[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-2a35e753efc42256-microsoft-exp-a-b-tests-expose-1-3-feature-success-summary":3,"summaries-facets-categories":204,"summary-related-2a35e753efc42256-microsoft-exp-a-b-tests-expose-1-3-feature-success-summary":3773},{"id":4,"title":5,"ai":6,"body":13,"categories":151,"created_at":153,"date_modified":153,"description":144,"extension":154,"faq":153,"featured":155,"kicker_label":153,"meta":156,"navigation":187,"path":188,"published_at":153,"question":153,"scraped_at":189,"seo":190,"sitemap":191,"source_id":192,"source_name":193,"source_type":194,"source_url":195,"stem":196,"tags":197,"thumbnail_url":153,"tldr":201,"tweet":153,"unknown_tags":202,"__hash__":203},"summaries\u002Fsummaries\u002F2a35e753efc42256-microsoft-exp-a-b-tests-expose-1-3-feature-success-summary.md","Microsoft ExP: A\u002FB Tests Expose 1\u002F3 Feature Success Rate",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8621,2532,17390,0.0029677,{"type":14,"value":15,"toc":143},"minimark",[16,21,25,28,31,35,38,85,88,91,95,98,101,104,107,111],[17,18,20],"h2",{"id":19},"cultural-barriers-to-data-driven-product-decisions","Cultural Barriers to Data-Driven Product Decisions",[22,23,24],"p",{},"Microsoft's product teams historically relied on HiPPO (Highest-Paid-Person's Opinion) or gut feelings for feature prioritization, leading to inefficient development. Ronny Kohavi proposed the Experimentation Platform (ExP) in 2005, inspired by Ray Ozzie's memo emphasizing closed-loop measurement for web services. Technical scalability for sites like MSN homepage was solvable, but cultural resistance proved harder: teams feared failure, preferred analysis over testing, and misunderstood statistics (e.g., dismissing sample sizes despite millions of users). ExP's dual mission—build an easy-to-integrate platform and foster data-driven culture—started as a 7-person incubation in 2006. Adoption grew from 2 experiments in FY2007 to 44 in FY2009 across 20 properties like MSN Homepages, Office Online, and Support.microsoft.com.",[22,26,27],{},"Testimonials highlight the shift: one team noted experiments \"dispelled long held assumptions about video advertising\" and changed feature prioritization; MSN UK ditched \"opinion, gut feeling\" for statistical data; another called ExP \"essential for the future success of all Microsoft online properties.\" ExP tackled resistance through education (monthly seminars), weekly result emails for institutional memory, and proving value via quick wins, enabling teams to resolve debates with data rather than deferring to authority.",[22,29,30],{},"\"We should use the A\u002FB testing methodology a LOT more than we do today\" – Bill Gates, 2008. This endorsement from leadership validated ExP, countering inertia where even Search teams underused statistical rigor pre-ExP.",[17,32,34],{"id":33},"real-world-ab-tests-validate-or-kill-ideas-with-hard-metrics","Real-World A\u002FB Tests Validate or Kill Ideas with Hard Metrics",[22,36,37],{},"ExP ran controlled experiments (randomized A\u002FB tests) measuring Overall Evaluation Criteria (OEC) like revenue, engagement, or CTR to establish causality. Key insight: preconceptions fail—even experts guessed wrong. Examples across MSN properties:",[39,40,41,49,55,61,67,73,79],"ul",{},[42,43,44,48],"li",{},[45,46,47],"strong",{},"MSN Real Estate Widget",": Tested 6 designs for \"Find a home\" widget driving referral revenue. Only 3\u002F21 ZAAZ designers predicted winner (Treatment 5, simpler search-like UI). Result: +10% revenue from higher clickthrough. Rejected flashier variants.",[42,50,51,54],{},[45,52,53],{},"MSN UK Hotmail Module",": Control opened Hotmail in same tab (replacing MSN page); Treatment used new tab. On 1M users over 16 days: +8.9% clicks per user on MSN homepage, boosting engagement. Rolled out to UK\u002FUS. Site manager: data flipped team rejection.",[42,56,57,60],{},[45,58,59],{},"MSN Entertainment Video Ads",": Pre-roll (Control) vs. post-roll (Treatment). OEC: 6-week user return rate on cohort. +2% returns insufficient vs. -50% ad impressions. Bonus: Cutting ad interval from 180s to 90s insensitive to users, significantly boosting annual revenue—deployed globally.",[42,62,63,66],{},[45,64,65],{},"MSN Homepage Ads",": Adding 3 below-fold ads projected $10k+\u002Fday but risked UX. Monetized page views\u002Fclicks via SEM costs. On 5% traffic (12 days): -0.35% relative CTR and page views\u002Fuser-day. Lost value > ad gains; idea killed.",[42,68,69,72],{},[45,70,71],{},"Support.microsoft.com Personalization",": Generic top issues (Control) vs. browser\u002FOS-specific (Treatment). +50% CTR; proved simple personalization value, leading to core system integration.",[42,74,75,78],{},[45,76,77],{},"MSN US Search Header",": Magnifying glass (Control) vs. words like \"Search\" (Treatments). +1.23% searches; actionable labels beat icons, aligning with Steve Krug's usability advice despite prior ignores.",[42,80,81,84],{},[45,82,83],{},"Pre-Bing Search Branding",": Variant increased Search box clicks, searches, and page clicks—informed Bing launch design.",[22,86,87],{},"These spanned widgets, ads, personalization, UX—showing experiments resolve tradeoffs (e.g., revenue vs. loyalty) at scale. Multi-variant and cohort tracking handled complexity.",[22,89,90],{},"\"Passion is inversely proportional to the amount of real information available\" – Gregory Benford, 1980. Authors invoke this to explain heated debates quelled by data.",[17,92,94],{"id":93},"roi-reality-low-success-rates-demand-rigorous-testing","ROI Reality: Low Success Rates Demand Rigorous Testing",[22,96,97],{},"ExP's ROI: Accelerated innovation by pruning bad ideas early. Sobering stats: ~1\u002F3 of tested ideas improve intended metrics, matching industry (Amazon \u003C50%). Internal evaluations pass most ideas, but experiments reveal failures—bias toward uncertain ideas doesn't fully explain. Launching without tests misses small effects (external noise dominates sequential observation) and backouts cost more.",[22,99,100],{},"Pre-ExP, Microsoft underused experiments outside Search\u002FMSN; no consistent stats. ExP centralized expertise for scalability. Humans intuit poorly (e.g., pattern-seeking loses to simple frequency guessing, per psych studies). Tradeoff: Experiments add upfront time but avoid sunk costs.",[22,102,103],{},"\"The fascinating thing about intuition is that a fair percentage of the time it's fabulously, gloriously, achingly wrong\" – John Quarto-vonTivadar, FutureNow. Underscores why ExP's data trumps HiPPO.",[22,105,106],{},"Progress: FY2007: 2 expts; FY2008: 8; FY2009: 44. Search evolved independently with ExP stats. Cultural wins: Teams now prioritize via data, share learnings.",[17,108,110],{"id":109},"key-takeaways","Key Takeaways",[39,112,113,116,119,122,125,128,131,134,137,140],{},[42,114,115],{},"Run A\u002FB tests on all major features using OEC to causally measure impact—randomization ensures differences stem from changes.",[42,117,118],{},"Define monetized OEC for tradeoffs (e.g., assign $ to clicks\u002Fpage views via SEM) to compare revenue vs. UX.",[42,120,121],{},"Expect ~1\u002F3 success rate; test uncertain ideas early to kill losers before full rollout.",[42,123,124],{},"Overcome culture via examples, education, leadership buy-in (Gates\u002FOzzie), and quick wins—share results widely.",[42,126,127],{},"Use cohorts\u002Flong-term tracking for retention; multi-variant for design contests.",[42,129,130],{},"Personalization\u002FUX tweaks (e.g., labels > icons, tabs > replaces) yield outsized gains—test assumptions.",[42,132,133],{},"Centralize platform for stats expertise\u002Fscalability; avoid sequential launches (noise hides signals).",[42,135,136],{},"Institutionalize: Weekly emails, seminars build memory\u002Fadvocacy.",[42,138,139],{},"Simpler often wins (e.g., search-like widgets, post-roll limits).",[42,141,142],{},"Stats matter: Millions of users still need significance tests.",{"title":144,"searchDepth":145,"depth":145,"links":146},"",2,[147,148,149,150],{"id":19,"depth":145,"text":20},{"id":33,"depth":145,"text":34},{"id":93,"depth":145,"text":94},{"id":109,"depth":145,"text":110},[152],"Product Strategy",null,"md",false,{"content_references":157,"triage":182},[158,164,168,172,175,178],{"type":159,"title":160,"author":161,"publisher":162,"context":163},"paper","Controlled experiments on the web: survey and practical guide","Kohavi, Longbotham, Sommerfield, & Henne","journal","cited",{"type":165,"title":166,"author":167,"context":163},"other","The Internet Services Disruption memo","Ray Ozzie",{"type":169,"title":170,"author":171,"context":163},"book","In Search of Excellence","Tom Peters and Robert Waterman",{"type":169,"title":173,"author":174,"context":163},"Don’t Make Me Think","Steve Krug",{"type":169,"title":176,"author":177,"context":163},"The Drunkard’s Walk","Leonard Mlodinow",{"type":165,"title":179,"author":180,"context":181},"Benford's Law of Controversy","Gregory Benford","mentioned",{"relevance":183,"novelty":184,"quality":183,"actionability":184,"composite":185,"reasoning":186},4,3,3.6,"Category: Product Strategy. The article discusses the implementation of A\u002FB testing at Microsoft, addressing a key pain point for product-minded builders regarding data-driven decision-making. It provides insights into cultural barriers and the importance of experimentation, which are actionable for teams looking to adopt similar practices.",true,"\u002Fsummaries\u002F2a35e753efc42256-microsoft-exp-a-b-tests-expose-1-3-feature-success-summary","2026-04-16 03:07:41",{"title":5,"description":144},{"loc":188},"2a35e753efc42256","__oneoff__","article","http:\u002F\u002Fai.stanford.edu\u002F~ronnyk\u002FExPThinkWeek2009Public.pdf","summaries\u002F2a35e753efc42256-microsoft-exp-a-b-tests-expose-1-3-feature-success-summary",[198,199,200],"product-strategy","dev-productivity","ab-testing","Microsoft's Experimentation Platform (ExP) enabled A\u002FB testing on high-traffic sites, shifting culture from HiPPO to data-driven decisions—yet only 1\u002F3 of tested ideas improved key metrics, humbling 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This leads high performers to repetitive tasks or overwhelm, trading high-impact work for noise. Chasing speed via more tabs and hacks fails because 'fast eats slow' rewards throughput, not tool knowledge. Result: frantic builders drown in unbounded optimization, as XKCD illustrates—automation rarely eliminates the original task, just adds maintenance.",[22,3792,3793],{},"XKCD's optimization table sets a clear rule: only automate if time saved (frequency × duration) exceeds setup cost. For a daily 5-minute task, cap setup at 25 hours; beyond that, ship messy and iterate later. This permission slip prevents spiraling: shave 1 minute daily, reclaim a full day yearly through compounding.",[17,3795,3797],{"id":3796},"anchor-on-outcomes-for-stable-speed","Anchor on Outcomes for Stable Speed",[22,3799,3800],{},"Define a North Star outcome like 'ship 1 prototype weekly,' 'automate 1 workflow monthly,' or 'turn work into reusable assets.' Stable goals let tools evolve without derailing you. Momentum beats mastery—replace 'keep up' with one concrete weekly ship: a tiny agent, Claude Code prototype, evaluation harness, or personal automation. This builds reliable ambiguity-to-artifact pipelines, turning frantic energy into calm capability.",[17,3802,3804],{"id":3803},"eudaimonia-stack-toolchains-over-collections","Eudaimonia Stack: Toolchains Over Collections",[22,3806,3807],{},"Craft repeatable toolchains reducing idea-to-prototype friction, minimizing decisions. Set hard XKCD budgets: if not worth it, ship imperfect. Protect identity—don't become a 'tool person'; become one who converts ambiguity to decisions. This aligns with eudaimonia: building with purpose compounds capability and calm. Evidence: 13k signed up for OpenClaw workshop; masterclass ships Mac minis to every student for hands-on leverage, proving demand for systems over tips.",{"title":144,"searchDepth":145,"depth":145,"links":3809},[3810,3811,3812],{"id":3786,"depth":145,"text":3787},{"id":3796,"depth":145,"text":3797},{"id":3803,"depth":145,"text":3804},[207],{},"\u002Fsummaries\u002Fescape-ai-tool-anxiety-with-eudaimonia-stack-summary","2026-04-08 21:21:17",{"title":3776,"description":144},{"loc":3815},"05d6ac8505c9d278","AI Product Academy","https:\u002F\u002Funknown","summaries\u002Fescape-ai-tool-anxiety-with-eudaimonia-stack-summary",[3824,198,199],"ai-tools","Chasing AI tools creates noise, not speed—anchor on North Star outcomes, toolchains, XKCD budgets, and weekly ships for calm, compounding throughput.",[199],"LScDa94VKuPh8fHsQ_OwZxr1LHQMCyTP1zho4lWVBus",{"id":3829,"title":3830,"ai":3831,"body":3836,"categories":3992,"created_at":153,"date_modified":153,"description":144,"extension":154,"faq":153,"featured":155,"kicker_label":153,"meta":3993,"navigation":187,"path":4029,"published_at":153,"question":153,"scraped_at":4030,"seo":4031,"sitemap":4032,"source_id":4033,"source_name":193,"source_type":194,"source_url":4034,"stem":4035,"tags":4036,"thumbnail_url":153,"tldr":4037,"tweet":153,"unknown_tags":4038,"__hash__":4039},"summaries\u002Fsummaries\u002Fa6c83f5afba5b730-ai-productivity-paradox-wrong-metrics-hide-gains-summary.md","AI Productivity Paradox: Wrong Metrics Hide Gains",{"provider":7,"model":8,"input_tokens":3832,"output_tokens":3833,"processing_time_ms":3834,"cost_usd":3835},8793,2781,14316,0.00312645,{"type":14,"value":3837,"toc":3984},[3838,3842,3845,3848,3852,3855,3858,3884,3887,3890,3894,3897,3918,3921,3924,3928,3931,3934,3937,3941,3944,3947,3950,3952],[17,3839,3841],{"id":3840},"the-apparent-disconnect-surging-adoption-stagnant-stats","The Apparent Disconnect: Surging Adoption, Stagnant Stats",[22,3843,3844],{},"AI use is exploding—McKinsey reports 88% of organizations apply it in at least one function, with Bain noting 40% of software dev pilots scaling to production (vs. 32% in customer service). U.S. AI investments hit $109B, adoption up 340% recently. Yet productivity growth hovers at 2.3%, matching the 2.2% historical average. No macro acceleration appears, despite hype. Marco van Hurne calls this the 'AI Productivity Paradox': inputs and excitement surge, outputs stay flat. Reason? Adoption ≠ transformation. Pilots deploy but workflows, roles, data, and metrics remain unchanged, yielding dashboards and costs without gains.",[22,3846,3847],{},"\"Adoption is not transformation. 'We use AI' often means 'someone opened ChatGPT twice, created a project and renamed it ‘knowledge management’'\"—van Hurne highlights how superficial use inflates stats while real change lags, trapping firms in pilots.",[17,3849,3851],{"id":3850},"j-curve-time-lag-upfront-investments-drag-before-payoff","J-Curve Time Lag: Upfront Investments Drag Before Payoff",[22,3853,3854],{},"General-purpose tech like AI follows a J-curve (per Brynjolfsson, Rock, Syverson): initial dips from 'complementary capital'—organizational redesign, training, data prep, R&D—before gains. Productivity drops short-term as firms invest in intangibles treated as costs. MIT\u002FU.S. Census study: manufacturing AI adopters saw drops, gains only after 4+ years.",[22,3856,3857],{},"Key buckets:",[39,3859,3860,3866,3872,3878],{},[42,3861,3862,3865],{},[45,3863,3864],{},"Workflows\u002Froles",": Redesign decision rights; failure: unchanged chaos amplified.",[42,3867,3868,3871],{},[45,3869,3870],{},"Skills",": Train\u002Fhire; track via completion rates.",[42,3873,3874,3877],{},[45,3875,3876],{},"Data",": Clean\u002Fgovern; avoid 'confident garbage'.",[42,3879,3880,3883],{},[45,3881,3882],{},"Experimentation",": Structured loops, not one-offs.",[22,3885,3886],{},"\"AI doesn’t create productivity, systems do, and AI only amplifies whatever system you already have, whether that system is a ‘well-run operation’ or a ‘chaos with lots of meetings’\"—van Hurne stresses AI as accessory; build org\u002Fpeople\u002Fdata\u002Flearning around it, or get costs without ROI.",[22,3888,3889],{},"Early signals: redesigned roles cut cycle times 20-40%; poor data spikes errors 2-3x. Without this, CFOs see 'investment hangover'.",[17,3891,3893],{"id":3892},"measurement-breakdown-task-level-wins-lost-in-aggregates","Measurement Breakdown: Task-Level Wins Lost in Aggregates",[22,3895,3896],{},"GDP tools, built for physical goods, miss AI's intangible, task-level impact. Issues:",[3898,3899,3900,3906,3912],"ol",{},[42,3901,3902,3905],{},[45,3903,3904],{},"No AI bucket",": BEA notes AI hides in 'software publishing\u002FIT services'; proposes satellite accounts.",[42,3907,3908,3911],{},[45,3909,3910],{},"Job vs. task",": Stats track jobs\u002Findustries; AI hits tasks (e.g., faster drafts but longer reviews). 'Project Iceberg': visible job layer hides task automation.",[42,3913,3914,3917],{},[45,3915,3916],{},"Intangibles undervalued",": WIPO\u002FDeloitte: intangibles (datasets, training) surge but expensed as costs, not assets—short-term drag despite long-term value.",[22,3919,3920],{},"Task gains absorb into systems: 10min saved drafting → 20min verifying + coordination = net loss. National stats understate as AI embeds in broad categories.",[22,3922,3923],{},"\"We’re trying to track a high-tech, intangible economy using frameworks built for factories and physical capital. No wonder the stats look unimpressed.\"—van Hurne critiques 'meat thermometer on a cloud', urging task\u002Fend-to-end outcome tracking.",[17,3925,3927],{"id":3926},"workflow-redesign-failure-pilots-die-at-integration","Workflow Redesign Failure: Pilots Die at Integration",[22,3929,3930],{},"Most bolt AI onto broken processes: faster outputs create downstream friction (e.g., escalations, debugging). MIT Sloan: 'work-backward'—deconstruct tasks, assign AI\u002Fhuman\u002FAI+human, rebuild end-to-end, measure outcomes (time\u002Fquality\u002Fcost\u002Frisk).",[22,3932,3933],{},"Pilot funnel collapses at integration: ideas → pilots (wide), then data cleanup\u002Fcompliance\u002Fchange management kills most; scaling tiny. Production demands clean data, monitoring, ownership—pilot 'feels faster' won't cut it.",[22,3935,3936],{},"\"It is easier to change the way the organization works, than to change the underlying technology.\"—van Hurne flips ERP wisdom: tool-forward pilots = 'graveyard'; redesign yields 30-50% cycle drops, quality rises.",[17,3938,3940],{"id":3939},"perception-trap-ai-can-slow-experts-users-overconfident","Perception Trap: AI Can Slow Experts, Users Overconfident",[22,3942,3943],{},"METR RCT: frontier AI (Claude) slowed experienced devs via verification overhead (fixing output > time saved), quality mismatch (ignores codebase norms), context limits (naive suggestions in large repos). Users feel faster but deliver slower.",[22,3945,3946],{},"Mechanisms: over-reliance skips thinking; coordination rises. Negative productivity hides in 'confident garbage'.",[22,3948,3949],{},"\"Giving developers access to frontier AI tools made them slower at completing tasks.\"—van Hurne cites METR, warning complex work backfires without redesign.",[17,3951,110],{"id":109},[39,3953,3954,3957,3960,3963,3966,3969,3972,3975,3978,3981],{},[42,3955,3956],{},"Track complementary capital early: monitor role changes, training uptake, data quality, experiment velocity.",[42,3958,3959],{},"Measure task\u002Fend-to-end: ignore job aggregates; log time\u002Fquality pre\u002Fpost-AI per workflow.",[42,3961,3962],{},"Work backward: task-decompose jobs, reassign AI\u002Fhuman, rebuild flows before pilots.",[42,3964,3965],{},"Demand production rigor: clean data, guardrails, monitoring—not demo vibes.",[42,3967,3968],{},"Watch for backfire: RCT-test AI in real tasks; verify net speed, not gut feel.",[42,3970,3971],{},"Build intangibles as assets: capitalize training\u002Fdatasets for true ROI view.",[42,3973,3974],{},"Redesign first: AI amplifies systems—fix chaos or amplify it.",[42,3976,3977],{},"Use satellite metrics: task logs, cycle times over GDP proxies.",[42,3979,3980],{},"Iterate structured: kill 'one pilot, one funeral'; loop learnings.",[42,3982,3983],{},"Align incentives: tie bonuses to outcomes, not tool installs.",{"title":144,"searchDepth":145,"depth":145,"links":3985},[3986,3987,3988,3989,3990,3991],{"id":3840,"depth":145,"text":3841},{"id":3850,"depth":145,"text":3851},{"id":3892,"depth":145,"text":3893},{"id":3926,"depth":145,"text":3927},{"id":3939,"depth":145,"text":3940},{"id":109,"depth":145,"text":110},[207],{"content_references":3994,"triage":4027},[3995,3999,4002,4005,4008,4011,4014,4017,4020,4024],{"type":3996,"title":3997,"author":3998,"context":163},"report","McKinsey’s latest global survey","McKinsey",{"type":3996,"title":4000,"author":4001,"context":163},"Bain report on pilots to production","Bain",{"type":159,"title":4003,"author":4004,"context":163},"Productivity J-curve","Erik Brynjolfsson, Daniel Rock, Chad Syverson",{"type":3996,"title":4006,"author":4007,"context":163},"MIT and U.S. Census Bureau manufacturing study","MIT, U.S. Census Bureau",{"type":3996,"title":4009,"author":4010,"context":163},"AI satellite account proposal","U.S. Bureau of Economic Analysis (BEA)",{"type":165,"title":4012,"author":4013,"context":163},"Intangible investment surge","World Intellectual Property Organization",{"type":3996,"title":4015,"author":4016,"context":163},"Intangibles in large businesses","Deloitte",{"type":3996,"title":4018,"author":4019,"context":163},"METR study on AI and developers","METR (Model Evaluation and Threat Research)",{"type":165,"title":4021,"author":4022,"url":4023,"context":163},"Empirical reflections on the silent murdering of the workforce via task-level automation","Marco van Hurne","https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fempirical-reflections-silent-murdering-workforce-via-marco-van-hurne-hwgvf\u002F",{"type":165,"title":4025,"author":4022,"url":4026,"context":163},"The AI productivity divide","https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fai-productivity-divide-marco-van-hurne-ydkqf\u002F",{"relevance":183,"novelty":184,"quality":183,"actionability":184,"composite":185,"reasoning":4028},"Category: Product Strategy. The article discusses the disconnect between AI adoption and productivity, addressing a key pain point for product-minded builders who need to understand how to effectively integrate AI into their workflows. It provides insights into the importance of redesigning systems to unlock value, which is actionable but lacks specific frameworks or step-by-step guidance.","\u002Fsummaries\u002Fa6c83f5afba5b730-ai-productivity-paradox-wrong-metrics-hide-gains-summary","2026-04-16 02:56:49",{"title":3830,"description":144},{"loc":4029},"a6c83f5afba5b730","https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fai-productivity-paradox-works-fine-youre-just-like-its-van-hurne-inkyc\u002F?trk=article-ssr-frontend-pulse_little-text-block","summaries\u002Fa6c83f5afba5b730-ai-productivity-paradox-wrong-metrics-hide-gains-summary",[3824,198,199],"High AI adoption hasn't spiked productivity stats due to time lags, outdated measurements, shallow workflows, and AI sometimes slowing workers—redesign systems to unlock real value.",[199],"oF1FW7rt_T9P6GbLB3uD24T-fTJmOKfkLV59tAFZuSQ",{"id":4041,"title":4042,"ai":4043,"body":4048,"categories":4283,"created_at":153,"date_modified":153,"description":4284,"extension":154,"faq":153,"featured":155,"kicker_label":153,"meta":4285,"navigation":187,"path":4286,"published_at":4287,"question":153,"scraped_at":4288,"seo":4289,"sitemap":4290,"source_id":4291,"source_name":4292,"source_type":4293,"source_url":4294,"stem":4295,"tags":4296,"thumbnail_url":153,"tldr":4298,"tweet":153,"unknown_tags":4299,"__hash__":4300},"summaries\u002Fsummaries\u002F09e94e776004a54b-master-restraint-decide-what-not-to-build-summary.md","Master Restraint: Decide What NOT to Build",{"provider":7,"model":8,"input_tokens":4044,"output_tokens":4045,"processing_time_ms":4046,"cost_usd":4047},8320,2153,21660,0.002718,{"type":14,"value":4049,"toc":4273},[4050,4054,4057,4063,4066,4069,4073,4076,4096,4099,4105,4110,4113,4117,4120,4125,4128,4145,4151,4155,4158,4168,4171,4176,4180,4183,4221,4227,4233,4236,4242,4244],[17,4051,4053],{"id":4052},"speed-without-restraint-bloats-products","Speed Without Restraint Bloats Products",[22,4055,4056],{},"AI flips workflows: building now takes 20% of time, planning 80%. But planning shifted from 'how to build' to 'should we build?' Without scarcity, builders ship everything possible, drowning products in features. Enterprise demands on a focused client portal (file sharing\u002Fapprovals) tempt adding invoicing\u002Ftime-tracking—each buildable in a weekend. Result: Onboarding swells, support shifts to unrelated issues, original users feel alienated as invoicing seekers dilute focus.",[22,4058,4059,4062],{},[45,4060,4061],{},"Quote:"," \"Restraint is about choosing focus over capability. The discipline to say, 'We could build this, but it doesn't belong here.'\"",[22,4064,4065],{},"Instead, integrate via APIs or agent skills (e.g., pre-built invoicing agent). This serves needs without bloating core identity. Restraint applies equally to internal tools: Avoid monoliths for content ops (news monitoring, drafting, visuals, publishing). Break into purpose-built micro-tools connected by agents—easier to maintain as processes evolve.",[22,4067,4068],{},"Agents excel with focused systems; monoliths brittle under change. Common mistake: Overbuilding from unchecked capability, leading to maintenance hell.",[17,4070,4072],{"id":4071},"spec-driven-development-plan-mode-as-industry-standard","Spec-Driven Development: Plan Mode as Industry Standard",[22,4074,4075],{},"By 2026, tools enforce planning first. Claude Code, Cursor, Codeex (all use Shift-Tab for plan mode) converge on spec-driven workflows. Feed a PRD (overview, problem, target customer, user flow, in\u002Fout scope, tech context) into plan mode:",[39,4077,4078,4084,4090],{},[42,4079,4080,4083],{},[45,4081,4082],{},"Claude Code:"," Auto-enters plan mode on PRD paste; asks clarifying questions, generates technical schematics\u002Fto-dos. Auto-accept edits to build.",[42,4085,4086,4089],{},[45,4087,4088],{},"Cursor:"," Pastes full PRD (no compaction); spawns sub-agents, iterative questions (even on auto-model). Outputs architecture diagrams, data flows, tracked to-dos.",[42,4091,4092,4095],{},[45,4093,4094],{},"Codeex:"," Text-based technical plan post-questions; simple 'implement' step.",[22,4097,4098],{},"All track progress autonomously. Nimbleist differentiates: Visual workspace with Markdown mockups, Excalidraw\u002FMermaid diagrams, data models alongside agent sessions. Tasks auto-update; local Markdown storage (Git-friendly, no lock-in). Spot scope creep visually before coding.",[22,4100,4101,4104],{},[45,4102,4103],{},"Before\u002Fafter:"," Raw PRD → Tool-specific implementation plan (technical breakdown, risks clarified). Quality criteria: Clarifying questions ensure alignment; diagrams reveal gaps.",[22,4106,4107,4109],{},[45,4108,4061],{}," \"Plan first, then build. Cloud code, cursor, codecs, planning is now a first class feature in all of them... spec-driven development has become the industry standard.\"",[22,4111,4112],{},"Pitfall: Jumping to plan mode without strategic vetting builds polished junk.",[17,4114,4116],{"id":4115},"pre-planning-framework-shape-ideas-into-scoped-prds","Pre-Planning Framework: Shape Ideas into Scoped PRDs",[22,4118,4119],{},"Before coding tools, run a Claude (or LLM) conversation as strategic partner. Solo: You + AI. Team: Independent runs, then align on convergence\u002Fdivergence.",[4121,4122,4124],"h3",{"id":4123},"step-1-brain-dump-raw-idea-voice-dictation-recommended","Step 1: Brain Dump Raw Idea (Voice Dictation Recommended)",[22,4126,4127],{},"Use tools like MacOS Whisper Flow. Cover:",[39,4129,4130,4133,4136,4139,4142],{},[42,4131,4132],{},"Feature\u002Ftool description.",[42,4134,4135],{},"Primary customer (traction sources; self for internal).",[42,4137,4138],{},"Core problem (job-to-be-done: e.g., \"Agencies share deliverables\u002Fget approvals without email chaos\").",[42,4140,4141],{},"Existing solutions\u002Fgaps.",[42,4143,4144],{},"User feedback\u002Fquotes\u002Ffrustrations (use verbatim for authenticity).",[22,4146,4147,4150],{},[45,4148,4149],{},"Quality check:"," More customer words = better AI probing.",[4121,4152,4154],{"id":4153},"step-2-prompt-claude-as-thought-partner","Step 2: Prompt Claude as Thought Partner",[22,4156,4157],{},"Template:",[4159,4160,4165],"pre",{"className":4161,"code":4163,"language":4164},[4162],"language-text","I'm considering building [description]. Primary customer: [who]. Core problem: [job-to-be-done]. Existing: [gaps]. Feedback: [quotes].\n\nAct as strategic thought partner. Ask clarifying questions on purpose, vision, focus, problem. Be constructive: Challenge assumptions, surface trade-offs, spot scope creep risks. Conversation first—no rushed specs\u002Fsolutions.\n","text",[4166,4167,4163],"code",{"__ignoreMap":144},[22,4169,4170],{},"Let LLM generate questions (don't prescribe list—leverages reasoning). Back-and-forth uncovers blind spots.",[22,4172,4173,4175],{},[45,4174,4061],{}," \"Before I open plan mode in any tool, I run a conversation that determines whether I should be planning this thing at all. So this is the step that most builders and most teams are skipping and it's where restraint actually happens.\"",[4121,4177,4179],{"id":4178},"step-3-direct-to-prd-output","Step 3: Direct to PRD Output",[22,4181,4182],{},"After 3-5 rounds, steer to PRD:",[39,4184,4185,4191,4197,4203,4209,4215],{},[42,4186,4187,4190],{},[45,4188,4189],{},"Overview:"," One-paragraph pitch.",[42,4192,4193,4196],{},[45,4194,4195],{},"Problem:"," Precise job-to-be-done.",[42,4198,4199,4202],{},[45,4200,4201],{},"Target Customer:"," Who fits perfectly (exclude others).",[42,4204,4205,4208],{},[45,4206,4207],{},"Core User Flow:"," Step-by-step (diagrams if visual).",[42,4210,4211,4214],{},[45,4212,4213],{},"In\u002FOut of Scope:"," Restraint muscle—list exclusions explicitly.",[42,4216,4217,4220],{},[45,4218,4219],{},"Technical Context:"," High-level (e.g., stack, integrations).",[22,4222,4223,4226],{},[45,4224,4225],{},"Example evolution:"," Client portal raw idea → Clarified (agencies only, no PM\u002Finvoicing) → Scoped PRD → Plan mode.",[22,4228,4229,4232],{},[45,4230,4231],{},"Trade-offs:"," Time upfront saves rework; critical for solos blurring builder\u002FPM roles. Prerequisites: Basic PM concepts (job-to-be-done); comfortable prompting.",[22,4234,4235],{},"Fits broader workflow: Idea → Pre-plan (restraint) → PRD → Plan mode → Build.",[22,4237,4238,4241],{},[45,4239,4240],{},"Exercise:"," Voice-dump next idea; run framework independently if team. Compare PRDs before\u002Fafter: Bloat reduced?",[17,4243,110],{"id":109},[39,4245,4246,4249,4252,4255,4258,4261,4264,4267,4270],{},[42,4247,4248],{},"Always ask 'should we?' before 'how?': Use restraint to protect product identity.",[42,4250,4251],{},"Build micro-tools + agent connections over monoliths for ops.",[42,4253,4254],{},"Shift-Tab into plan mode in Claude Code\u002FCursor\u002FCodeex after PRD.",[42,4256,4257],{},"Brain-dump with customer quotes; prompt LLM to challenge assumptions\u002Fscope creep.",[42,4259,4260],{},"Output scoped PRD: Explicit in\u002Fout scope prevents feature bloat.",[42,4262,4263],{},"Visual tools like Nimbleist catch issues early via diagrams.",[42,4265,4266],{},"Run pre-planning solo\u002Fteam; align on divergences for strategy.",[42,4268,4269],{},"Voice dictation accelerates dumps; verbatim feedback grounds prompts.",[42,4271,4272],{},"Practice: Shape one raw idea to PRD this week—feed to tool, build only if passes restraint.",{"title":144,"searchDepth":145,"depth":145,"links":4274},[4275,4276,4277,4282],{"id":4052,"depth":145,"text":4053},{"id":4071,"depth":145,"text":4072},{"id":4115,"depth":145,"text":4116,"children":4278},[4279,4280,4281],{"id":4123,"depth":184,"text":4124},{"id":4153,"depth":184,"text":4154},{"id":4178,"depth":184,"text":4179},{"id":109,"depth":145,"text":110},[152],"AI can build anything now. The harder question is what deserves to be built. I break down why restraint is the most important skill in AI-first development, then give you a concrete framework for practicing it.\n\nI'll give you a pre-planning prompt template and demo how to use plan mode demos across all popular tools, plus a look at how I architect my own operations using focused tools connected by agent skills.\n\n👇 **Check out Nimbalyst**\nUse Nimbalyst for free - The visual workspace for building with Codex and Claude Code. https:\u002F\u002Fnimbalyst.com\n\n👇 **Your Builder Briefing (free)**\nhttps:\u002F\u002Fbuildermethods.com - Your free, 5-minute read to keep up with the latest tools & workflows for building with AI.\n\n👇 **Join Builder Methods Pro**\nhttps:\u002F\u002Fbuildermethods.com\u002Fpro - The membership for pros building with AI.  Courses.  Workshops.  Private community.  Video training library.\n\n👇 **Try my tools** (free open source):\nhttps:\u002F\u002Fbuildermethods.com\u002Fagent-os\nhttps:\u002F\u002Fbuildermethods.com\u002Fdesign-os\n\n▶️ Related videos:\nMaster these skills to gain an UNFAIR advantage: https:\u002F\u002Fyoutu.be\u002F7JBuA1GHAjQ\n\n💬 Drop a comment with your questions and requests for upcoming videos!\n\nChapters:\n\n00:00 Building software in 2026\n01:12  The new craft.\n02:05  Product-market-fit\n03:09 Internal-tool building.\n04:14 Spec-driven development\n12:07 Nimbalyst\n14:04 Shape before plan",{},"\u002Fsummaries\u002F09e94e776004a54b-master-restraint-decide-what-not-to-build-summary","2026-03-31 12:01:03","2026-04-03 21:22:23",{"title":4042,"description":4284},{"loc":4286},"09e94e776004a54b","Brian Casel","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=s_YTsqTLRxw","summaries\u002F09e94e776004a54b-master-restraint-decide-what-not-to-build-summary",[198,3824,4297,199],"prompt-engineering","AI speeds execution, but restraint—deciding 'should we build this?'—prevents scope creep. Use a pre-planning framework to shape raw ideas into scoped PRDs before spec-driven tools like Cursor or Claude Code.",[199],"mjM5aA0mRxkcukvREX81Pm0zTdgpK5zuJNIqsPrZBZ0",{"id":4302,"title":4303,"ai":4304,"body":4309,"categories":4412,"created_at":153,"date_modified":153,"description":144,"extension":154,"faq":153,"featured":155,"kicker_label":153,"meta":4413,"navigation":187,"path":4423,"published_at":4424,"question":153,"scraped_at":4425,"seo":4426,"sitemap":4427,"source_id":4428,"source_name":4429,"source_type":194,"source_url":4430,"stem":4431,"tags":4432,"thumbnail_url":153,"tldr":4435,"tweet":153,"unknown_tags":4436,"__hash__":4437},"summaries\u002Fsummaries\u002F046a0fd5ac47e3c7-ai-speeds-shipping-but-taste-wins-linear-cto-on-qu-summary.md","AI Speeds Shipping, But Taste Wins: Linear CTO on Quality",{"provider":7,"model":8,"input_tokens":4305,"output_tokens":4306,"processing_time_ms":4307,"cost_usd":4308},8690,2007,18233,0.00245005,{"type":14,"value":4310,"toc":4404},[4311,4315,4318,4321,4324,4328,4331,4334,4337,4341,4344,4347,4350,4354,4357,4360,4363,4367,4370,4373,4376,4378],[17,4312,4314],{"id":4313},"ai-lowers-barriers-amplifying-old-pitfalls","AI Lowers Barriers, Amplifying Old Pitfalls",[22,4316,4317],{},"Tuomas Artman, CTO and cofounder of Linear, warns that AI agents like Claude remove engineering friction, making it too easy to ship every feature request or whim. This echoes Steve Jobs' philosophy: \"Great products come out of saying no to 999 things and yes to one thing.\" Without gates, products become convoluted, confusing users. Artman draws from Uber hypergrowth, where relentless shipping outpaced rivals but eroded quality as revenue metrics overshadowed polish. Today, solo AI builders compete with teams, heightening the need for 'tasteful software'—high-quality experiences that provide a moat.",[22,4319,4320],{},"Gergely Orosz, interviewer and former Uber colleague, challenges if this is new; feature factories predated AI. Artman agrees but sees AI democratizing speed, forcing differentiation via craft. At Linear, they reject prototypes, grouping customer requests to solve root problems rather than surface symptoms. AI aids by summarizing feedback, but human judgment crafts ideal UX.",[22,4322,4323],{},"\"The pendulum has swung too far into the wrong direction where if you get a feature request you might now be in the position to just immediately ship it and that might be the wrong thing to do,\" Artman says.",[17,4325,4327],{"id":4326},"quality-as-competitive-edge-over-time","Quality as Competitive Edge Over Time",[22,4329,4330],{},"Metrics like Uber's revenue, trips taken, and time-to-first-trip fail to capture quality until competitors match features. Early Uber engineers obsessed over pixels—Artman recalls his first PR rejected for a two-pixel map overlay offset, measured precisely by the first iOS engineer. This upheld performance, but scale and revenue pressure shifted priorities. Low-price features like Uber Pool boosted metrics short-term, ignoring UX until Lyft matched and users defected gradually to smoother alternatives.",[22,4332,4333],{},"Artman predicts AI accelerates this: ship fast, match features, then lose to superior feel. Linear invests upfront in taste, using AI selectively. Bugs flow constantly; 10% now auto-fixed via single-shot agents creating PRs. Artman envisions near-100% automation soon, freeing humans for design. He critiques Claude Code—Anthropic's tool, reportedly all Claude-built—as buggy despite speed, a symptom of AI arms-race shipping.",[22,4335,4336],{},"\"Over time people will pick the one that is of higher quality... it'll just happen over time. There will be no A\u002FB test,\" Artman explains.",[17,4338,4340],{"id":4339},"quality-wednesdays-cultivating-obsession","Quality Wednesdays: Cultivating Obsession",[22,4342,4343],{},"Artman's signature ritual started at an offsite: auditing one menu revealed 35 issues, from missing hover highlights (instant on, 150ms fade-out for smoothness) to regressions. The app felt fast via micro-interactions, but lapses accumulated. Team fixed 2,500-3,000 such details since. Now weekly, all 25 remote engineers share one self-found fix in 30-40 minutes—from one-pixel tweaks to backend efficiencies.",[22,4345,4346],{},"Key: Engineers hunt proactively for Wednesdays, embedding vigilance into daily work. Unrelated features get polished en route, slashing regressions. Orosz calls it aspirational; Artman urges all teams, especially with AI easing hunts.",[22,4348,4349],{},"\"If you think about quality all the time... you're bound to make less mistakes,\" Artman notes.",[17,4351,4353],{"id":4352},"zero-bug-policy-immediate-accountability","Zero Bug Policy: Immediate Accountability",[22,4355,4356],{},"Bugs accrue constantly; backlogs balloon until crisis triage matches inflow—two months late. Linear's fix: three weeks halting features to zero the queue, then enforce. Agents auto-assign by code ownership; highest priority. Fix same-day (often 2-3 hours) or triage low-impact ones. Users love rapid resolutions—email: \"Refresh, it's fixed.\"",[22,4358,4359],{},"Bugs ≠ Quality Wednesdays (proactive polish). With AI pinpointing issues, every company should adopt: constant fix rate means zero policy trades nothing for perfection.",[22,4361,4362],{},"\"There's a very small trade-off... all you need to do is stop development of new features for as long as it takes,\" Artman advises.",[17,4364,4366],{"id":4365},"ais-blind-spots-no-taste-no-feel","AI's Blind Spots: No Taste, No Feel",[22,4368,4369],{},"AI excels at code, tests, even animations—but lacks 'taste.' It generates functional UIs without perceiving time (e.g., 2s click feels slow?), spatial harmony, or emotional flow. Linear design engineer Emil's X demo: agents built pop-ups\u002Fbutton highlights competently (ease-in curves), but manual tweaks made them 'natural.' AI is timeless, screenshot\u002FDOM-bound; no frustration from lag.",[22,4371,4372],{},"Artman: Hand rote tasks (bugs) to agents; humans own UX judgment. Future tasteful AI? Possible last bastion.",[22,4374,4375],{},"\"They have no taste... they simply don't,\" Artman states bluntly.",[17,4377,110],{"id":109},[39,4379,4380,4383,4386,4389,4392,4395,4398,4401],{},[42,4381,4382],{},"Say no to 90% of requests: Group feedback, solve roots, design thoughtfully—AI summarizes, humans decide.",[42,4384,4385],{},"Implement Zero Bug Policy: Auto-assign, fix immediately (or triage); halt features briefly to zero backlog—users rave.",[42,4387,4388],{},"Run Quality Wednesdays: Mandate weekly self-found fixes, share in 30 mins—builds product-wide vigilance.",[42,4390,4391],{},"Obsess pixels and feel: Instant highlights, 150ms fades; measure what revenue misses.",[42,4393,4394],{},"Use AI for grind (10%+ bugs auto-fixed), not craft—leverage speed without sacrificing taste.",[42,4396,4397],{},"Watch competitors: Match features lose to gradual quality wins—no A\u002FB needed.",[42,4399,4400],{},"Proactive polish during features: Wednesday hunts train constant awareness.",[42,4402,4403],{},"Critique tools ruthlessly: Claude Code buggy from haste—quality signals maturity.",{"title":144,"searchDepth":145,"depth":145,"links":4405},[4406,4407,4408,4409,4410,4411],{"id":4313,"depth":145,"text":4314},{"id":4326,"depth":145,"text":4327},{"id":4339,"depth":145,"text":4340},{"id":4352,"depth":145,"text":4353},{"id":4365,"depth":145,"text":4366},{"id":109,"depth":145,"text":110},[264],{"content_references":4414,"triage":4421},[4415,4418],{"type":4416,"title":4417,"context":181},"tool","Claude Code",{"type":165,"title":4419,"author":4420,"context":181},"Emil's X post on agent animations","Emil (Linear design engineer)",{"relevance":183,"novelty":184,"quality":183,"actionability":184,"composite":185,"reasoning":4422},"Category: Product Strategy. The article discusses the balance between rapid feature shipping enabled by AI and the importance of maintaining quality, addressing a key pain point for product-minded builders. It offers insights into how Linear uses customer feedback and a Zero Bug Policy to prioritize quality, which is actionable but lacks specific frameworks or step-by-step guidance.","\u002Fsummaries\u002F046a0fd5ac47e3c7-ai-speeds-shipping-but-taste-wins-linear-cto-on-qu-summary","2026-04-21 14:00:06","2026-04-21 15:11:28",{"title":4303,"description":144},{"loc":4423},"e4902f78f5c7f317","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wjk0ulMAkbc","summaries\u002F046a0fd5ac47e3c7-ai-speeds-shipping-but-taste-wins-linear-cto-on-qu-summary",[198,4433,199,4434],"software-engineering","ai-llms","AI agents enable rapid feature shipping, risking bloat and poor UX; Linear counters with deep customer insight, Zero Bug Policy, and Quality Wednesdays to build tasteful software that outlasts competitors.",[4433,199,4434],"W-eikIGYBUPhTdn42kGhv7tod38qgXiVtfVY4vXFX_w"]