evaluation involved.", 301 "source": "opus" 302 }, 303 "benchmark_contamination_addressed": { 304 "applies": false, 305 "answer": false, 306 "justification": "Not applicable — no benchmark evaluation of a pre-trained model.", 307 "source": "opus" 308 } 309 }, 310 "human_studies": { 311 "pre_registered": { 312 "applies": true, 313 "answer": false, 314 "justification": "No mention of pre-registration (OSF, AsPredicted, or any registry) found in the paper.", 315 "source": "opus" 316 }, 317 "irb_or_ethics_approval": { 318 "applies": true, 319 "answer": true, 320 "justification": "Section 4 states: 'The survey began with the university's Institutional Review Board (IRB) approved consent form.'", 321 "source": "opus" 322 }, 323 "demographics_reported": { 324 "applies": true, 325 "answer": true, 326 "justification": "Table 1 reports detailed demographics: gender (90% men, 10% gender minorities), role (11 categories), organization size (4 levels), and experience (4 levels). N=442.", 327 "source": "opus" 328 }, 329 "inclusion_exclusion_criteria": { 330 "applies": true, 331 "answer": false, 332 "justification": "The paper describes broad recruitment targeting 56 OSS communities but does not state specific inclusion/exclusion criteria for who qualified as a valid participant. The reduction from 688 to 442 responses mentions 'excluding invalid responses' without stating the criteria.", 333 "source": "opus" 334 }, 335 "randomization_described": { 336 "applies": false, 337 "answer": false, 338 "justification": "Not applicable — this is a cross-sectional survey study, not an experimental study with treatment/control conditions.", 339 "source": "opus" 340 }, 341 "blinding_described": { 342 "applies": false, 343 "answer": false, 344 "justification": "Not applicable — this is a cross-sectional survey, not an experimental study where blinding would be relevant.", 345 "source": "opus" 346 }, 347 "attrition_reported": { 348 "applies": true, 349 "answer": true, 350 "justification": "688 responses received, 442 retained after excluding invalid responses (attrition of 35.8%). For qualitative analysis, N=221 responded to the open-ended question, and N=361 responded to the resources question.", 351 "source": "opus" 352 } 353 }, 354 "cost_and_practicality": { 355 "inference_cost_reported": { 356 "applies": false, 357 "answer": false, 358 "justification": "Not applicable — this is a survey study, not a computational method with inference costs.", 359 "source": "opus" 360 }, 361 "compute_budget_stated": { 362 "applies": false, 363 "answer": false, 364 "justification": "Not applicable — this is a survey study with no significant computational requirements.", 365 "source": "opus" 366 } 367 } 368 } 369 }, 370 "claims": [ 371 { 372 "claim": "GenAI adoption is positively associated with developer burnout through elevated job demands (organizational pressure and workload).", 373 "evidence": "PLS-SEM path coefficient Job-Demands → Burnout: β=0.398, 95% CI (0.313, 0.483), p<.001, N=442.", 374 "supported": "moderate" 375 }, 376 { 377 "claim": "Job resources (autonomy and learning resources) are negatively associated with burnout, buffering the demand-side effects.", 378 "evidence": "PLS-SEM path coefficient Job-Resources → Burnout: β=−0.360, 95% CI (−0.445, −0.278), p<.001.", 379 "supported": "moderate" 380 }, 381 { 382 "claim": "Favorable AI perceptions are independently associated with lower burnout.", 383 "evidence": "AI-Perception → Burnout path coefficient: β=−0.246, 95% CI (−0.334, −0.148), p<.001; model R²=0.398.", 384 "supported": "moderate" 385 }, 386 { 387 "claim": "Burnout is a broadly experienced phenomenon regardless of developer characteristics (role, organization size, seniority).", 388 "evidence": "OLS regression for Burnout: none of the three developer characteristics (Coder, Size, Years) reached statistical significance after BH correction (Table 4).", 389 "supported": "moderate" 390 }, 391 { 392 "claim": "Developers in coding-intensive roles and larger organizations experience greater organizational pressure from GenAI adoption.", 393 "evidence": "Regression: Coder → Organizational Pressure β=0.51 (p<.05); Size → Organizational Pressure β=0.41 (p<.001).", 394 "supported": "strong" 395 }, 396 { 397 "claim": "Senior developers have more autonomy and access to learning resources than junior developers.", 398 "evidence": "Years → Autonomy β=0.18 (p<.001); Years → Learning Resources β=0.14 (p<.05). Larger organizations provide more learning resources (Size → Learning Resources β=0.28, p<.001).", 399 "supported": "strong" 400 }, 401 { 402 "claim": "22.4% of developers received no meaningful organizational support for learning AI tools.", 403 "evidence": "Qualitative coding of 361 open-ended responses found 22.4% indicated organizations provided no meaningful support; most common support was internal training (30.2%).", 404 "supported": "moderate" 405 } 406 ], 407 "methodology_tags": [ 408 "observational", 409 "qualitative" 410 ], 411 "key_findings": "A survey of 442 developers analyzed via PLS-SEM finds that GenAI adoption is associated with increased burnout through elevated organizational pressure and workload (β=0.398), while autonomy and learning resources buffer this effect (β=−0.360). Favorable AI perceptions also independently reduce burnout (β=−0.246). Burnout is broadly distributed across developer demographics, but organizational pressure falls disproportionately on coding roles and large organizations, while senior developers have significantly more autonomy and resource access than junior developers. Qualitative findings reveal that AI hype creates unsustainable output expectations, verification overhead erases efficiency gains, and junior developers are particularly vulnerable due to reduced mentoring and inadequate organizational support.", 412 "red_flags": [ 413 { 414 "flag": "Causal language on cross-sectional data", 415 "detail": "The abstract claims GenAI adoption 'heightens burnout by increasing job demands' — causal framing — but the study is cross-sectional with no temporal ordering, acknowledged in limitations as establishing associations only." 416 }, 417 { 418 "flag": "Exclusion criteria undisclosed", 419 "detail": "246 of 688 responses (35.7%) were excluded as 'invalid' without specifying what criteria defined invalidity, raising concern about selection bias in the final sample." 420 }, 421 { 422 "flag": "OSS community sampling bias", 423 "detail": "Recruiting from 56 OSS communities via email likely overrepresents developers engaged with open-source ecosystems; results may not generalize to enterprise developers without OSS involvement." 424 }, 425 { 426 "flag": "Single-item construct for Learning Resources", 427 "detail": "Learning Resources is measured by a single Likert item ('My organization gives me the time, training, and resources I need to master AI'), which cannot be assessed for reliability or AVE, reducing construct validity." 428 }, 429 { 430 "flag": "90% male sample", 431 "detail": "The sample is 90.05% men, severely limiting generalizability to gender minorities and constraining subgroup analyses; the 44 gender-minority respondents are too few for reliable subgroup analysis." 432 }, 433 { 434 "flag": "No raw data or code released", 435 "detail": "Survey responses and analysis scripts are not publicly available; the Zenodo supplement contains appendix tables only, making independent replication impossible." 436 } 437 ], 438 "cited_papers": [ 439 { 440 "title": "The job demands-resources model of burnout", 441 "relevance": "Foundational JD-R model used as the theoretical lens for the entire study (Demerouti et al. 2001)" 442 }, 443 { 444 "title": "Job demands–resources theory: taking stock and looking forward", 445 "relevance": "Core JD-R theory update used to operationalize job demands and resources constructs (Bakker & Demerouti 2017)" 446 }, 447 { 448 "title": "Predicting attrition among software professionals: Antecedents and consequences of burnout and engagement", 449 "relevance": "Prior burnout measurement in software engineering; burnout survey items adapted from this work (Trinkenreich et al. 2024)" 450 }, 451 { 452 "title": "The impact of AI on developer productivity: Evidence from GitHub Copilot", 453 "relevance": "Key productivity study showing 55% speedup; contrasted with this study's burnout findings (Peng et al. 2023)" 454 }, 455 { 456 "title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity", 457 "relevance": "Contradictory productivity finding (19% slowdown) used to motivate the demand-side analysis (Becker et al. 2025)" 458 }, 459 { 460 "title": "Navigating the complexity of generative AI adoption in software engineering", 461 "relevance": "Prior SE study on GenAI adoption patterns and developer perceptions (Russo 2024)" 462 }, 463 { 464 "title": "Burnout in software engineering: A systematic mapping study", 465 "relevance": "Systematic review establishing prior burnout disruptions in SE (agile, COVID-19) that frames this paper's contribution (Tulili et al. 2023)" 466 }, 467 { 468 "title": "Accelerate State of DevOps 2024: A Decade with DORA", 469 "relevance": "Large-scale industry survey showing 7.2% delivery performance decline with AI adoption, supporting demand-side harms (DeBellis et al. 2024)" 470 }, 471 { 472 "title": "The SPACE of AI: Real-World Lessons on AI's Impact on Developers", 473 "relevance": "SPACE framework application showing AI boosts productivity for routine tasks; contrasted with burnout costs (Houck et al. 2025)" 474 }, 475 { 476 "title": "JD-R model on job insecurity and the moderating effect of COVID-19 perceived susceptibility", 477 "relevance": "Prior JD-R application to COVID-19 workload; workload survey items adapted from this work (Cao et al. 2024)" 478 } 479 ], 480 "engagement_factors": { 481 "practical_relevance": { 482 "score": 3, 483 "justification": "Directly actionable for engineering managers with specific recommendations on KPI redesign, learning scaffolding, and team workflow practices for AI rollout." 484 }, 485 "surprise_contrarian": { 486 "score": 2, 487 "justification": "Challenges the dominant productivity-gains narrative by demonstrating systematic burnout costs of AI adoption that aren't visible in speedup metrics." 488 }, 489 "fear_safety": { 490 "score": 1, 491 "justification": "Raises concerns about developer well-being and generational skill erosion but does not address existential or safety-critical AI risks." 492 }, 493 "drama_conflict": { 494 "score": 2, 495 "justification": "Directly positions itself against AI hype culture ('forced to use AI so a metric can be reported') with vivid qualitative quotes illustrating organizational dysfunction." 496 }, 497 "demo_ability": { 498 "score": 0, 499 "justification": "Survey study with no tool, demo, or interactive artifact that practitioners can try." 500 }, 501 "brand_recognition": { 502 "score": 0, 503 "justification": "Oregon State University researchers with no affiliation to a major AI lab or widely-known product." 504 } 505 }, 506 "hn_data": { 507 "threads": [ 508 { 509 "hn_id": "29005793", 510 "title": "Bugs in our pockets: the risks of client-side scanning", 511 "points": 204, 512 "comments": 131, 513 "url": "https://news.ycombinator.com/item?id=29005793", 514 "created_at": "2021-10-26T20:24:05Z" 515 }, 516 { 517 "hn_id": "28956546", 518 "title": "Bugs in Our Pockets: The Risks of Client-Side Scanning", 519 "points": 5, 520 "comments": 0, 521 "url": "https://news.ycombinator.com/item?id=28956546", 522 "created_at": "2021-10-22T12:44:28Z" 523 }, 524 { 525 "hn_id": "28875180", 526 "title": "Bugs in Our Pockets: The Risks of Client-Side Scanning", 527 "points": 4, 528 "comments": 1, 529 "url": "https://news.ycombinator.com/item?id=28875180", 530 "created_at": "2021-10-15T07:10:35Z" 531 }, 532 { 533 "hn_id": "28921166", 534 "title": "Bugs in Our Pockets: The Risks of Client-Side Scanning (2021)", 535 "points": 2, 536 "comments": 0, 537 "url": "https://news.ycombinator.com/item?id=28921166", 538 "created_at": "2021-10-19T17:52:50Z" 539 }, 540 { 541 "hn_id": "28887043", 542 "title": "Bugs in Our Pockets: The Risks of Client-Side Scanning", 543 "points": 2, 544 "comments": 0, 545 "url": "https://news.ycombinator.com/item?id=28887043", 546 "created_at": "2021-10-16T11:10:22Z" 547 }, 548 { 549 "hn_id": "45609137", 550 "title": "Modeling Developer Burnout with GenAI Adoption", 551 "points": 1, 552 "comments": 0, 553 "url": "https://news.ycombinator.com/item?id=45609137", 554 "created_at": "2025-10-16T18:43:48Z" 555 }, 556 { 557 "hn_id": "43884165", 558 "title": "Can Transformers Reason Logically? A Study in SAT Solving", 559 "points": 1, 560 "comments": 0, 561 "url": "https://news.ycombinator.com/item?id=43884165", 562 "created_at": "2025-05-04T03:09:31Z" 563 }, 564 { 565 "hn_id": "33334207", 566 "title": "Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning", 567 "points": 1, 568 "comments": 0, 569 "url": "https://news.ycombinator.com/item?id=33334207", 570 "created_at": "2022-10-25T18:14:15Z" 571 }, 572 { 573 "hn_id": "28905207", 574 "title": "Bugs in Our Pockets: The Risks of Client-Side Scanning", 575 "points": 1, 576 "comments": 0, 577 "url": "https://news.ycombinator.com/item?id=28905207", 578 "created_at": "2021-10-18T13:26:29Z" 579 } 580 ], 581 "top_points": 204, 582 "total_points": 221, 583 "total_comments": 132 584 } 585 }