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No company lacks data quality issues. AI operationalizes these flaws: messy lending data leads to approving bad loans, duplicative sales data misprioritizes customers, and broken metrics optimize flawed processes. Trusting confident but wrong AI outputs industrializes bad decisions that stayed contained in traditional reports and dashboards.",[17,3677,3679],{"id":3678},"ais-semantic-processing-exposes-data-costs","AI's Semantic Processing Exposes Data Costs",[22,3681,3682],{},"Unlike cheap, deterministic SQL queries scanning 10,000 rows in milliseconds with near-zero marginal cost, AI uses GPU-heavy semantic search: it embeds data into vectors, performs matrix multiplications for inference, and synthesizes proactive insights like spotting outliers, seasonal spikes, or correlations without explicit queries. This makes AI 10x more energy-intensive per query, billing for cognition—tokens processed, context maintained, reasoning performed—scaling like tireless labor. Dirty data forces repeated heavy processing in evolving conversations, shifting economics from FinOps-style cost reduction (store, query, pay per run) to usage → output → value, where data quality determines real returns.",[17,3684,3686],{"id":3685},"reframe-ai-management-around-data-not-symptoms","Reframe AI Management Around Data, Not Symptoms",[22,3688,3689],{},"Fears of AI costs, skills gaps, and security mask root data problems; rising costs signal inefficient processing of messy data, variable outputs reveal inaccuracies, and slowed adoption ignores symptoms. Traditional IT models (cost → efficiency → reduction) fail for probabilistic, consumption-based AI fueled by imperfect data. Leaders must prioritize data cleaning to avoid AI confidently recommending actions like shutting profitable lines based on flawed inputs. AI acts as an unavoidable mirror: fix data to capture its value, or scale mistakes.",{"title":45,"searchDepth":46,"depth":46,"links":3691},[3692,3693,3694],{"id":3671,"depth":46,"text":3672},{"id":3678,"depth":46,"text":3679},{"id":3685,"depth":46,"text":3686},[],{"content_references":3697,"triage":3698},[],{"relevance":3699,"novelty":3700,"quality":3699,"actionability":3700,"composite":3701,"reasoning":3702},4,3,3.6,"Category: Data Science & Visualization. The article discusses the critical importance of data quality in AI implementations, addressing a specific pain point for product builders who need to ensure their data is clean before deploying AI solutions. It provides insights into the consequences of poor data but lacks detailed actionable steps for improving data quality.","\u002Fsummaries\u002F6c1cdcac335f19f8-ai-amplifies-bad-data-fix-it-first-summary","2026-04-20 16:23:11","2026-04-21 15:26:26",{"title":3661,"description":45},{"loc":3703},"6c1cdcac335f19f8","Data Driven Investor","https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fdont-be-afraid-of-ai-be-terrified-of-your-data-97569858b42f?source=rss----32881626c9c9---4","summaries\u002F6c1cdcac335f19f8-ai-amplifies-bad-data-fix-it-first-summary",[67,69],"AI doesn't fix poor data quality; it scales the errors, leading to wrong decisions like approving bad loans or prioritizing wrong customers. 85% of AI failures stem from bad data, so clean data before adopting AI.",[69],"pXNZvNlaf4m9AWQ5hD6eh2a7NtGiLwpEY1JU9QQSt6c",{"id":3717,"title":3718,"ai":3719,"body":3724,"categories":3752,"created_at":52,"date_modified":52,"description":45,"extension":53,"faq":52,"featured":54,"kicker_label":52,"meta":3753,"navigation":56,"path":3767,"published_at":3768,"question":52,"scraped_at":3769,"seo":3770,"sitemap":3771,"source_id":3772,"source_name":3773,"source_type":63,"source_url":3774,"stem":3775,"tags":3776,"thumbnail_url":52,"tldr":3779,"tweet":52,"unknown_tags":3780,"__hash__":3781},"summaries\u002Fsummaries\u002Fa3ec373360dad952-scale-genai-to-billions-of-rows-in-bigquery-at-94--summary.md","Scale GenAI to Billions of Rows in BigQuery at 94% Less Cost",{"provider":7,"model":8,"input_tokens":3720,"output_tokens":3721,"processing_time_ms":3722,"cost_usd":3723},4687,1619,26747,0.0017244,{"type":14,"value":3725,"toc":3747},[3726,3730,3733,3737,3740,3744],[17,3727,3729],{"id":3728},"replace-per-row-llm-calls-with-distilled-models-for-massive-savings","Replace Per-Row LLM Calls with Distilled Models for Massive Savings",[22,3731,3732],{},"Standard BigQuery AI functions like AI.CLASSIFY and AI.IF send every row to an LLM, burning through tokens and time on datasets with millions of rows—e.g., product reviews, claims, or support tickets. Optimized mode fixes this by automatically distilling a task-specific lightweight model: BigQuery samples your data, sends only that subset to the LLM for labeling, generates embeddings, and trains the distilled model locally on BigQuery compute. This model then processes the remaining rows using semantic embeddings for LLM-quality classification, filtering, or rating without full LLM inference per row. Result: process billions of rows at BigQuery speeds with drastically reduced latency and costs, as savings compound with data volume.",[17,3734,3736],{"id":3735},"trigger-optimization-automatically-or-with-one-parameter","Trigger Optimization Automatically or with One Parameter",[22,3738,3739],{},"No code rewrites needed—optimized mode activates for supported functions when you supply embeddings as a parameter (e.g., add embeddings column to AI.CLASSIFY) or if BigQuery's autonomous embeddings exist in the table. It auto-detects them, samples data, distills, and optimizes inline. For image analysis on 34k self-driving car camera shots, adding embeddings dropped tokens from 55M+ to 3M (94% reduction) and runtime from 16min to 2min, with  vast majority of rows processed by the distilled model. On 50k driver voice commands using AI.IF to filter 'slow down' requests, auto-detection optimized most rows without changes, delivering filtered results fast and cheap.",[17,3741,3743],{"id":3742},"trade-offs-and-when-to-use","Trade-offs and When to Use",[22,3745,3746],{},"Distillation trades full LLM flexibility for speed\u002Fcost on repetitive tasks like classification—ideal for large-scale filtering where you don't need per-row creativity. Quality matches LLM on samples and generalizes via embeddings; check job info tab post-query for optimization stats (e.g., % rows optimized). Start by adding embeddings to existing AI queries; scales best on growing datasets where per-row LLM becomes prohibitive.",{"title":45,"searchDepth":46,"depth":46,"links":3748},[3749,3750,3751],{"id":3728,"depth":46,"text":3729},{"id":3735,"depth":46,"text":3736},{"id":3742,"depth":46,"text":3743},[82],{"content_references":3754,"triage":3763},[3755,3760],{"type":3756,"title":3757,"url":3758,"context":3759},"other","Documentation for Optimized Mode","https:\u002F\u002Fgoo.gle\u002Foptimize-ai-functions","recommended",{"type":3756,"title":3761,"url":3762,"context":3759},"Generative AI in BigQuery overview","https:\u002F\u002Fgoo.gle\u002Fbq-genai-overview",{"relevance":3764,"novelty":3699,"quality":3699,"actionability":3699,"composite":3765,"reasoning":3766},5,4.35,"Category: AI & LLMs. The article provides a detailed explanation of how to optimize LLM usage in BigQuery, addressing a specific pain point of cost and efficiency for AI-powered product builders. It offers actionable steps for implementing distilled models, making it highly relevant and practical.","\u002Fsummaries\u002Fa3ec373360dad952-scale-genai-to-billions-of-rows-in-bigquery-at-94-summary","2026-05-04 17:53:30","2026-05-05 16:07:55",{"title":3718,"description":45},{"loc":3767},"9a60decd09d8b7c9","Google Cloud Tech","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=-QLXKr94X6Q","summaries\u002Fa3ec373360dad952-scale-genai-to-billions-of-rows-in-bigquery-at-94--summary",[67,69,3777,3778],"devops-cloud","embeddings","BigQuery's optimized mode distills LLMs into lightweight models using embeddings, slashing token use by 94% (55M to 3M) and query time from 16min to 2min on 34k images or 50k voice commands, scaling to billions of rows.",[69,3777,3778],"HkxLBPyyl4DNze72ncpNs3IhUyMxhPDK2Q9X7o58TRA",{"id":3783,"title":3784,"ai":3785,"body":3790,"categories":3818,"created_at":52,"date_modified":52,"description":45,"extension":53,"faq":52,"featured":54,"kicker_label":52,"meta":3819,"navigation":56,"path":3843,"published_at":3844,"question":52,"scraped_at":3845,"seo":3846,"sitemap":3847,"source_id":3848,"source_name":62,"source_type":63,"source_url":3849,"stem":3850,"tags":3851,"thumbnail_url":52,"tldr":3854,"tweet":52,"unknown_tags":3855,"__hash__":3856},"summaries\u002Fsummaries\u002Ffc664a403f73d829-data-and-beyond-grows-to-49k-views-ai-topics-domin-summary.md","Data And Beyond Grows to 49K Views, AI Topics Dominate",{"provider":7,"model":8,"input_tokens":3786,"output_tokens":3787,"processing_time_ms":3788,"cost_usd":3789},4692,2373,19462,0.00161805,{"type":14,"value":3791,"toc":3813},[3792,3796,3799,3803,3806,3810],[17,3793,3795],{"id":3794},"steady-growth-signals-strong-data-content-demand","Steady Growth Signals Strong Data Content Demand",[22,3797,3798],{},"The Data And Beyond publication hit 49,000 total views in April 2026, with 14,800 full reads, driving follower count from 1,950 to 2,040 (+90). These metrics show consistent traction for practical data science and AI content on Medium, where reader engagement directly boosts visibility—full reads matter more than raw views for algorithm favor.",[17,3800,3802],{"id":3801},"ai-leaks-and-comparisons-outpace-traditional-tools","AI Leaks and Comparisons Outpace Traditional Tools",[22,3804,3805],{},"Top read (#1): Satyam Sahu's \"RAG vs MCP\" unpacks real-world trade-offs for AI developers building retrieval systems, emphasizing what beats standard RAG in production. Claude-focused stories dominated: Hareem Fatima's #4 on the 2026 Claude Code Leak exposes Anthropic's detailed AI architecture blueprint accidentally revealed, and #3 details \"Claude Mythos,\" an unreleased model too risky to ship. These reveal insider AI risks and architectures, pulling more reads than pure data tools.",[17,3807,3809],{"id":3808},"clustering-and-spark-optimization-as-core-data-wins","Clustering and Spark Optimization as Core Data Wins",[22,3811,3812],{},"Dima Iakubovskyi's #2 warns \"You Are Probably Using the Wrong Clustering Algorithm,\" arguing most default to suboptimal methods—switching yields better results on real datasets. #5 by Sriw World of Coding details spark-submit flags for Spark resource allocation, cutting costs and speeding jobs via precise executor\u002Fmemory tuning. Together, top 5 prove readers prioritize actionable fixes in pipelines and ML over hype.",{"title":45,"searchDepth":46,"depth":46,"links":3814},[3815,3816,3817],{"id":3794,"depth":46,"text":3795},{"id":3801,"depth":46,"text":3802},{"id":3808,"depth":46,"text":3809},[126],{"content_references":3820,"triage":3840},[3821,3825,3829,3832,3836],{"type":3756,"title":3822,"author":3823,"url":3824,"context":3759},"Optimizing Spark Resource Allocation with spark-submit","Sriw World of Coding","https:\u002F\u002Fmedium.com\u002Fp\u002Fc17b4a49f152",{"type":3756,"title":3826,"author":3827,"url":3828,"context":3759},"The Claude Code Leak of 2026: Anthropic Accidentally Gave the World Its Most Detailed AI Architecture Blueprint","Hareem Fatima 👻","https:\u002F\u002Fmedium.com\u002Fp\u002Fdb5adcebbe69",{"type":3756,"title":3830,"author":3827,"url":3831,"context":3759},"Claude Mythos: The AI Anthropic Built and Is Too Scared to Release","https:\u002F\u002Fmedium.com\u002Fp\u002F9fc43851dfb4",{"type":3756,"title":3833,"author":3834,"url":3835,"context":3759},"You Are Probably Using the Wrong Clustering Algorithm","Dima Iakubovskyi","https:\u002F\u002Fmedium.com\u002Fp\u002F338512ee2cc6",{"type":3756,"title":3837,"author":3838,"url":3839,"context":3759},"RAG vs MCP: What Every AI Developer Actually Needs to Know","Satyam Sahu","https:\u002F\u002Fmedium.com\u002Fp\u002F3d8da413e61c",{"relevance":3700,"novelty":3700,"quality":3699,"actionability":3700,"composite":3841,"reasoning":3842},3.25,"Category: Data Science & Visualization. The article discusses practical data science topics and AI content that resonate with the audience's interests, such as RAG vs MCP for AI developers. While it provides some insights into popular articles, it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002Ffc664a403f73d829-data-and-beyond-grows-to-49k-views-ai-topics-domin-summary","2026-05-01 17:37:16","2026-05-03 17:01:18",{"title":3784,"description":45},{"loc":3843},"fc664a403f73d829","https:\u002F\u002Fmedium.com\u002Fdata-and-beyond\u002Fdata-and-beyond-monthly-newsletter-issue-10-d26d0c80b067?source=rss----b680b860beb1---4","summaries\u002Ffc664a403f73d829-data-and-beyond-grows-to-49k-views-ai-topics-domin-summary",[67,68,3852,3853],"content-marketing","llm","April 2026 stats: 49K views, 14.8K reads, +90 followers to 2K. Top stories cover Spark optimization, Claude AI leaks, clustering pitfalls, and RAG vs MCP.",[],"kIG9ilpoH7jOop2DTcJkGk4OscMqlaL3DDESCc35Tbw",{"id":3858,"title":3859,"ai":3860,"body":3865,"categories":3919,"created_at":52,"date_modified":52,"description":45,"extension":53,"faq":52,"featured":54,"kicker_label":52,"meta":3920,"navigation":56,"path":3929,"published_at":3930,"question":52,"scraped_at":3931,"seo":3932,"sitemap":3933,"source_id":3934,"source_name":62,"source_type":63,"source_url":3935,"stem":3936,"tags":3937,"thumbnail_url":52,"tldr":3939,"tweet":52,"unknown_tags":3940,"__hash__":3941},"summaries\u002Fsummaries\u002F0496e80967f34739-data-and-beyond-doubles-followers-to-2k-in-10-mont-summary.md","Data And Beyond Doubles Followers to 2K in 10 Months",{"provider":7,"model":8,"input_tokens":3861,"output_tokens":3862,"processing_time_ms":3863,"cost_usd":3864},5757,2018,18947,0.00165355,{"type":14,"value":3866,"toc":3914},[3867,3871,3874,3878,3881,3904,3907,3911],[17,3868,3870],{"id":3869},"explosive-growth-via-high-engagement-content","Explosive Growth via High-Engagement Content",[22,3872,3873],{},"The 'Data And Beyond' Medium publication doubled its followers from 1,000 (milestone hit previously) to 2,000 in about 10 months. Monthly views and reads continue rising, with March 2026 stats showing sustained traction from reader and author contributions. Growth stems from curiosity-driven content on data science, AI\u002FML tools, and practical implementations, proving consistent quality posts build audiences faster than sporadic publishing.",[17,3875,3877],{"id":3876},"top-content-drives-reads-ai-agents-and-ml-tutorials-dominate","Top Content Drives Reads: AI Agents and ML Tutorials Dominate",[22,3879,3880],{},"The 20 all-time most-read posts reveal reader demand for hands-on guides over theory:",[3882,3883,3884,3892,3898],"ul",{},[3885,3886,3887,3891],"li",{},[3888,3889,3890],"strong",{},"AI Agents & Automation (top theme, 7\u002F20 posts)",": Browser-Use (open-source web agent), Claude Cowork (Anthropic desktop agent), MCP Servers\u002FProtocol guides, DeepSeek OCR for scaling, n8n intro for workflows, PrivateGPT on Windows. These deliver setup\u002Frun instructions for production-ready tools, explaining clicks\u002Freads\u002Fautomation and billion-dollar AI challenges solved quietly.",[3885,3893,3894,3897],{},[3888,3895,3896],{},"ML\u002FData Engineering Tutorials (core appeal)",": #1 Vector Databases beginner's guide (Pavan Belagatti); #2 BERT from scratch in PyTorch (CheeKean); #3 EDA mastery (Sze Zhong LIM); Optuna hyperparameter tuning (Tushar Aggarwal); PySpark 'when' statement and ORC format (Pratik Barjatiya). Readers favor step-by-step builds, from sentiment analysis with ChatGPT\u002FPython to outlier detection via R's Tukey boxplots.",[3885,3899,3900,3903],{},[3888,3901,3902],{},"Niche Insights",": Salary trends in AI\u002FML 2025 (largest increases unspecified), Airbnb data digging (reviews\u002Fsentiments\u002Fpricing), Gemini LaTeX for math over Word, structured Data RAG beyond basic RAG.",[22,3905,3906],{},"TONI RAMCHANDANI authored 6 top-20 hits, emphasizing agent\u002Ftool deep dives. This mix—40% AI agents, 30% ML builds, 20% data tools—shows practical, code-inclusive posts (Python, R, PySpark) outperform general overviews, sustaining 2x growth.",[17,3908,3910],{"id":3909},"reader-impact-and-next-steps","Reader Impact and Next Steps",[22,3912,3913],{},"Author credits community dedication for success, urging comments\u002FLinkedIn\u002FBlueSky engagement. Lesson: Curate contributor content around proven hits (agents > theory) to scale publications without paid promo.",{"title":45,"searchDepth":46,"depth":46,"links":3915},[3916,3917,3918],{"id":3869,"depth":46,"text":3870},{"id":3876,"depth":46,"text":3877},{"id":3909,"depth":46,"text":3910},[140],{"content_references":3921,"triage":3926},[3922],{"type":3756,"title":3923,"url":3924,"context":3925},"Data And Beyond now reached 1,000 followers","https:\u002F\u002Fmedium.com\u002Fdata-and-beyond\u002Fdata-and-beyond-now-reached-1-000-followers-e01df6cdbd19","mentioned",{"relevance":3764,"novelty":3700,"quality":3699,"actionability":3699,"composite":3927,"reasoning":3928},4.15,"Category: Marketing & Growth. The article provides actionable insights on how a publication effectively grew its audience through practical content on AI and data science, addressing the audience's need for growth strategies. It highlights specific content types that drove engagement, which can inform similar strategies for product builders.","\u002Fsummaries\u002F0496e80967f34739-data-and-beyond-doubles-followers-to-2k-in-10-mont-summary","2026-04-18 15:12:30","2026-04-19 01:22:23",{"title":3859,"description":45},{"loc":3929},"0496e80967f34739","https:\u002F\u002Fmedium.com\u002Fdata-and-beyond\u002Fdata-and-beyond-now-reached-2-000-followers-d3f658d1c5b3?source=rss----b680b860beb1---4","summaries\u002F0496e80967f34739-data-and-beyond-doubles-followers-to-2k-in-10-mont-summary",[3852,3938,67,69],"growth","Medium data\u002FAI publication grew from 1,000 to 2,000 followers in ~10 months, fueled by practical guides on AI agents, ML models, data tools, and analysis techniques—top post on vector databases.",[69],"vgsVu1hYlqUvHk9CjAx5dcx057N-d9KVTr7Lr4swcyM"]