[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-aaedb2dcbeaee678-void-erases-video-objects-while-rewriting-physics-summary":3,"summaries-facets-categories":162,"summary-related-aaedb2dcbeaee678-void-erases-video-objects-while-rewriting-physics-summary":5736},{"id":4,"title":5,"ai":6,"body":13,"categories":118,"created_at":120,"date_modified":120,"description":111,"extension":121,"faq":120,"featured":122,"kicker_label":120,"meta":123,"navigation":144,"path":145,"published_at":146,"question":120,"scraped_at":147,"seo":148,"sitemap":149,"source_id":150,"source_name":151,"source_type":152,"source_url":153,"stem":154,"tags":155,"thumbnail_url":120,"tldr":159,"tweet":120,"unknown_tags":160,"__hash__":161},"summaries\u002Fsummaries\u002Faaedb2dcbeaee678-void-erases-video-objects-while-rewriting-physics-summary.md","VOID Erases Video Objects While Rewriting Physics",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6420,2132,20274,0.00232765,{"type":14,"value":15,"toc":110},"minimark",[16,21,25,28,31,34,38,41,44,48,58,81,84,88,95,101,107],[17,18,20],"h2",{"id":19},"voids-two-pass-pipeline-fixes-ghost-interactions","VOID's Two-Pass Pipeline Fixes Ghost Interactions",[22,23,24],"p",{},"Standard video inpainting tools erase objects like watermarks or static people by filling pixels from surroundings, but they ignore physics, leaving artifacts like spinning blenders or falling pins without cause. VOID counters this by reimagining a 'counterfactual reality' where the object never existed.",[22,26,27],{},"First pass: Reasoning. A vision-language model (VLM) paired with SAM 2 (Segment Anything Model 2) tracks the target pixel-perfectly and predicts causal effects—e.g., removing one domino flags affected chain reactions. This generates a 'quad mask' expanding beyond the object to map physics rewrite zones.",[22,29,30],{},"Second pass: Generation and refinement. A video diffusion model inpaints using the quad mask. To prevent morphing or dreaminess, an optional flow warp noise step locks remaining objects' shapes and consistency. Prompts focus on the desired scene without mentioning the removed object, e.g., 'fighter in dark kimono in gym' instead of referencing the erased white-kimono fighter.",[22,32,33],{},"Trade-off: Works best for simple interactions; complex dynamics like fights produce ghost-like remnants because physics simulation can't fully rewrite human behavior.",[17,35,37],{"id":36},"training-on-synthetic-physics-simulations","Training on Synthetic Physics Simulations",[22,39,40],{},"Real-world data lacks 'unhappened' events, so Netflix\u002FInsight trained VOID on synthetic environments like Kubric. Run thousands of physics sims: one with object collision (before\u002Fafter), one without. AI learns object presence → environmental impact mappings. This teaches cause-effect without filming impossibilities like 'uncrashed cars.'",[22,42,43],{},"Outcome: VOID generalizes to real videos, handling interactions better than pixel-fill alone, but requires precise segmentation and prompts for optimal masks.",[17,45,47],{"id":46},"streamlined-setup-with-custom-web-app","Streamlined Setup with Custom Web App",[22,49,50,51,57],{},"Raw GitHub repo (",[52,53,54],"a",{"href":54,"rel":55},"https:\u002F\u002Fgithub.com\u002FNetflix\u002Fvoid-model",[56],"nofollow",") has gaps: undocumented SAM 3 needs, strict 'quad_mask_0.mpp4' naming, no built-in GUI for masking. Fix by deploying on Runpod H100 GPU pod (100GB container, port 8998):",[59,60,61,70,78],"ol",{},[62,63,64,65,69],"li",{},"SSH, clone ",[52,66,67],{"href":67,"rel":68},"https:\u002F\u002Fgithub.com\u002Fandrisgauracs\u002Fnetflix-void-web-app",[56],".",[62,71,72,73,77],{},"Run ",[74,75,76],"code",{},"run.sh"," with Hugging Face token (for models), SAM 3 gated access, Gemini API key (pose estimation).",[62,79,80],{},"Access UI tabs: Segment (prompt + points for SAM 2 mask), Inference (counterfactual prompt), Results (view + optional second-pass refinement).",[22,82,83],{},"This automates workflow: upload video → mask → infer → refine. Speeds testing from hours of CLI debugging to minutes, but demands beefy GPU (H100 recommended) and API approvals.",[17,85,87],{"id":86},"test-results-strengths-in-motion-weak-in-combat","Test Results: Strengths in Motion, Weak in Combat",[22,89,90,94],{},[91,92,93],"strong",{},"Matrix fight (remove Neo):"," Morpheus punches air\u002Fghost; hand inconsistencies persist post-refinement. Fails to make opponent static—can't invent idle behavior.",[22,96,97,100],{},[91,98,99],{},"La La Land dance (remove Emma Stone):"," Near-flawless. Ryan Gosling dances solo seamlessly, even through occlusions; minor artifacts only. Best result—proves strength in rhythmic, predictable motion.",[22,102,103,106],{},[91,104,105],{},"Titanic bow (remove Jack):"," Kate stands alone convincingly, but arm artifacts and morphing face create uncanny valley. User error in segmentation left hand remnants; highlights need precise points.",[22,108,109],{},"Overall: Delivers on physics rewrite for 2\u002F3 tests, but artifacts in occlusion\u002Fcomplexity. Future: Netflix interactive narratives like Bandersnatch, user-driven edits. Use for VFX cleanup, personalized video—test your clips to gauge fit.",{"title":111,"searchDepth":112,"depth":112,"links":113},"",2,[114,115,116,117],{"id":19,"depth":112,"text":20},{"id":36,"depth":112,"text":37},{"id":46,"depth":112,"text":47},{"id":86,"depth":112,"text":87},[119],"AI & LLMs",null,"md",false,{"content_references":124,"triage":139},[125,129,133,135,137],{"type":126,"title":127,"url":54,"context":128},"tool","VOID Model","mentioned",{"type":126,"title":130,"author":131,"url":67,"context":132},"Netflix VOID Web App","andrisgauracs","recommended",{"type":126,"title":134,"context":128},"SAM 2",{"type":126,"title":136,"context":128},"Kubri",{"type":126,"title":138,"context":128},"Runpod",{"relevance":140,"novelty":141,"quality":141,"actionability":112,"composite":142,"reasoning":143},3,4,3.25,"Category: AI & LLMs. The article discusses a novel AI model, VOID, that addresses specific challenges in video inpainting, presenting new insights into its two-pass pipeline. However, while it offers interesting technical details, it lacks actionable steps for implementation, making it less practical for the target audience.",true,"\u002Fsummaries\u002Faaedb2dcbeaee678-void-erases-video-objects-while-rewriting-physics-summary","2026-04-30 00:00:06","2026-05-03 16:47:32",{"title":5,"description":111},{"loc":145},"3079cb563e1445cf","Better Stack","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1yj46x45-QI","summaries\u002Faaedb2dcbeaee678-void-erases-video-objects-while-rewriting-physics-summary",[156,157,158],"ai-tools","machine-learning","automation","Netflix's open-source VOID model uses a two-pass pipeline—reasoning with VLM + SAM 2 for quad masks, then diffusion generation—to remove objects and simulate counterfactual scenes without ghost interactions, excelling in dance but struggling with 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By simulating outcomes, the model helps scientists narrow down which strategies are most likely to yield significant results, potentially saving months of trial-and-error.",[62,5775,5776,5779],{},[91,5777,5778],{},"Predictive Validation:"," In a blind test, the model correctly predicted the enhanced cancer-killing capabilities of CD8+ T cells in a lymphoma study that had not yet been published or indexed on the internet. This suggests the model is performing genuine reasoning rather than simple retrieval.",[17,5781,5783],{"id":5782},"the-human-in-the-loop-requirement","The Human-in-the-Loop Requirement",[22,5785,5786],{},"Despite these capabilities, Unutmaz emphasizes that subject matter expertise remains non-negotiable. The AI provides the insight, but the human researcher must evaluate its biological plausibility and significance. The model acts as a force multiplier—streamlining literature reviews and data synthesis—but the scientist remains the final arbiter of the research direction. This collaborative workflow allows for the rapid generation of complex materials, such as large-scale mutation datasets and specialized textbooks, which would otherwise require significant manual labor.",{"title":111,"searchDepth":112,"depth":112,"links":5788},[5789,5790,5791],{"id":5750,"depth":112,"text":5751},{"id":5760,"depth":112,"text":5761},{"id":5782,"depth":112,"text":5783},[119],{"content_references":5794,"triage":5802},[5795,5798,5800],{"type":126,"title":5796,"publisher":5797,"context":132},"GPT-5 Pro","OpenAI",{"type":126,"title":5799,"publisher":5797,"context":128},"Codex",{"type":126,"title":5801,"publisher":5797,"context":128},"GPT-5.2 Deep Research",{"relevance":140,"novelty":140,"quality":141,"actionability":112,"composite":5803,"reasoning":5804},3.05,"Category: AI & LLMs. The article discusses the application of LLMs in scientific research, which aligns with the AI & LLMs category. It presents a specific case of using AI for hypothesis testing, but lacks detailed actionable steps for the audience to implement similar strategies in their own work.","\u002Fsummaries\u002F072c0c3973379946-accelerating-scientific-discovery-with-llm-driven-summary","2026-06-18 15:00:00","2026-06-24 12:56:44",{"title":5739,"description":111},{"loc":5805},"072c0c3973379946","OpenAI News","https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-5-immunology-mystery","summaries\u002F072c0c3973379946-accelerating-scientific-discovery-with-llm-driven-summary",[156,5815,157,158],"research","Immunologist Derya Unutmaz demonstrates how LLMs act as research collaborators by identifying hidden biological mechanisms in experimental data and simulating outcomes to prioritize high-value lab work.",[],"u5UH9ku4qUJnLmHJUZvkFFYG2VBXIRH15EPUas0MIfU",{"id":5820,"title":5821,"ai":5822,"body":5827,"categories":5894,"created_at":120,"date_modified":120,"description":111,"extension":121,"faq":120,"featured":122,"kicker_label":120,"meta":5895,"navigation":144,"path":5908,"published_at":5909,"question":120,"scraped_at":5910,"seo":5911,"sitemap":5912,"source_id":5913,"source_name":5914,"source_type":5915,"source_url":5916,"stem":5917,"tags":5918,"thumbnail_url":5920,"tldr":5921,"tweet":5922,"unknown_tags":5923,"__hash__":5924},"summaries\u002Fsummaries\u002F1a6b27682bc5657a-optimizing-video-diffusion-for-real-time-generatio-summary.md","Optimizing Video Diffusion for Real-Time Generation",{"provider":7,"model":5741,"input_tokens":5823,"output_tokens":5824,"processing_time_ms":5825,"cost_usd":5826},7266,665,4888,0.002814,{"type":14,"value":5828,"toc":5889},[5829,5833,5836,5840,5878,5882],[17,5830,5832],{"id":5831},"the-path-to-real-time-diffusion","The Path to Real-Time Diffusion",[22,5834,5835],{},"Standard diffusion models typically require 20 to 50 denoising steps, creating significant latency that hinders real-time applications like robotics or interactive content. Achieving real-time performance requires an additive approach, stacking three primary optimization techniques: quantization, caching, and distillation.",[17,5837,5839],{"id":5838},"three-pillars-of-optimization","Three Pillars of Optimization",[5766,5841,5842,5852,5858],{},[62,5843,5844,5847,5848,5851],{},[91,5845,5846],{},"Quantization:"," This is the lowest-hanging fruit. While diffusion models are attention-heavy and less sensitive to quantization than LLMs, dynamic quantization (computing ranges on the fly) effectively reduces memory footprint and improves throughput. NVIDIA’s ",[74,5849,5850],{},"TRTLM"," repository provides pre-quantized checkpoints to simplify deployment.",[62,5853,5854,5857],{},[91,5855,5856],{},"Caching:"," By identifying redundant computations between denoising steps, caching skips re-processing latent chunks that remain static. Modern approaches use chunk-based caching, which isolates dynamic elements (like a moving speaker) from static backgrounds, significantly reducing redundant GPU cycles.",[62,5859,5860,5863,5864],{},[91,5861,5862],{},"Step Distillation:"," The most impactful technique, distillation trains a 'student' model to match a 'teacher' model's output in significantly fewer steps (e.g., 4, 8, or even 1).\n",[5766,5865,5866,5872],{},[62,5867,5868,5871],{},[91,5869,5870],{},"Trajectory-based:"," The student learns to mimic the teacher's exact denoising path.",[62,5873,5874,5877],{},[91,5875,5876],{},"Distribution-based:"," The student only learns to land on the same final output. This is currently the preferred, higher-quality method.",[17,5879,5881],{"id":5880},"implementation-strategy","Implementation Strategy",[22,5883,5884,5885,5888],{},"NVIDIA’s ",[74,5886,5887],{},"FastGen"," repository provides the necessary infrastructure to handle the complexity of sharding large models (20B–40B+ parameters) across multiple GPUs. The process is incremental: start with quantization to see if performance meets requirements, then layer in caching, and finally apply distillation for the most significant speedups. While distillation is a post-training technique requiring specific data and compute, it does not require the massive resources needed for initial pre-training, making it accessible on standard enterprise hardware like H100s or B200s.",{"title":111,"searchDepth":112,"depth":112,"links":5890},[5891,5892,5893],{"id":5831,"depth":112,"text":5832},{"id":5838,"depth":112,"text":5839},{"id":5880,"depth":112,"text":5881},[119],{"content_references":5896,"triage":5904},[5897,5900],{"type":126,"title":5887,"author":5898,"url":5899,"context":132},"NVIDIA Research","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FFastGen",{"type":126,"title":5901,"author":5902,"url":5903,"context":132},"TRTLM (TensorRT-LLM)","NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM",{"relevance":5905,"novelty":141,"quality":141,"actionability":141,"composite":5906,"reasoning":5907},5,4.35,"Category: AI & LLMs. The article provides a detailed approach to optimizing video diffusion models for real-time generation, addressing a specific pain point of latency in AI applications. It outlines practical techniques like quantization, caching, and step distillation, making it actionable for developers looking to implement these optimizations.","\u002Fsummaries\u002F1a6b27682bc5657a-optimizing-video-diffusion-for-real-time-generatio-summary","2026-06-16 13:00:06","2026-06-17 12:56:17",{"title":5821,"description":111},{"loc":5908},"1a6b27682bc5657a","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gHs5ZiY80PM","summaries\u002F1a6b27682bc5657a-optimizing-video-diffusion-for-real-time-generatio-summary",[5919,156,158,157],"llm","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FgHs5ZiY80PM\u002Fhqdefault.jpg","Achieve real-time video generation by stacking quantization, caching, and step distillation to reduce the standard 50-step denoising process to as few as 1-8 steps.","This presentation outlines three technical strategies for reducing the latency of diffusion models: dynamic quantization, latent caching, and step distillation. The speaker explains how these methods can be combined to achieve real-time generation, with implementation details and tools available in the [FastGen](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM) repository.",[],"c1TOl0LiL8H8OotgeIMeRvn9YfVIGnuc9eEFEeoKjmg",{"id":5926,"title":5927,"ai":5928,"body":5933,"categories":5976,"created_at":120,"date_modified":120,"description":111,"extension":121,"faq":120,"featured":122,"kicker_label":120,"meta":5977,"navigation":144,"path":5988,"published_at":5989,"question":120,"scraped_at":5989,"seo":5990,"sitemap":5991,"source_id":5992,"source_name":5993,"source_type":152,"source_url":5983,"stem":5994,"tags":5995,"thumbnail_url":120,"tldr":5996,"tweet":120,"unknown_tags":5997,"__hash__":5998},"summaries\u002Fsummaries\u002Fd5c54c878ef99cc8-sim2schedule-simulator-guided-llm-framework-for-mi-summary.md","Sim2Schedule: Simulator-Guided LLM Framework for Mine Scheduling",{"provider":7,"model":5741,"input_tokens":5929,"output_tokens":5930,"processing_time_ms":5931,"cost_usd":5932},4057,538,3287,0.00182125,{"type":14,"value":5934,"toc":5972},[5935,5939,5942,5946,5949,5969],[17,5936,5938],{"id":5937},"the-challenge-of-autonomous-mine-scheduling","The Challenge of Autonomous Mine Scheduling",[22,5940,5941],{},"Open-pit mine scheduling is a high-stakes, multi-objective optimization problem that requires balancing production targets, equipment constraints, and geological variability. Traditional mathematical optimization methods often struggle with the dynamic, non-linear nature of these environments, while pure LLM-based approaches lack the domain-specific grounding required to ensure safety and operational feasibility. Sim2Schedule bridges this gap by integrating Large Language Models (LLMs) with specialized simulation environments to create a closed-loop planning system.",[17,5943,5945],{"id":5944},"the-sim2schedule-framework","The Sim2Schedule Framework",[22,5947,5948],{},"Sim2Schedule functions as an iterative, simulator-guided agent. Instead of relying on a single-shot prompt to generate a schedule, the framework employs a multi-step process:",[59,5950,5951,5957,5963],{},[62,5952,5953,5956],{},[91,5954,5955],{},"Reasoning & Planning",": The LLM acts as the central planner, translating high-level production goals into actionable scheduling sequences.",[62,5958,5959,5962],{},[91,5960,5961],{},"Simulator Feedback Loop",": The generated schedule is executed within a domain-specific simulator. This simulator acts as a 'reality check,' identifying violations of operational constraints (e.g., equipment capacity, slope stability, or material flow bottlenecks).",[62,5964,5965,5968],{},[91,5966,5967],{},"Iterative Refinement",": The simulator provides structured feedback to the LLM, detailing the specific failures or inefficiencies found in the current plan. The LLM then uses this feedback to adjust its strategy, iteratively improving the schedule until it meets performance criteria.",[22,5970,5971],{},"This approach effectively treats the simulator as a tool for verification, allowing the LLM to leverage its reasoning capabilities for complex decision-making while offloading the heavy lifting of constraint validation to a reliable, deterministic system.",{"title":111,"searchDepth":112,"depth":112,"links":5973},[5974,5975],{"id":5937,"depth":112,"text":5938},{"id":5944,"depth":112,"text":5945},[119],{"content_references":5978,"triage":5985},[5979],{"type":5980,"title":5981,"author":5982,"url":5983,"context":5984},"paper","Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling","Not specified","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.10286","reviewed",{"relevance":5905,"novelty":141,"quality":141,"actionability":140,"composite":5986,"reasoning":5987},4.15,"Category: AI & LLMs. The article presents a novel framework that integrates LLMs with domain-specific simulators for mine scheduling, addressing a specific pain point in the optimization of complex environments. It provides a detailed description of the iterative process, but lacks specific actionable steps for implementation.","\u002Fsummaries\u002Fd5c54c878ef99cc8-sim2schedule-simulator-guided-llm-framework-for-mi-summary","2026-06-10 12:57:08",{"title":5927,"description":111},{"loc":5988},"d5c54c878ef99cc8","arXiv cs.AI","summaries\u002Fd5c54c878ef99cc8-sim2schedule-simulator-guided-llm-framework-for-mi-summary",[5919,156,157,158],"Sim2Schedule addresses the complexity of open-pit mine scheduling by combining LLM reasoning with domain-specific simulators to iteratively refine production plans.",[],"SHbvAdHQgzicIPZrrxTErWZcrjbybOu2rnkGQc94IlM",{"id":6000,"title":6001,"ai":6002,"body":6007,"categories":6039,"created_at":120,"date_modified":120,"description":111,"extension":121,"faq":120,"featured":122,"kicker_label":120,"meta":6040,"navigation":144,"path":6052,"published_at":6053,"question":120,"scraped_at":6054,"seo":6055,"sitemap":6056,"source_id":6057,"source_name":6058,"source_type":5915,"source_url":6059,"stem":6060,"tags":6061,"thumbnail_url":6063,"tldr":6064,"tweet":6065,"unknown_tags":6066,"__hash__":6067},"summaries\u002Fsummaries\u002F2565fa4752d4d84f-real-time-fraud-detection-with-alloydb-ai-summary.md","Real-Time Fraud Detection with AlloyDB AI",{"provider":7,"model":5741,"input_tokens":6003,"output_tokens":6004,"processing_time_ms":6005,"cost_usd":6006},4890,661,3047,0.002214,{"type":14,"value":6008,"toc":6034},[6009,6013,6016,6020,6027,6031],[17,6010,6012],{"id":6011},"scaling-vector-search-for-high-velocity-streams","Scaling Vector Search for High-Velocity Streams",[22,6014,6015],{},"Traditional vector search implementations often struggle with the memory and latency requirements of high-frequency financial data. AlloyDB addresses this by utilizing ScaNN (Scalable Nearest Neighbors), a vector index algorithm that requires 4x less memory than HNSW. This efficiency allows for scaling to over 10 billion vectors, making it suitable for processing millions of transactions in real-time. By generating embeddings from textual representations of transaction data, organizations can identify anomalies by calculating the vector distance between incoming transactions and known fraudulent patterns.",[17,6017,6019],{"id":6018},"hybrid-reasoning-to-resolve-false-positives-and-negatives","Hybrid Reasoning to Resolve False Positives and Negatives",[22,6021,6022,6023,6026],{},"Fraud detection inherently involves a trade-off between sensitivity (recall) and specificity (false positives). Relying solely on vector distance thresholds often forces a compromise: stricter thresholds catch more fraud but increase false positives, while looser thresholds increase false negatives. AlloyDB AI solves this by introducing a hybrid approach using the ",[74,6024,6025],{},"ai.if()"," function. This allows developers to trigger Gemini’s natural language reasoning for transactions that fall into the \"gray area\" near the threshold. By asking the LLM to evaluate specific context—such as whether a transaction matches a user's typical spending patterns—the system can make nuanced decisions that boost recall and reduce false negatives by over 5%.",[17,6028,6030],{"id":6029},"optimizing-inference-for-production-performance","Optimizing Inference for Production Performance",[22,6032,6033],{},"Integrating LLMs into database workflows typically risks significant latency. AlloyDB optimizes this by moving away from traditional row-by-row calls, which are inefficient for high-volume streams. Through array-based processing and native in-database execution, the system achieves throughput of 100,000 rows per second. This represents a 23,000x improvement over standard row-at-a-time processing. These architectural optimizations not only ensure real-time performance but also drive down costs by approximately 6,000x, bringing the cost of intelligent inference to roughly 1\u002F10th of a cent per transaction.",{"title":111,"searchDepth":112,"depth":112,"links":6035},[6036,6037,6038],{"id":6011,"depth":112,"text":6012},{"id":6018,"depth":112,"text":6019},{"id":6029,"depth":112,"text":6030},[119],{"content_references":6041,"triage":6050},[6042,6045,6048],{"type":126,"title":6043,"url":6044,"context":132},"AlloyDB for PostgreSQL","https:\u002F\u002Fgoo.gle\u002F4d3cuSe",{"type":126,"title":6046,"context":6047},"ScaNN","cited",{"type":126,"title":6049,"context":6047},"Gemini",{"relevance":5905,"novelty":141,"quality":141,"actionability":141,"composite":5906,"reasoning":6051},"Category: AI & LLMs. The article provides a deep dive into AlloyDB AI's capabilities for real-time fraud detection, addressing specific pain points such as processing efficiency and false positive reduction. It offers actionable insights on implementing hybrid reasoning with LLMs, which is directly applicable for developers looking to enhance their AI-powered products.","\u002Fsummaries\u002F2565fa4752d4d84f-real-time-fraud-detection-with-alloydb-ai-summary","2026-05-18 16:00:04","2026-05-18 19:00:21",{"title":6001,"description":111},{"loc":6052},"2565fa4752d4d84f","Google Cloud Tech","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=aYCuGy97Nf4","summaries\u002F2565fa4752d4d84f-real-time-fraud-detection-with-alloydb-ai-summary",[156,157,6062,158],"saas","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FaYCuGy97Nf4\u002Fhqdefault.jpg","AlloyDB AI enables high-velocity fraud detection by combining ScaNN vector indexing with Gemini's natural language reasoning, achieving 100,000 rows per second processing and significant cost reductions.","This demo shows how to use [AlloyDB for PostgreSQL](https:\u002F\u002Fgoo.gle\u002F4d3cuSe) to perform real-time fraud detection by combining vector search with ScaNN indexing and LLM-based reasoning. The speaker walks through a [provided demo](https:\u002F\u002Fgoo.gle\u002F4cPvQvf) that uses in-database AI functions to reduce false negatives without the latency typically associated with external model calls.",[],"pgJuXM-PU_7lTrFkscgkMyqNgaDBQtxe0Tuno4wfOOc"]