scan-v5.json (29539B)
1 { 2 "scan_version": 5, 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": "The abstract claims GenAI heightens burnout via job demands (β=0.398, p<.001) and that job resources and positive AI perceptions mitigate burnout (β=-0.360 and β=-0.246, both p<.001). All main claims are matched by PLS-SEM results.", 22 "source": "haiku" 23 }, 24 "causal_claims_justified": { 25 "applies": true, 26 "answer": false, 27 "justification": "The abstract uses causal language ('heightens burnout,' 'increasing job demands') but the study is cross-sectional; the limitations section explicitly acknowledges that only associations, not causal relationships, are established. The design cannot support causal inference.", 28 "source": "haiku" 29 }, 30 "generalization_bounded": { 31 "applies": true, 32 "answer": false, 33 "justification": "The sample is 90% male and recruited exclusively from OSS communities, yet conclusions are stated broadly without adequately bounding them to this specific, non-representative population.", 34 "source": "haiku" 35 }, 36 "alternative_explanations_discussed": { 37 "applies": true, 38 "answer": true, 39 "justification": "The limitations section acknowledges reverse causality (already-burned-out developers may adopt AI differently) and unmeasured confounders (trust in AI, perceived productivity) as alternative explanations, though only briefly.", 40 "source": "haiku" 41 }, 42 "proxy_outcome_distinction": { 43 "applies": true, 44 "answer": true, 45 "justification": "The paper clearly distinguishes the latent burnout construct from its four reflective survey indicators, validates the measurement model via AVE and composite reliability, and explicitly describes the proxy relationship throughout Section 5.1.", 46 "source": "haiku" 47 } 48 }, 49 "limitations_and_scope": { 50 "limitations_section_present": { 51 "applies": true, 52 "answer": true, 53 "justification": "Section 7 'Limitations' is a dedicated section discussing methodological constraints including JD-R framework scope, cross-sectional design, and sample representativeness.", 54 "source": "haiku" 55 }, 56 "threats_to_validity_specific": { 57 "applies": true, 58 "answer": true, 59 "justification": "Specific threats include cross-sectional design precluding causal inference, reverse causality from pre-existing burnout affecting AI adoption behavior, unmeasured confounders (trust, perceived productivity), and OSS-only recruitment. These go beyond generic boilerplate.", 60 "source": "haiku" 61 }, 62 "scope_boundaries_stated": { 63 "applies": true, 64 "answer": true, 65 "justification": "The authors state results 'should be interpreted as a theoretical starting point' and scope within the JD-R framework, acknowledging aspects of AI adoption it may not capture and noting their OSS sample cannot represent the 'entire global software workforce.'", 66 "source": "haiku" 67 } 68 }, 69 "conflicts_of_interest": { 70 "funding_disclosed": { 71 "applies": true, 72 "answer": true, 73 "justification": "The Acknowledgments section states: 'This work was supported by NSF Grant No. 2303043.'", 74 "source": "haiku" 75 }, 76 "affiliations_disclosed": { 77 "applies": true, 78 "answer": true, 79 "justification": "All three authors list Oregon State University affiliations on the title page.", 80 "source": "haiku" 81 }, 82 "funder_independent_of_outcome": { 83 "applies": true, 84 "answer": true, 85 "justification": "NSF is a government funding agency with no financial stake in whether GenAI adoption causes developer burnout.", 86 "source": "haiku" 87 }, 88 "financial_interests_declared": { 89 "applies": true, 90 "answer": false, 91 "justification": "No competing interests or financial interests statement appears anywhere in the paper.", 92 "source": "haiku" 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'), the JD-R model is explained in Section 2-3, and job demands/resources and their components are formally defined. GenAI is referenced contextually but not formally defined.", 100 "source": "haiku" 101 }, 102 "intended_contribution_clear": { 103 "applies": true, 104 "answer": true, 105 "justification": "Contributions are explicitly enumerated as threefold: establishing GenAI-burnout linkage via JD-R, offering the first explanatory model of demand amplifier vs. resource enabler, and translating findings into 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": "Section 2 reviews GenAI productivity literature and JD-R prior applications; the theory section (Section 3) explicitly builds on and extends existing JD-R studies to the GenAI context with specific citations for each hypothesis.", 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 analysis code is released. The supplementary appendix on zenodo (doi: 10.5281/zenodo.17232161) contains survey instruments and statistical tables but no reproducible analysis scripts.", 123 "source": "haiku" 124 }, 125 "data_released": { 126 "applies": true, 127 "answer": false, 128 "justification": "Raw survey response data is not publicly released; only aggregated tables and the survey instrument appear in the supplementary document.", 129 "source": "haiku" 130 }, 131 "environment_specified": { 132 "applies": true, 133 "answer": false, 134 "justification": "SmartPLS 4 and JASP (v0.17.2) are named, but no full dependency specification or computational environment description is provided.", 135 "source": "haiku" 136 }, 137 "reproduction_instructions": { 138 "applies": true, 139 "answer": false, 140 "justification": "No step-by-step reproduction instructions are provided; the supplementary appendix contains results tables and the survey instrument but not a replication protocol.", 141 "source": "haiku" 142 } 143 }, 144 "statistical_methodology": { 145 "confidence_intervals_or_error_bars": { 146 "applies": true, 147 "answer": true, 148 "justification": "Table 3 reports 95% confidence intervals for all three main path coefficients alongside standard deviations and p-values.", 149 "source": "haiku" 150 }, 151 "significance_tests": { 152 "applies": true, 153 "answer": true, 154 "justification": "Nonparametric bootstrapping with 5,000 subsamples provides p-values for PLS-SEM path coefficients; Benjamini-Hochberg correction is applied to multiple regression comparisons in Table 4.", 155 "source": "haiku" 156 }, 157 "effect_sizes_reported": { 158 "applies": true, 159 "answer": true, 160 "justification": "Standardized path coefficients (β) are reported throughout and R²=0.398 for burnout is explicitly benchmarked against organizational psychology norms (0.30–0.40 = moderate).", 161 "source": "haiku" 162 }, 163 "sample_size_justified": { 164 "applies": true, 165 "answer": true, 166 "justification": "G*Power analysis (f²=0.15, α=0.05, power=0.95) yields minimum n=119; actual n=442 is explicitly reported as exceeding this threshold.", 167 "source": "haiku" 168 }, 169 "variance_reported": { 170 "applies": true, 171 "answer": true, 172 "justification": "Standard deviations are reported in Table 3 for all path coefficients; the measurement model tables also report AVE and reliability metrics with appropriate ranges.", 173 "source": "haiku" 174 } 175 }, 176 "evaluation_design": { 177 "baselines_included": { 178 "applies": false, 179 "answer": false, 180 "justification": "This is a survey-based structural equation modeling study, not a system evaluation; comparative baselines are not applicable.", 181 "source": "haiku" 182 }, 183 "baselines_contemporary": { 184 "applies": false, 185 "answer": false, 186 "justification": "Not applicable — no comparative system baselines in a survey study.", 187 "source": "haiku" 188 }, 189 "ablation_study": { 190 "applies": false, 191 "answer": false, 192 "justification": "Not applicable in the context of hypothesis-testing structural equation modeling.", 193 "source": "haiku" 194 }, 195 "multiple_metrics": { 196 "applies": true, 197 "answer": true, 198 "justification": "Multiple analytical approaches are used: PLS-SEM for structural relationships, OLS regression for developer characteristic effects, and qualitative thematic coding of open-ended responses with IRR validation (90% and 92% Jaccard).", 199 "source": "haiku" 200 }, 201 "human_evaluation": { 202 "applies": false, 203 "answer": false, 204 "justification": "Not applicable — the paper surveys human participants directly about their own experiences, not evaluating AI system outputs.", 205 "source": "haiku" 206 }, 207 "held_out_test_set": { 208 "applies": false, 209 "answer": false, 210 "justification": "Not a prediction task; Stone-Geisser Q²=0.373 via blindfolding assesses predictive relevance but the full sample is used for model fitting.", 211 "source": "haiku" 212 }, 213 "per_category_breakdown": { 214 "applies": true, 215 "answer": true, 216 "justification": "Section 6 provides regression breakdowns by developer role (coder vs. non-coder), organization size, and industry seniority across five dependent constructs (burnout, organizational pressure, workload, autonomy, learning resources).", 217 "source": "haiku" 218 }, 219 "failure_cases_discussed": { 220 "applies": true, 221 "answer": true, 222 "justification": "Section 8 and qualitative findings extensively discuss failure modes: AI mandates increasing workload, insufficient training, reduced autonomy from top-down mandates, and burnout from misaligned productivity expectations.", 223 "source": "haiku" 224 }, 225 "negative_results_reported": { 226 "applies": true, 227 "answer": true, 228 "justification": "H4a (developer characteristics → burnout) and H4c (characteristics → workload) are explicitly reported as not supported, and H4b is only partially supported; non-significant results are clearly reported in Table 4.", 229 "source": "haiku" 230 } 231 }, 232 "setup_transparency": { 233 "model_versions_specified": { 234 "applies": false, 235 "answer": false, 236 "justification": "Not applicable — this paper studies general GenAI adoption phenomena via survey, not specific AI model capabilities; model version specification is irrelevant to the research design.", 237 "source": "haiku" 238 }, 239 "prompts_provided": { 240 "applies": false, 241 "answer": false, 242 "justification": "Not applicable — no AI model prompting in the research methodology; the paper uses traditional survey and statistical methods.", 243 "source": "haiku" 244 }, 245 "hyperparameters_reported": { 246 "applies": true, 247 "answer": true, 248 "justification": "Statistical parameters are documented: bootstrap subsamples (n=5,000), G*Power parameters (f²=0.15, α=0.05, 1-β=0.95), EFA decision criteria (loading≥0.50, cross-loading<0.30, uniqueness<0.60), and VIF thresholds.", 249 "source": "haiku" 250 }, 251 "scaffolding_described": { 252 "applies": false, 253 "answer": false, 254 "justification": "Not applicable — this is a survey study with no agentic scaffolding.", 255 "source": "haiku" 256 }, 257 "data_preprocessing_documented": { 258 "applies": true, 259 "answer": false, 260 "justification": "The paper reports 688 responses received and 246 excluded as 'invalid' but never specifies the criteria for invalidity, making data cleaning unreproducible.", 261 "source": "haiku" 262 } 263 }, 264 "data_integrity": { 265 "raw_data_available": { 266 "applies": true, 267 "answer": false, 268 "justification": "Raw survey responses are not released; the zenodo supplementary contains survey instruments and aggregated results tables only.", 269 "source": "haiku" 270 }, 271 "data_collection_described": { 272 "applies": true, 273 "answer": true, 274 "justification": "Section 4 describes survey design in detail: sandbox with 5 participants, pilot with 2 collaborators, recruitment from 56 OSS communities via email, IRB consent form, two-week collection window, and GDPR compliance.", 275 "source": "haiku" 276 }, 277 "recruitment_methods_described": { 278 "applies": true, 279 "answer": true, 280 "justification": "Recruitment from 56 named OSS communities is described with examples (Microsoft, Google, Kubernetes, Hugging Face), email invitation process, voluntary participation, and rationale for broad coverage strategy.", 281 "source": "haiku" 282 }, 283 "data_pipeline_documented": { 284 "applies": true, 285 "answer": true, 286 "justification": "Figure 1 provides an overview of the analysis pipeline (EFA → measurement model assessment → PLS-SEM structural model → qualitative composite assessment), with each step detailed in Section 5.", 287 "source": "haiku" 288 } 289 }, 290 "contamination": { 291 "training_cutoff_stated": { 292 "applies": false, 293 "answer": false, 294 "justification": "Not applicable — this paper surveys human developers about burnout experiences, not evaluating AI model capabilities on benchmarks.", 295 "source": "haiku" 296 }, 297 "train_test_overlap_discussed": { 298 "applies": false, 299 "answer": false, 300 "justification": "Not applicable — no ML benchmark evaluation.", 301 "source": "haiku" 302 }, 303 "benchmark_contamination_addressed": { 304 "applies": false, 305 "answer": false, 306 "justification": "Not applicable — no benchmark evaluation in this study.", 307 "source": "haiku" 308 } 309 }, 310 "human_studies": { 311 "pre_registered": { 312 "applies": true, 313 "answer": false, 314 "justification": "No pre-registration is mentioned anywhere in the paper despite a hypothesis-driven design with specific directional predictions.", 315 "source": "haiku" 316 }, 317 "irb_or_ethics_approval": { 318 "applies": true, 319 "answer": true, 320 "justification": "The paper states: 'The survey began with the university's Institutional Review Board (IRB) approved consent form,' and explicitly mentions GDPR compliance for data protection.", 321 "source": "haiku" 322 }, 323 "demographics_reported": { 324 "applies": true, 325 "answer": true, 326 "justification": "Table 1 reports complete participant distribution by gender (man/gender minorities), primary role (12 categories), organization size (4 levels), and industry experience (4 levels) for all 442 participants.", 327 "source": "haiku" 328 }, 329 "inclusion_exclusion_criteria": { 330 "applies": true, 331 "answer": false, 332 "justification": "Inclusion criteria (software professionals from OSS communities) are stated, but the criteria for excluding 246 of 688 responses (35.8%) as 'invalid' are never specified.", 333 "source": "haiku" 334 }, 335 "randomization_described": { 336 "applies": false, 337 "answer": false, 338 "justification": "Not applicable — this is an observational survey study with no randomized experimental conditions.", 339 "source": "haiku" 340 }, 341 "blinding_described": { 342 "applies": false, 343 "answer": false, 344 "justification": "Not applicable — single-condition survey study with no treatment/control manipulation requiring blinding.", 345 "source": "haiku" 346 }, 347 "attrition_reported": { 348 "applies": true, 349 "answer": false, 350 "justification": "The paper reports 688 received and 442 valid (246 excluded) but does not explain the criteria for excluding responses, making attrition assessment incomplete.", 351 "source": "haiku" 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 with no AI model inference costs.", 359 "source": "haiku" 360 }, 361 "compute_budget_stated": { 362 "applies": false, 363 "answer": false, 364 "justification": "Not applicable — survey and SEM analysis; no GPU or compute budget is relevant.", 365 "source": "haiku" 366 } 367 } 368 } 369 }, 370 "claims": [ 371 { 372 "claim": "GenAI adoption heightens developer burnout by increasing job demands (organizational pressure and workload)", 373 "evidence": "PLS-SEM path coefficient: Job Demands → Burnout β=0.398 (p<.001, 95% CI: 0.313–0.483), N=442", 374 "supported": "moderate" 375 }, 376 { 377 "claim": "Job resources (autonomy and learning resources) mitigate the negative effects of GenAI adoption on burnout", 378 "evidence": "PLS-SEM path coefficient: Job Resources → Burnout β=-0.360 (p<.001, 95% CI: -0.445 to -0.278)", 379 "supported": "moderate" 380 }, 381 { 382 "claim": "Favorable perceptions of AI are associated with reduced developer burnout", 383 "evidence": "PLS-SEM path coefficient: AI Perception → Burnout β=-0.246 (p<.001, 95% CI: -0.334 to -0.148)", 384 "supported": "moderate" 385 }, 386 { 387 "claim": "Developer burnout from GenAI adoption is experienced broadly regardless of role, organization size, or seniority", 388 "evidence": "OLS regression: no statistically significant associations between any developer characteristic and burnout (H4a not supported, Table 4)", 389 "supported": "moderate" 390 }, 391 { 392 "claim": "Developers in larger organizations face greater organizational pressure but have more access to learning resources", 393 "evidence": "OLS regression: org size → organizational pressure β=0.41 (p<.001); org size → learning resources β=0.28 (p<.001); org size → autonomy β=-0.13 (p<.001)", 394 "supported": "strong" 395 }, 396 { 397 "claim": "Senior developers retain more autonomy in AI adoption decisions than junior developers", 398 "evidence": "OLS regression: industry seniority → autonomy β=0.18 (p<.001), consistent with qualitative responses about senior developers shaping adoption practices", 399 "supported": "strong" 400 }, 401 { 402 "claim": "The JD-R model accounts for approximately 40% of variance in burnout related to GenAI adoption", 403 "evidence": "R²=0.398 for burnout construct, Q²=0.373 via blindfolding indicating large predictive relevance; SRMR meets goodness-of-fit thresholds", 404 "supported": "strong" 405 } 406 ], 407 "methodology_tags": [ 408 "observational", 409 "qualitative" 410 ], 411 "key_findings": "A mixed-methods survey of 442 software developers finds that GenAI adoption is associated with elevated burnout through two demand mechanisms: heightened organizational pressure from top-down adoption mandates and intensified workload from verification and debugging of AI-generated outputs. The JD-R model explains ~40% of burnout variance, with job resources (autonomy, learning opportunities) and favorable AI perceptions providing significant mitigation. Burnout appears broadly distributed regardless of developer role or seniority, suggesting a systemic rather than individual-level phenomenon. Qualitative evidence reveals three structural workflow shifts: from productivity euphoria to mandate-driven stress, from social apprenticeship to private AI-assisted workflows, and from individual gains to team-level collaboration friction from AI-generated code review burden.", 412 "red_flags": [ 413 { 414 "flag": "Causal language for cross-sectional design", 415 "detail": "The abstract states GenAI adoption 'heightens burnout by increasing job demands,' but the study is cross-sectional; the limitations section acknowledges that only associations, not causal relationships, are established. The design cannot rule out reverse causality." 416 }, 417 { 418 "flag": "90% male OSS sample overgeneralized", 419 "detail": "The sample is 90% male and recruited exclusively from OSS communities, yet conclusions are stated broadly. The paper justifies this by noting sample size comparability to prior SE work but does not adequately bound findings to this specific non-representative population." 420 }, 421 { 422 "flag": "Unexplained exclusion of 35.8% of responses", 423 "detail": "246 of 688 responses were excluded as 'invalid' with no specification of what rendered responses invalid, raising data selection concerns and preventing reproducibility." 424 }, 425 { 426 "flag": "Single-item construct measures", 427 "detail": "Learning Resources and AI Perception are each measured with a single survey item, limiting reliability assessment; single-item measures are known to have lower validity than multi-item scales for complex constructs." 428 }, 429 { 430 "flag": "No pre-registration", 431 "detail": "Despite a hypothesis-driven design with specific directional predictions, the study was not pre-registered, leaving open the possibility of post-hoc hypothesis refinement." 432 }, 433 { 434 "flag": "Raw data not released", 435 "detail": "Raw survey responses are not publicly available, preventing independent verification of the statistical models, measurement model decisions, or alternative analytical approaches." 436 } 437 ], 438 "cited_papers": [ 439 { 440 "title": "Job demands–resources theory: taking stock and looking forward", 441 "relevance": "Foundational JD-R model theory used as the primary analytical lens; defines job demands and resources constructs" 442 }, 443 { 444 "title": "Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study", 445 "relevance": "Key prior application of JD-R to burnout; survey items and construct modeling approach adapted from this work" 446 }, 447 { 448 "title": "Predicting attrition among software professionals: Antecedents and consequences of burnout and engagement", 449 "relevance": "SE-specific JD-R burnout application; burnout survey items adapted directly from this work" 450 }, 451 { 452 "title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity", 453 "relevance": "Contrasting evidence showing AI slowed experienced developers by 19% — cited as key motivation for studying demand-side effects" 454 }, 455 { 456 "title": "Accelerate State of DevOps 2024: A Decade with DORA", 457 "relevance": "Large-scale survey finding only 2.1% productivity rise and 7.2% delivery performance decline with AI adoption — major evidence against uniform productivity narrative" 458 }, 459 { 460 "title": "PLS-SEM for software engineering research: An introduction and survey", 461 "relevance": "Methodological foundation validating PLS-SEM use in SE research contexts" 462 }, 463 { 464 "title": "Navigating the complexity of generative AI adoption in software engineering", 465 "relevance": "Prior empirical work on GenAI adoption factors, trust, and intentions in SE" 466 }, 467 { 468 "title": "Burnout in software engineering: A systematic mapping study", 469 "relevance": "Prior systematic review establishing burnout as an ongoing SE concern pre-GenAI, providing historical context" 470 }, 471 { 472 "title": "The impact of AI on developer productivity: Evidence from GitHub Copilot", 473 "relevance": "Key industry study showing productivity speedups from Copilot — the dominant narrative this paper challenges" 474 }, 475 { 476 "title": "What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI", 477 "relevance": "Prior work on GenAI adoption intentions from overlapping author group; situates trust and adoption as precursors to demand effects" 478 } 479 ], 480 "engagement_factors": { 481 "practical_relevance": { 482 "score": 3, 483 "justification": "Directly addresses developer burnout from mandatory GenAI adoption — immediately actionable for engineering managers, HR, and team leads designing AI rollouts." 484 }, 485 "surprise_contrarian": { 486 "score": 2, 487 "justification": "Quantifies burnout costs that challenge the dominant 'GenAI = productivity gains' narrative, though industry surveys are increasingly recognizing this problem." 488 }, 489 "fear_safety": { 490 "score": 2, 491 "justification": "Raises concerns about generational deskilling, job insecurity, and hidden workforce harms from AI automation mandates hitting early-career developers hardest." 492 }, 493 "drama_conflict": { 494 "score": 2, 495 "justification": "Frames industry AI mandates as harming the very developers they are supposed to benefit, with vivid qualitative quotes ('I move fast with AI and move mountains of work, but I am losing my passion') amplifying the conflict." 496 }, 497 "demo_ability": { 498 "score": 0, 499 "justification": "Survey research with statistical modeling — no interactive demo or artifact for practitioners to try." 500 }, 501 "brand_recognition": { 502 "score": 1, 503 "justification": "Oregon State University (Sarma lab) is a recognized SE research group but not a marquee industry lab; no famous industry co-authors or products." 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 }