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9.75 92.91 9.59C93.18 9.42 93.49 9.31 93.8 9.26C94.11 9.2 94.44 9.21 94.75 9.27L94.75 11.85C94.69 11.84 94.63 11.83 94.57 11.82C94.51 11.81 94.44 11.8 94.38 11.79C94.32 11.78 94.26 11.78 94.2 11.78C94.13 11.77 94.07 11.77 94.01 11.77C93.8 11.75 93.58 11.77 93.38 11.83C93.17 11.89 92.98 11.98 92.81 12.11C92.64 12.24 92.49 12.39 92.37 12.57C92.26 12.75 92.18 12.95 92.13 13.16C92.12 13.21 92.12 13.26 92.12 13.31C92.12 13.37 92.11 13.42 92.11 13.47C92.11 13.52 92.12 13.57 92.12 13.63C92.12 13.68 92.12 13.73 92.13 13.78L92.13 18.25L89.3 18.25L89.3 12.33Z'></path> 52</svg> 53 54</a><a class="link_to_mobile_menu" aria-label="Open GitClear navigation dropdown" href="#"><i class="far fa-bars fa_icon"></i></a> 55<div class='public_header_actions'> 56<div class='action_links'> 57<div class='features_opener'> 58<a class="features_popper padded_action research_learning_link " href="/how_to_measure_developer_productivity_and_other_measurement_research">Research & Learning</a> 59<div 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180rebuilt nightly · 12 min p95 181</div> 182</div> 183<div class='databricks_medallion_strip'> 184<div class='medallion_tier is_bronze_tier'> 185<div class='medallion_tier_label'>Bronze</div> 186<div class='medallion_tier_tables'> 187<div class='medallion_table'>bronze.ai_assistant_events</div> 188<div class='medallion_table'>bronze.git_commits</div> 189<div class='medallion_table'>bronze.pull_request_reviews</div> 190</div> 191<div class='medallion_tier_note'>as your vendors ship it</div> 192</div> 193<div class='medallion_arrow'> 194<div class='medallion_arrow_label'>conform</div> 195<div class='medallion_arrow_line'></div> 196</div> 197<div class='medallion_tier is_silver_tier'> 198<div class='medallion_tier_label'>Silver</div> 199<div class='medallion_tier_tables'> 200<div class='medallion_table'>silver.ai_work_episodes</div> 201<div class='medallion_table'>silver.change_lineage</div> 202</div> 203<div class='medallion_tier_note'>one row per AI work episode</div> 204</div> 205<div class='medallion_arrow'> 206<div class='medallion_arrow_label'>relate</div> 207<div class='medallion_arrow_line'></div> 208</div> 209<div class='medallion_tier is_gold_tier'> 210<div class='medallion_tier_label'>Gold</div> 211<div class='medallion_tier_tables'> 212<div class='medallion_table'>gold.model_task_roi</div> 213<div class='medallion_table'>gold.durability_curve</div> 214</div> 215<div class='medallion_tier_note'>question-shaped marts</div> 216</div> 217</div> 218<div class='databricks_panels'> 219<div class='databricks_panel is_matrix_panel'> 220<div class='databricks_panel_head'> 221<span class='databricks_panel_number'>01</span> 222<span class='databricks_panel_title'>TaskâModel ROI Matrix</span> 223</div> 224<div class='databricks_panel_question'>âFor this kind of task, which model has historically delivered the best value?â</div> 225<div class='databricks_metric_modes'> 226<div class='databricks_metric_mode is_active_mode'>best value</div> 227<div class='databricks_metric_mode'>lowest all-in cost</div> 228<div class='databricks_metric_mode'>fastest result</div> 229<div class='databricks_metric_mode'>lowest revision risk</div> 230<div class='databricks_metric_mode'>least steering</div> 231</div> 232<div class='databricks_matrix_clip'> 233<table class='databricks_matrix'> 234<thead> 235<tr> 236<th class='matrix_category_heading'>prompt category</th> 237<th class='matrix_model_heading is_apex_r_series'> 238<div class='matrix_model_name'>Apex-R 2.4</div> 239<div class='matrix_model_note'>premium reasoning</div> 240</th> 241<th class='matrix_model_heading is_apex_f_series'> 242<div class='matrix_model_name'>Apex-F 2.4</div> 243<div class='matrix_model_note'>premium fast</div> 244</th> 245<th class='matrix_model_heading is_core_series'> 246<div class='matrix_model_name'>Core 3.1</div> 247<div class='matrix_model_note'>balanced workhorse</div> 248</th> 249<th class='matrix_model_heading is_lite_series'> 250<div class='matrix_model_name'>Lite 1.9</div> 251<div class='matrix_model_note'>cheap, verbose</div> 252</th> 253<th class='matrix_model_heading is_local_series'> 254<div class='matrix_model_name'>Local-32B</div> 255<div class='matrix_model_note'>self-hosted</div> 256</th> 257</tr> 258</thead> 259<tbody> 260<tr> 261<td class='matrix_category_cell'> 262<div class='matrix_category_name'>implement_feature</div> 263<div class='matrix_category_outcome'>outcome: durable Diff Delta @ 90d</div> 264</td> 265<td class='matrix_value_cell is_heat_step_4 is_tied_standing'> 266<div class='matrix_multiplier'>1.85Ã</div> 267<div class='matrix_cost'>$79.2 / success</div> 268<div class='matrix_retention'>80% @30d · n=262</div> 269</td> 270<td class='matrix_value_cell is_heat_step_4 is_tied_standing'> 271<div class='matrix_multiplier'>1.70Ã</div> 272<div class='matrix_cost'>$79.8 / success</div> 273<div class='matrix_retention'>78% @30d · n=87</div> 274</td> 275<td class='matrix_value_cell is_heat_step_2'> 276<div class='matrix_multiplier'>0.97Ã</div> 277<div class='matrix_cost'>$98.9 / success</div> 278<div class='matrix_retention'>74% @30d · n=612</div> 279</td> 280<td class='matrix_value_cell is_heat_step_1'> 281<div class='matrix_multiplier'>0.37Ã</div> 282<div class='matrix_cost'>$212 / success</div> 283<div class='matrix_retention'>60% @30d · n=349</div> 284</td> 285<td class='matrix_value_cell is_heat_step_1'> 286<div class='matrix_multiplier'>0.11Ã</div> 287<div class='matrix_cost'>$436 / success</div> 288<div class='matrix_retention'>49% @30d · n=146</div> 289</td> 290</tr> 291<tr> 292<td class='matrix_category_cell'> 293<div class='matrix_category_name'>root_cause_bug</div> 294<div class='matrix_category_outcome'>outcome: verified diagnosis, no recurrence</div> 295</td> 296<td class='matrix_value_cell is_heat_step_5 is_best_standing'> 297<div class='matrix_multiplier'>2.34Ã</div> 298<div class='matrix_cost'>$81.9 / success</div> 299<div class='matrix_retention'>82% @30d · n=131</div> 300</td> 301<td class='matrix_value_cell is_heat_step_4'> 302<div class='matrix_multiplier'>1.62Ã</div> 303<div class='matrix_cost'>$109 / success</div> 304<div class='matrix_retention'>71% @30d · n=44</div> 305</td> 306<td class='matrix_value_cell is_heat_step_2'> 307<div class='matrix_multiplier'>0.81Ã</div> 308<div class='matrix_cost'>$154 / success</div> 309<div class='matrix_retention'>64% @30d · n=306</div> 310</td> 311<td class='matrix_value_cell is_heat_step_1'> 312<div class='matrix_multiplier'>0.19Ã</div> 313<div class='matrix_cost'>$558 / success</div> 314<div class='matrix_retention'>44% @30d · n=175</div> 315</td> 316<td class='matrix_value_cell is_heat_step_1'> 317<div class='matrix_multiplier'>0.04Ã</div> 318<div class='matrix_cost'>$1381 / success</div> 319<div class='matrix_retention'>34% @30d · n=73</div> 320</td> 321</tr> 322<tr> 323<td class='matrix_category_cell'> 324<div class='matrix_category_name'>dry_cleanup</div> 325<div class='matrix_category_outcome'>outcome: duplication removed, retained @ 90d</div> 326</td> 327<td class='matrix_value_cell is_heat_step_4 is_best_standing'> 328<div class='matrix_multiplier'>1.63Ã</div> 329<div class='matrix_cost'>$52.1 / success</div> 330<div class='matrix_retention'>90% @30d · n=66</div> 331</td> 332<td class='matrix_value_cell is_heat_step_0 is_insufficient_standing'> 333<div class='matrix_insufficient_note'>n=22 insufficient</div> 334</td> 335<td class='matrix_value_cell is_heat_step_3'> 336<div class='matrix_multiplier'>1.00Ã</div> 337<div class='matrix_cost'>$55.5 / success</div> 338<div class='matrix_retention'>90% @30d · n=153</div> 339</td> 340<td class='matrix_value_cell is_heat_step_2'> 341<div class='matrix_multiplier'>0.60Ã</div> 342<div class='matrix_cost'>$75.8 / success</div> 343<div class='matrix_retention'>86% @30d · n=87</div> 344</td> 345<td class='matrix_value_cell is_heat_step_1'> 346<div class='matrix_multiplier'>0.17Ã</div> 347<div class='matrix_cost'>$156 / success</div> 348<div class='matrix_retention'>70% @30d · n=36</div> 349</td> 350</tr> 351<tr> 352<td class='matrix_category_cell'> 353<div class='matrix_category_name'>explain_code</div> 354<div class='matrix_category_outcome'>outcome: explanation accepted, next task lands</div> 355</td>
356<td class='matrix_value_cell is_heat_step_4 is_tied_standing'> 357<div class='matrix_multiplier'>1.60Ã</div> 358<div class='matrix_cost'>$21.0 / success</div> 359<div class='matrix_retention'>97% @30d · n=112</div> 360</td> 361<td class='matrix_value_cell is_heat_step_4 is_best_standing'> 362<div class='matrix_multiplier'>1.74Ã</div> 363<div class='matrix_cost'>$18.0 / success</div> 364<div class='matrix_retention'>97% @30d · n=37</div> 365</td> 366<td class='matrix_value_cell is_heat_step_3'> 367<div class='matrix_multiplier'>1.00Ã</div> 368<div class='matrix_cost'>$21.5 / success</div> 369<div class='matrix_retention'>94% @30d · n=262</div> 370</td> 371<td class='matrix_value_cell is_heat_step_2'> 372<div class='matrix_multiplier'>0.51Ã</div> 373<div class='matrix_cost'>$34.8 / success</div> 374<div class='matrix_retention'>83% @30d · n=150</div> 375</td> 376<td class='matrix_value_cell is_heat_step_1'> 377<div class='matrix_multiplier'>0.15Ã</div> 378<div class='matrix_cost'>$70.0 / success</div> 379<div class='matrix_retention'>69% @30d · n=62</div> 380</td> 381</tr> 382<tr> 383<td class='matrix_category_cell'> 384<div class='matrix_category_name'>address_pr_feedback</div> 385<div class='matrix_category_outcome'>outcome: thread resolved without reopen</div> 386</td> 387<td class='matrix_value_cell is_heat_step_4 is_tied_standing'> 388<div class='matrix_multiplier'>1.71Ã</div> 389<div class='matrix_cost'>$50.3 / success</div> 390<div class='matrix_retention'>89% @30d · n=103</div> 391</td> 392<td class='matrix_value_cell is_heat_step_4 is_best_standing'> 393<div class='matrix_multiplier'>1.78Ã</div> 394<div class='matrix_cost'>$44.7 / success</div> 395<div class='matrix_retention'>92% @30d · n=34</div> 396</td> 397<td class='matrix_value_cell is_heat_step_2'> 398<div class='matrix_multiplier'>0.98Ã</div> 399<div class='matrix_cost'>$57.1 / success</div> 400<div class='matrix_retention'>87% @30d · n=240</div> 401</td> 402<td class='matrix_value_cell is_heat_step_2'> 403<div class='matrix_multiplier'>0.42Ã</div> 404<div class='matrix_cost'>$110 / success</div> 405<div class='matrix_retention'>73% @30d · n=137</div> 406</td> 407<td class='matrix_value_cell is_heat_step_1'> 408<div class='matrix_multiplier'>0.11Ã</div> 409<div class='matrix_cost'>$238 / success</div> 410<div class='matrix_retention'>59% @30d · n=57</div> 411</td> 412</tr> 413<tr> 414<td class='matrix_category_cell'> 415<div class='matrix_category_name'>build_failing_test</div> 416<div class='matrix_category_outcome'>outcome: fails before fix, passes after</div> 417</td> 418<td class='matrix_value_cell is_heat_step_4 is_best_standing'> 419<div class='matrix_multiplier'>1.66Ã</div> 420<div class='matrix_cost'>$32.1 / success</div> 421<div class='matrix_retention'>94% @30d · n=47</div> 422</td> 423<td class='matrix_value_cell is_heat_step_0 is_insufficient_standing'> 424<div class='matrix_insufficient_note'>n=16 insufficient</div> 425</td> 426<td class='matrix_value_cell is_heat_step_3'> 427<div class='matrix_multiplier'>1.04Ã</div> 428<div class='matrix_cost'>$33.6 / success</div> 429<div class='matrix_retention'>91% @30d · n=109</div> 430</td> 431<td class='matrix_value_cell is_heat_step_2'> 432<div class='matrix_multiplier'>0.48Ã</div> 433<div class='matrix_cost'>$59.9 / success</div> 434<div class='matrix_retention'>78% @30d · n=62</div> 435</td> 436<td class='matrix_value_cell is_heat_step_1'> 437<div class='matrix_multiplier'>0.14Ã</div> 438<div class='matrix_cost'>$117 / success</div> 439<div class='matrix_retention'>65% @30d · n=26</div> 440</td> 441</tr> 442</tbody> 443</table> 444</div> 445<div class='databricks_matrix_legend'> 446<span class='matrix_legend_item is_best_legend'>best in row</span> 447<span class='matrix_legend_item is_tied_legend'>tied with best (CI overlap)</span> 448<span class='matrix_legend_item is_insufficient_legend'>n < 25, no ranking shown</span> 449</div> 450</div> 451<div class='databricks_panel is_curve_panel'> 452<div class='databricks_panel_head'> 453<span class='databricks_panel_number'>02</span> 454<span class='databricks_panel_title'>Token-to-Durable-Production Yield Curve</span> 455</div> 456<div class='databricks_panel_question'>âWhere does each model's apparent productivity disappear?â</div> 457<svg class='databricks_curve' preserveAspectRatio='xMidYMid meet' role='img' viewBox='0 0 488 292' xmlns='http://www.w3.org/2000/svg'> 458<title>Share of each model's initially attributed change still present in production, by funnel stage</title> 459<rect class='curve_post_merge_band' height='214' width='152.16666666666666' x='247.83333333333334' y='34'></rect> 460<line class='curve_gridline' x1='46' x2='400' y1='248.0' y2='248.0'></line> 461<text class='curve_axis_label' text-anchor='end' x='40' y='251.0'>0%</text> 462<line class='curve_gridline' x1='46' x2='400' y1='193.5' y2='193.5'></line> 463<text class='curve_axis_label' text-anchor='end' x='40' y='196.5'>27%</text> 464<line class='curve_gridline' x1='46' x2='400' y1='141.0' y2='141.0'></line> 465<text class='curve_axis_label' text-anchor='end' x='40' y='144.0'>53%</text> 466<line class='curve_gridline' x1='46' x2='400' y1='86.5' y2='86.5'></line> 467<text class='curve_axis_label' text-anchor='end' x='40' y='89.5'>80%</text> 468<line class='curve_gridline' x1='46' x2='400' y1='34.0' y2='34.0'></line> 469<text class='curve_axis_label' text-anchor='end' x='40' y='37.0'>106%</text> 470<line class='curve_boundary_line' x1='247.83333333333334' x2='247.83333333333334' y1='34' y2='248'></line> 471<text class='curve_censor_note' x='255.83333333333334' y='46'>right-censored: 12d exposure, survival estimate</text> 472<text class='curve_axis_caption' x='46' y='24'>â % of initially attributed change still present</text> 473<polyline class='curve_series_line is_apex_r_series' points='46.0,46.1 103.7,70.3 161.3,74.4 219.0,82.5 276.7,86.5 334.3,90.5 392.0,98.6'></polyline> 474<circle class='curve_series_dot is_apex_r_series' cx='46.0' cy='46.1' r='2.6'></circle> 475<circle class='curve_series_dot is_apex_r_series' cx='103.7' cy='70.3' r='2.6'></circle> 476<circle class='curve_series_dot is_apex_r_series' cx='161.3' cy='74.4' r='2.6'></circle> 477<circle class='curve_series_dot is_apex_r_series' cx='219.0' cy='82.5' r='2.6'></circle> 478<circle class='curve_series_dot is_apex_r_series' cx='276.7' cy='86.5' r='2.6'></circle> 479<circle class='curve_series_dot is_apex_r_series' cx='334.3' cy='90.5' r='2.6'></circle> 480<circle class='curve_series_dot is_apex_r_series' cx='392.0' cy='98.6' r='2.6'></circle> 481<text class='curve_series_label is_apex_r_series' x='406' y='101.6'> 482Apex-R 2.4 483<tspan class='curve_series_finish' dx='5'>74%</tspan> 484</text> 485<polyline class='curve_series_line is_apex_f_series' points='46.0,46.1 103.7,72.4 161.3,76.4 219.0,84.5 276.7,90.5'></polyline> 486<polyline class='curve_series_line is_censored_segment is_apex_f_series' points='276.7,90.5 334.3,94.6 392.0,104.7'></polyline> 487<circle class='curve_series_dot is_apex_f_series' cx='46.0' cy='46.1' r='2.6'></circle> 488<circle class='curve_series_dot is_apex_f_series' cx='103.7' cy='72.4' r='2.6'></circle> 489<circle class='curve_series_dot is_apex_f_series' cx='161.3' cy='76.4' r='2.6'></circle> 490<circle class='curve_series_dot is_apex_f_series' cx='219.0' cy='84.5' r='2.6'></circle> 491<circle class='curve_series_dot is_apex_f_series' cx='276.7' cy='90.5' r='2.6'></circle> 492<circle class='curve_series_dot is_apex_f_series is_hollow_marker' cx='334.3' cy='94.6' r='2.6'></circle> 493<circle class='curve_series_dot is_apex_f_series' cx='392.0' cy='104.7' r='2.6'></circle> 494<text class='curve_series_label is_apex_f_series' x='406' y='115.6'> 495Apex-F 2.4 496<tspan class='curve_series_finish' dx='5'>71%</tspan> 497</text> 498<polyline class='curve_series_line is_core_series' points='46.0,46.1 103.7,76.4 161.3,82.5 219.0,88.5 276.7,116.8 334.3,126.9 392.0,134.9'></polyline> 499<circle class='curve_series_dot is_core_series' cx='46.0' cy='46.1' r='2.6'></circle> 500<circle class='curve_series_dot is_core_series' cx='103.7' cy='76.4' r='2.6'></circle> 501<circle class='curve_series_dot is_core_series' cx='161.3' cy='82.5' r='2.6'></circle> 502<circle class='curve_series_dot is_core_series' cx='219.0' cy='88.5' r='2.6'></circle> 503<circle class='curve_series_dot is_core_series' cx='276.7' cy='116.8' r='2.6'></circle> 504<circle class='curve_series_dot is_core_series' cx='334.3' cy='126.9' r='2.6'></circle> 505<circle class='curve_series_dot is_core_series' cx='392.0' cy='134.9' r='2.6'></circle> 506<text class='curve_series_label is_core_series' x='406' y='137.9'> 507Core 3.1 508<tspan class='curve_series_finish' dx='5'>56%</tspan> 509</text> 510<polyline class='curve_series_line is_lite_series' points='46.0,46.1 103.7,130.9 161.3,145.0 219.0,159.2 276.7,171.3 334.3,181.4 392.0,189.5'></polyline> 511<circle class='curve_series_dot is_lite_series' cx='46.0' cy='46.1' r='2.6'></circle> 512<circle class='curve_series_dot is_lite_series' cx='103.7' cy='130.9' r='2.6'></circle> 513<circle class='curve_series_dot is_lite_series' cx='161.3' cy='145.0' r='2.6'></circle> 514<circle class='curve_series_dot is_lite_series' cx='219.0' cy='159.2' r='2.6'></circle> 515<circle class='curve_series_dot is_lite_series' cx='276.7' cy='171.3' r='2.6'></circle> 516<circle class='curve_series_dot is_lite_series' cx='334.3' cy='181.4' r='2.6'></circle> 517<circle class='curve_series_dot is_lite_series' cx='392.0' cy='189.5' r='2.6'></circle> 518<text class='curve_series_label is_lite_series' x='406' y='192.5'> 519Lite 1.9 520<tspan class='curve_series_finish' dx='5'>29%</tspan> 521</text> 522<polyline class='curve_series_line is_local_series' points='46.0,46.1 103.7,134.9 161.3,149.1 219.0,161.2 276.7,179.4 334.3,191.5 392.0,199.5'></polyline> 523<circle class='curve_series_dot is_local_series' cx='46.0' cy='46.1' r='2.6'></circle> 524<circle class='curve_series_dot is_local_series' cx='103.7' cy='134.9' r='2.6'></circle> 525<circle class='curve_series_dot is_local_series' cx='161.3' cy='149.1' r='2.6'></circle> 526<circle class='curve_series_dot is_local_series' cx='219.0' cy='161.2' r='2.6'></circle> 527<circle class='curve_series_dot is_local_series' cx='276.7' cy='179.4' r='2.6'></circle> 528<circle class='curve_series_dot is_local_series' cx='334.3' cy='191.5' r='2.6'></circle> 529<circle class='curve_series_dot is_local_series' cx='392.0' cy='199.5' r='2.6'></circle> 530<text class='curve_series_label is_local_series' x='406' y='206.5'> 531Local-32B 532<tspan class='curve_series_finish' dx='5'>24%</tspan> 533</text> 534<text class='curve_stage_label' text-anchor='middle' x='46.0' y='263'>generated</text> 535<text class='curve_stage_label' text-anchor='middle' x='103.7' y='263'>accepted</text> 536<text class='curve_stage_label' text-anchor='middle' x='161.3' y='263'>committed</text> 537<text class='curve_stage_label' text-anchor='middle' x='219.0' y='263'>merged</text> 538<text class='curve_stage_label' text-anchor='middle' x='276.7' y='263'>day 7</text> 539<text class='curve_stage_label' text-anchor='middle' x='334.3' y='263'>day 30</text> 540<text class='curve_stage_label' text-anchor='middle' x='392.0' y='263'>day 90</text> 541<text class='curve_stage_caption' text-anchor='middle' x='146.91666666666669' y='280'>â pre-merge leakage â</text> 542<text class='curve_stage_caption' text-anchor='middle' x='319.9166666666667' y='280'>â post-merge decay â</text> 543</svg> 544<div class='databricks_curve_footnote'> 545Diff Delta is lineage-aware: moves, renames and reformatting keep their lineage, so code the developer rewrites 546stops counting toward the model. 547</div> 548</div> 549</div> 550<div class='databricks_card_disclaimer'> 551A clipped section of two gold tables from our design study, drawn in HTML/CSS so it scales. 552<b>Synthetic data</b> 553: model names are placeholders and no real measurement is implied. 554</div> 555</div> 556 557</div> 558</div> 559<div class='press_mentions_strip'> 560<div class='content_container'> 561<p class='press_strip_label'> 562<span>â</span> 563AI Quality Research Cited By 564<span>â</span> 565</p> 566<div class='press_strip_row'> 567<a class="image_0" href="/press_mentions"><img alt="Stack Overflow" class="mention_source" src="https://images.amplenote.com/badb3cb2-5d3a-11f0-b3e2-553e9273cc52/8a9f7e94-85ba-45f0-a896-8c011677fa1f.png" /></a> 568<a class="image_1" href="/press_mentions"><img alt="Visual Studio Magazine" class="mention_source" src="https://images.amplenote.com/badb3cb2-5d3a-11f0-b3e2-553e9273cc52/16ac6bc5-33c0-4eb9-818b-1813aac7893c.png" /></a> 569<a class="image_2" href="/press_mentions"><img alt="InfoWorld (Matt Asay)" class="mention_source" src="https://images.amplenote.com/badb3cb2-5d3a-11f0-b3e2-553e9273cc52/a1bbb7a9-483f-4b9a-a6c6-db933e9c977b.png" /></a> 570<a class="image_3" href="/press_mentions"><img alt="Geekwire" class="mention_source" src="https://images.amplenote.com/badb3cb2-5d3a-11f0-b3e2-553e9273cc52/08f07f0e-687e-4565-b335-3ebb36bc7629.jpg" /></a> 571<a class="image_4" href="/press_mentions">
571<img alt="Augment Code (Molisha Shah)" class="mention_source" src="https://images.amplenote.com/0cb33a88-4342-11f1-a22e-a398116079d4/2a6fc444-c778-4373-8800-8dcfc2d63001.png" /></a> 572<a class="image_5" href="/press_mentions"><img alt="MIT Technology Review (Edd Gent)" class="mention_source" src="https://images.amplenote.com/7ab862da-eb39-11f0-a9b5-39fae2be90d4/203ffb10-e865-4f63-92e5-7d633a0c00de.png" /></a> 573<a class="image_6" href="/press_mentions"><img alt="Axios (Megan Morrone)" class="mention_source" src="https://images.amplenote.com/badb3cb2-5d3a-11f0-b3e2-553e9273cc52/189e7b3b-133e-4bc8-a32d-7e48dd49f7c1.png" /></a> 574</div> 575</div> 576</div> 577 578<div class='home_databricks'> 579<div class='databricks_inner'> 580<div class='databricks_eyebrow'>Databricks data engineering</div> 581<h2 class='databricks_header'>Your bronze tables already hold the answer. We build the tables that relate it.</h2> 582<p class='databricks_copy'> 583GitClear specializes in enriching <b>Databricks bronze tables</b> â raw AI assistant telemetry, git history, pull 584request events, issue trackers â into the silver and gold tables that relate facts nobody could join before: which 585model earned its inference spend on which kind of task, and how much of its output was still in production 90 days 586later. The two panels at the top of this page are those tables. 587</p> 588<p class='databricks_copy'> 589We build these pipelines on Apache Airflow with <a target="_blank" rel="noopener noreferrer" href="https://www.astronomer.io/">Astronomer</a>, the same orchestration we run for 590billion-dollar enterprises. Bronze ingest â silver work episodes â gold marts, rebuilt nightly in Unity Catalog, 591against your own warehouse â your data never leaves your Databricks account. 592</p> 593<ul class='databricks_points'> 594<li>Medallion modeling (bronze â silver â gold) in Unity Catalog, with lineage that survives an audit</li> 595<li>Containerized, idempotent Airflow DAGs on Astronomer â backfill a year of history without babysitting it</li> 596<li><a href="https://www.gitclear.com/help/understand_diff_delta_from_first_principles_stats_on_metric_stability">Diff Delta</a> as the unit of output, so moves, renames and reformatting never inflate a model's numbers</li> 597<li>Episode-grain facts, so cost, durability and revision risk can all be asked about the same row</li> 598</ul> 599<div class='databricks_actions'> 600<a class="databricks_actions_primary" href="/demo_requests/new">Talk to us about your pipeline</a> 601<a class="databricks_actions_secondary" href="https://www.gitclear.com/developer_metrics_encyclopedia_illustrated_software_metrics_and_measurements">See the metrics we model</a> 602</div> 603</div> 604</div> 605 606<section class='product_section is_dark_section' id='product'> 607<div class='ai_roi_v2_content_container'> 608<div class='product_section_head is_centered_section_head'> 609<div class='section_eyebrow_label'>The product</div> 610<h2 class='section_headline_text'> 611Four surfaces. One 612<em>defensible</em> 613ROI score. 614</h2> 615<p class='section_intro_copy'> 616Every AI stat in GitClear originates from deep analysis of code changes â so when a number doesn't look right, 617you can always drill into the code that produced it. 618</p> 619</div> 620<div class='product_showcase_row is_revealable'> 621<div class='showcase_copy_column'> 622<div class='showcase_kicker'>01 · Line-level attribution</div> 623<h3 class='showcase_heading'> 624Every line tagged with the model that 625<em>wrote it.</em> 626</h3> 627<p class='showcase_paragraph'> 628GitClear cross-references your Git history with vendor AI usage APIs and agent telemetry hooks to produce 629<strong>commit-grade provenance</strong> 630â no guessing, no aggregate estimates. 631</p> 632<ul class='showcase_bullets'> 633<li> 634<strong>Claude, Copilot, Cursor, Codex, Augment and Gemini APIs</strong> 635supported out of the box 636</li> 637<li> 638Attribution precision maximized via telemetry hooks 639</li> 640<li> 641Access via a robust API, for your own analysis or internal reporting 642</li> 643</ul> 644</div> 645<div class='showcase_shot_column'> 646<div class='attribution_view'> 647<div class='attribution_head'> 648<div class='attribution_file_label'>src/api/payments/checkout.ts</div> 649<div class='attribution_meta'>authored_by_llm · 90d view</div> 650</div> 651<div class='attribution_line is_blue_highlight'> 652<div class='attribution_line_number'>42</div> 653<div class='attribution_tag is_copilot_tag'>COPILOT</div> 654<div class='attribution_code'> 655<span class='attribution_code_keyword'>const</span> 656result = 657<span class='attribution_code_keyword'>await</span> 658<span class='attribution_code_function'>validatePayment</span> 659(req.body); 660</div> 661</div> 662<div class='attribution_line is_blue_highlight'> 663<div class='attribution_line_number'>43</div> 664<div class='attribution_tag is_copilot_tag'>COPILOT</div> 665<div class='attribution_code'> 666<span class='attribution_code_keyword'>if</span> 667(!result.ok) 668<span class='attribution_code_keyword'>return</span> 669res.
670<span class='attribution_code_function'>status</span> 671( 672<span class='attribution_code_string'>400</span> 673). 674<span class='attribution_code_function'>json</span> 675(...); 676</div> 677</div> 678<div class='attribution_line is_human_highlight'> 679<div class='attribution_line_number'>44</div> 680<div class='attribution_tag is_human_tag'>HUMAN</div> 681<div class='attribution_code'> 682<span class='attribution_code_comment'>// edge case: retry on 503</span> 683</div> 684</div> 685<div class='attribution_line is_amber_highlight'> 686<div class='attribution_line_number'>45</div> 687<div class='attribution_tag is_claude_tag'>CLAUDE</div> 688<div class='attribution_code'> 689<span class='attribution_code_keyword'>try</span> 690{ 691<span class='attribution_code_keyword'>await</span> 692<span class='attribution_code_function'>chargeWithRetry</span> 693(result.token, 3); } 694</div> 695</div> 696<div class='attribution_line is_amber_highlight'> 697<div class='attribution_line_number'>46</div> 698<div class='attribution_tag is_claude_tag'>CLAUDE</div> 699<div class='attribution_code'> 700<span class='attribution_code_keyword'>catch</span> 701(err) { logger. 702<span class='attribution_code_function'>error</span> 703(err); 704<span class='attribution_code_keyword'>throw</span> 705err; } 706</div> 707</div> 708<div class='attribution_line is_violet_highlight'> 709<div class='attribution_line_number'>47</div> 710<div class='attribution_tag is_cursor_tag'>CURSOR</div> 711<div class='attribution_code'> 712<span class='attribution_code_keyword'>const</span> 713audit = 714<span class='attribution_code_keyword'>await</span> 715<span class='attribution_code_function'>logTransaction</span> 716(result, req.user); 717</div> 718</div> 719<div class='attribution_line is_human_highlight'> 720<div class='attribution_line_number'>48</div> 721<div class='attribution_tag is_human_tag'>HUMAN</div> 722<div class='attribution_code'> 723<span class='attribution_code_keyword'>return</span> 724res. 725<span class='attribution_code_function'>json</span> 726({ ok: 727<span class='attribution_code_keyword'>true</span> 728, id: audit.id }); 729</div> 730</div> 731<div class='attribution_summary_grid'> 732<div class='attribution_summary_cell is_copilot_summary'> 733<div class='attribution_summary_value'>28%</div> 734<div class='attribution_summary_label'>Copilot</div> 735</div> 736<div class='attribution_summary_cell is_cursor_summary'> 737<div class='attribution_summary_value'>14%</div> 738<div class='attribution_summary_label'>Cursor</div> 739</div> 740<div class='attribution_summary_cell is_claude_summary'> 741<div class='attribution_summary_value'>29%</div> 742<div class='attribution_summary_label'>Claude</div> 743</div> 744<div class='attribution_summary_cell is_human_summary'> 745<div class='attribution_summary_value'>29%</div> 746<div class='attribution_summary_label'>Human</div> 747</div> 748</div> 749</div> 750</div> 751</div> 752<div class='product_showcase_row is_revealable is_reversed_layout'> 753<div class='showcase_copy_column'> 754<div class='showcase_kicker'>02 · AI hotspot directories</div> 755<h3 class='showcase_heading'> 756Find the folders where AI is 757<em>creating</em> 758more work than it saves. 759</h3> 760<p class='showcase_paragraph'> 761Not every directory responds to AI the same way. GitClear surfaces the folders where 762<strong>AI-assisted code has elevated defect and duplication rates</strong> 763â so you can coach, gate, or restrict tool access before it compounds. 764</p> 765<ul class='showcase_bullets'> 766<li> 767Per-directory 768<strong>AI %, defect Î, duplication Î</strong> 769</li> 770<li>Risk score normalized against your own baseline</li> 771<li>Exportable as quarterly engineering review artifact</li> 772</ul> 773</div> 774<div class='showcase_shot_column'> 775<div class='hotspot_view'> 776<div class='hotspot_head'> 777<span>AI hotspot directories â defect & duplication risk</span> 778<span>last 90d</span> 779</div> 780<div class='hotspot_table'> 781<div class='hotspot_row is_header_row'> 782<span>Directory</span> 783<span class='is_right_aligned'>AI %</span> 784<span class='is_right_aligned'>Defect</span> 785<span class='is_right_aligned'>Dupl</span> 786<span>Risk</span> 787</div> 788<div class='hotspot_row'> 789<span class='hotspot_path'>src/api/payments/</span> 790<span class='hotspot_value is_medium_value'>68%</span> 791<span class='hotspot_value is_high_value'>+4.1%</span> 792<span class='hotspot_value is_high_value'>3.2Ã</span> 793<div class='hotspot_risk_track'> 794<div class='hotspot_risk_fill is_high_risk' style='width: 86%;'></div> 795</div> 796</div> 797<div class='hotspot_row'> 798<span class='hotspot_path'>lib/auth/oauth/</span> 799<span class='hotspot_value is_low_value'>54%</span> 800<span class='hotspot_value is_medium_value'>+2.8%</span> 801<span class='hotspot_value is_medium_value'>2.4Ã</span> 802<div class='hotspot_risk_track'> 803<div class='hotspot_risk_fill is_medium_risk' style='width: 64%;'></div> 804</div> 805</div> 806<div class='hotspot_row'> 807<span class='hotspot_path'>app/models/user/</span> 808<span class='hotspot_value is_low_value'>47%</span> 809<span class='hotspot_value is_medium_value'>+1.2%</span> 810<span class='hotspot_value is_low_value'>1.9Ã</span> 811<div class='hotspot_risk_track'> 812<div class='hotspot_risk_fill is_medium_risk' style='width: 44%;'></div> 813</div> 814</div> 815<div class='hotspot_row'> 816<span class='hotspot_path'>src/components/ui/</span> 817<span class='hotspot_value is_high_value'>71%</span> 818<span class='hotspot_value is_low_value'>+0.3%</span> 819<span class='hotspot_value is_low_value'>1.4Ã</span> 820<div class='hotspot_risk_track'> 821<div class='hotspot_risk_fill is_low_risk' style='width: 28%;'></div> 822</div> 823</div> 824<div class='hotspot_row'> 825<span class='hotspot_path'>test/integration/</span> 826<span class='hotspot_value is_high_value'>82%</span> 827<span class='hotspot_value is_low_value'>-0.1%</span> 828<span class='hotspot_value is_low_value'>0.8Ã</span> 829<div class='hotspot_risk_track'> 830<div class='hotspot_risk_fill is_low_risk' style='width: 16%;'></div> 831</div> 832</div> 833</div> 834</div> 835</div> 836</div> 837<div class='product_showcase_row is_revealable'> 838<div class='showcase_copy_column'> 839<div class='showcase_kicker'>03 · Cohort comparison</div> 840<h3 class='showcase_heading'> 841See human vs. LLM code, measured by the 842<em>same yardstick.</em> 843</h3> 844<p class='showcase_paragraph'> 845GitClear's Diff Delta metric works the same way whether a line came from Claude or a senior staff engineer. Compare durable change velocity, rework rate, and review time 846<strong>across cohorts</strong> 847â without apples-to-oranges caveats. 848</p> 849<ul class='showcase_bullets'> 850<li>Cohort views by team, repo, or AI tool usage level</li> 851<li> 852<strong>Side-by-side weekly trends</strong> 853â AI power users vs. non-adopters 854</li> 855<li>Statistical significance flags on every delta</li> 856</ul> 857</div> 858<div class='showcase_shot_column'> 859<div class='cohort_view'> 860<div class='cohort_head'> 861<span>Durable change · AI-assisted vs. human-authored</span> 862<span>12 wk</span> 863</div> 864<div class='cohort_grid'> 865<div class='cohort_card'> 866<div class='cohort_card_head'>
867<div class='cohort_card_title'> 868<span class='cohort_dot is_ai_dot'></span> 869AI-assisted 870</div> 871<div class='cohort_count'>11 devs</div> 872</div> 873<div class='cohort_stat'> 874<span class='cohort_stat_label'>Diff Delta / wk</span> 875<span class='cohort_stat_value is_positive_value'>+18%</span> 876</div> 877<div class='cohort_stat'> 878<span class='cohort_stat_label'>Rework rate (30d)</span> 879<span class='cohort_stat_value is_negative_value'>12%</span> 880</div> 881<div class='cohort_stat'> 882<span class='cohort_stat_label'>PRs merged / wk</span> 883<span class='cohort_stat_value'>34</span> 884</div> 885<div class='cohort_stat'> 886<span class='cohort_stat_label'>Lead time</span> 887<span class='cohort_stat_value'>1.4d</span> 888</div> 889</div> 890<div class='cohort_card'> 891<div class='cohort_card_head'> 892<div class='cohort_card_title'> 893<span class='cohort_dot is_human_dot'></span> 894Human-authored 895</div> 896<div class='cohort_count'>3 devs</div> 897</div> 898<div class='cohort_stat'> 899<span class='cohort_stat_label'>Diff Delta / wk</span> 900<span class='cohort_stat_value'>baseline</span> 901</div> 902<div class='cohort_stat'> 903<span class='cohort_stat_label'>Rework rate (30d)</span> 904<span class='cohort_stat_value is_positive_value'>9%</span> 905</div> 906<div class='cohort_stat'> 907<span class='cohort_stat_label'>PRs merged / wk</span> 908<span class='cohort_stat_value'>28</span> 909</div> 910<div class='cohort_stat'> 911<span class='cohort_stat_label'>Lead time</span> 912<span class='cohort_stat_value'>2.2d</span> 913</div> 914</div> 915</div> 916<div class='cohort_chart_box'> 917<div class='cohort_chart_head'> 918<span>Weekly durable change</span> 919<div class='cohort_chart_legend'> 920<span> 921<span class='cohort_dot is_ai_dot is_legend_dot'></span> 922AI 923</span> 924<span> 925<span class='cohort_dot is_human_dot is_legend_dot'></span> 926Human 927</span> 928</div> 929</div> 930<div class='cohort_bars_grid' id='cohort_bars_grid'></div> 931</div> 932</div> 933</div> 934</div> 935</div> 936</section> 937<section class='methodology_section' id='methodology'> 938<div class='ai_roi_v2_tight_container'> 939<div class='methodology_section_head'> 940<div class='section_eyebrow_label'>The methodology</div> 941<h2 class='section_headline_text'> 942Inspired by Google DORA. Built for the 943<em>AI era.</em> 944</h2> 945<p class='section_intro_copy'> 946Three inputs, one defensible number â so finance, your board, and your own engineers can all read the same scorecard without arguing about what it means. 947</p> 948</div> 949<div class='methodology_features_grid'> 950<div class='methodology_feature_card'> 951<div class='methodology_feature_icon'>01</div> 952<h4 class='methodology_feature_heading'>Attribution</h4> 953<p class='methodology_feature_copy'>AI usage APIs plus commit heuristics plus agent telemetry hooks â not survey estimates. Every line traceable to the model that wrote it.</p> 954</div> 955<div class='methodology_feature_card'> 956<div class='methodology_feature_icon'>02</div> 957<h4 class='methodology_feature_heading'>Output quality</h4> 958<p class='methodology_feature_copy'>Diff Delta quantifies durable change vs. churn. Human and LLM code measured with the same metric, across the same time window.</p> 959</div> 960<div class='methodology_feature_card'> 961<div class='methodology_feature_icon'>03</div> 962<h4 class='methodology_feature_heading'>Developer experience</h4> 963<p class='methodology_feature_copy'>Self-reported hours saved and satisfaction scores. Productivity gains don't count if your best engineers are walking.</p> 964</div> 965</div> 966</div> 967</section> 968<section class='gc_micro_demo width_wrapper' id=''> 969<div class='gc_micro_demo_inner'> 970<div class='gc_micro_demo_copy'> 971<div class='gc_micro_demo_eyebrow'>Live data agent · 62-second micro-demo</div> 972<h3 class='gc_micro_demo_title'>Ask plain-English questions of your real repo data.</h3> 973<p class='gc_micro_demo_caption'>Once the scorecard is live, GitClear's data agent answers ad-hoc questions about AI authorship, durability, and team output â no SQL, no pre-built dashboard required.</p> 974</div> 975<div class='gc_micro_demo_player'> 976<video aria-label='GitClear real-time data agent demo' class='gc_micro_demo_video' playsinline='true' poster='https://asset.gitclear.com/assets/home/micro-demo-preview-f87bb451ca1a8dd9541bfb0746fb67f5c1e874a76fa1561ee610f95e895e4b86.jpg' preload='none' src='https://image.gitclear.com/marketing/micro-demo-data-agent.mov'> 977Your browser does not support embedded video. 978</video> 979<button aria-label='Play demo video' class='gc_micro_demo_play' onclick='const v=this.previousElementSibling; v.setAttribute('controls',''); v.play(); this.hidden=true;' type='button'>
980<svg aria-hidden class='gc_micro_demo_play_icon' viewBox='0 0 24 24'> 981<path d='M8 5v14l11-7z'></path> 982</svg> 983</button> 984</div> 985</div> 986</section> 987 988<div class='testimonial_wrapper'> 989<div class='testimonial_band'><div class='hero_testimonial_blurb'> 990<div data-react-class="TestimonialCarousel" data-react-props="{"testimonials":[{"author":"Lee Atchison, O'Reilly Author and Thought Leader","avatarImage":"https://asset.gitclear.com/assets/home/lee-a75d429197611b3fd83d15eb759e796073ffd7e8f76acf70b04566647a2e2876.jpg","avatarInitials":"LA","text":"Interested in understanding how AI is fundamentally transforming code quality? I recommend reading the entire GitClear report."},{"author":"Greg Thomas, CTO and Author","avatarImage":"https://asset.gitclear.com/assets/home/code-your-way-up-a2cc341bd7a7b766a01a42489f9c0a532ed70dcb2bb7085c84d6c0561dca7e09.jpg","avatarInitials":"GT","text":"It's hard to think of a single engineering manager I've worked with who wouldn't love to get their hands on reports like these."},{"author":"TheNewStack","avatarImage":"https://asset.gitclear.com/assets/home/new-stack-square-b6d07259b4a42cc7ab5ae0bae9d97c796a86c95cadba53d0bf5c0f634d15727e.png","avatarInitials":"NS","text":"GitClear finds pull request changes can radically cut the number of lines that coders need to review."},{"author":"Matt Johnston, CEO, GitKraken","avatarImage":"https://asset.gitclear.com/assets/logos/gitkraken-simple-574fcc0f369986602c27d9202b6fa8674d7a79f0a0804fdefa29e08c786a1c64.jpg","avatarInitials":"MJ","text":"We built GitKraken Insights in partnership with GitClear, whose engineering analytics technology powers some of the most sophisticated development organizations in the world. Their SEI capabilities create something unique in the market."},{"author":"LeadDev","avatarImage":"https://asset.gitclear.com/assets/home/leaddev-8bd7b851a79b0df186b619eda52b8f3b3c56b8422f3b132bf1008b38461f6a73.png","avatarInitials":"LD","text":"Does unchecked LLM-generated code threaten software maintainability? GitClear's 2025 AI Copilot Code Quality Report analyzes 211m changed lines for answers."},{"author":"Stack Overflow Podcast","avatarImage":"https://asset.gitclear.com/assets/home/stack-overflow-podcast-76f655cbb40b7e057a0d018c8e80ea473e7a7f8758b532d24d9bf7bb2ef61fb7.jpg","avatarInitials":"SO","text":"GitClear is a developer-friendly code review tool that delivers higher developer satisfaction and faster releases."},{"author":"Davit Lapiashvili, Bank of Georgia","avatarImage":"https://asset.gitclear.com/assets/home/davit-dce6b1dd02bcd470402b9042aa94c503c53911b5a2944ac8380933de28054fb8.jpg","avatarInitials":"DL","text":"GitClear'
990s code review analytics have been a game-changer for our teams. By providing a clear window into our processes, we have reduced our PR \"awaiting review\" times by more than 30%."},{"author":"Dr. Alain Abran, Professor of Software Engineering (13,000+ citations)","avatarImage":"https://asset.gitclear.com/assets/home/alain_abran-6eb54f994418a62deca8c547672c23d3522f16b75e465b11912fd7d0d71afcd5.jpg","avatarInitials":"AA","text":"For your intended purpose to provide an evidence base that your measurement approach (\"Diff Delta\") is better than other alternatives, I am of the opinion that you have clearly demonstrated its superiority."}]}" class=" react_component"></div> 991</div> 992</div> 993</div> 994<section class='research_band' id='research'> 995<h2> 996<svg class='gitclear_logo' viewBox='0 0 100 24' xmlns='http://www.w3.org/2000/svg'> 997<path class='logo_icon' d='M6.77 4.68C6.54 5.24 6.67 5.88 7.1 6.31C7.53 6.74 8.17 6.87 8.73 6.64C9.29 6.4 9.66 5.86 9.66 5.25C9.66 5.05 9.62 4.86 9.55 4.68C9.47 4.49 9.36 4.33 9.22 4.19C9.08 4.05 8.92 3.94 8.73 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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.