[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-58aa82efe57a452b-vector-search-explained-from-brute-force-to-ann-summary":3,"summaries-facets-categories":125,"summary-related-58aa82efe57a452b-vector-search-explained-from-brute-force-to-ann-summary":5699},{"id":4,"title":5,"ai":6,"body":13,"categories":89,"created_at":91,"date_modified":91,"description":83,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":94,"navigation":107,"path":108,"published_at":109,"question":91,"scraped_at":110,"seo":111,"sitemap":112,"source_id":113,"source_name":114,"source_type":115,"source_url":116,"stem":117,"tags":118,"thumbnail_url":91,"tldr":122,"tweet":91,"unknown_tags":123,"__hash__":124},"summaries\u002Fsummaries\u002F58aa82efe57a452b-vector-search-explained-from-brute-force-to-ann-summary.md","Vector Search Explained: From Brute Force to ANN",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",4838,590,3656,0.0020945,{"type":14,"value":15,"toc":82},"minimark",[16,21,25,29,32,55,59,62],[17,18,20],"h2",{"id":19},"the-scaling-problem-brute-force-vs-indexing","The Scaling Problem: Brute Force vs. Indexing",[22,23,24],"p",{},"Vector search involves finding the nearest neighbors to a query vector within a massive dataset. A 'brute-force' approach—a linear scan—compares the query against every single item in the database. While this guarantees the most accurate result, it is computationally prohibitive at scale (e.g., millions of vectors). For small datasets (a few thousand vectors), brute force is sufficient, but for production-scale applications, it creates a performance bottleneck.",[17,26,28],{"id":27},"the-aisle-strategy-approximate-nearest-neighbor-ann","The 'Aisle' Strategy: Approximate Nearest Neighbor (ANN)",[22,30,31],{},"To achieve speed, systems use Approximate Nearest Neighbor (ANN) search, which mimics a supermarket's organization. Instead of searching the entire store, the system uses an indexing method to create 'aisles' (clusters).",[33,34,35,43,49],"ul",{},[36,37,38,42],"li",{},[39,40,41],"strong",{},"Clustering",": Similar vectors naturally clump together. The system draws boundaries around these clumps and assigns a 'signpost' (centroid) representing the average position of the items within that cluster.",[36,44,45,48],{},[39,46,47],{},"The Two-Step Search",": When a query arrives, the system compares it only against the signposts. It then selects the nearest aisle(s) and performs a scan only within that subset.",[36,50,51,54],{},[39,52,53],{},"The Trade-off",": This method is 'approximate' because a relevant item might be miscategorized or missed if the query is directed to the wrong aisle. This trade-off between speed and perfect accuracy is fundamental to high-dimensional search.",[17,56,58],{"id":57},"libraries-vs-databases","Libraries vs. Databases",[22,60,61],{},"There is a critical distinction between a search library like FAISS and a full-featured vector database. A library provides the shelving method (e.g., IVF index), but a production-ready database adds the operational infrastructure required for real-world applications:",[33,63,64,70,76],{},[36,65,66,69],{},[39,67,68],{},"Persistence and Updates",": Managing stock while the store is open and ensuring data survives system restarts.",[36,71,72,75],{},[39,73,74],{},"Metadata Filtering",": The ability to restrict searches by specific criteria (e.g., 'only this user's documents'). This is technically difficult because filters often conflict with the pre-indexed 'aisles,' requiring complex engineering to maintain performance.",[36,77,78,81],{},[39,79,80],{},"Hybrid Search",": Combining vector similarity with traditional keyword or structured data filtering.",{"title":83,"searchDepth":84,"depth":84,"links":85},"",2,[86,87,88],{"id":19,"depth":84,"text":20},{"id":27,"depth":84,"text":28},{"id":57,"depth":84,"text":58},[90],"AI & LLMs",null,"md",false,{"content_references":95,"triage":101},[96],{"type":97,"title":98,"url":99,"context":100},"tool","FAISS","https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffaiss","mentioned",{"relevance":102,"novelty":103,"quality":104,"actionability":104,"composite":105,"reasoning":106},5,3,4,4.15,"Category: AI & LLMs. The article provides a detailed explanation of vector search techniques, specifically focusing on Approximate Nearest Neighbor (ANN) methods, which are crucial for AI-powered product builders dealing with large datasets. It offers actionable insights into the trade-offs between speed and accuracy in search systems, making it relevant for developers looking to implement efficient search functionalities.",true,"\u002Fsummaries\u002F58aa82efe57a452b-vector-search-explained-from-brute-force-to-ann-summary","2026-06-23 04:50:43","2026-06-23 12:56:47",{"title":5,"description":83},{"loc":108},"58aa82efe57a452b","Level Up Coding","article","https:\u002F\u002Flevelup.gitconnected.com\u002Fvector-search-explained-visually-how-databases-find-a-needle-in-5-million-vectors-bb67825b1a75?source=rss----5517fd7b58a6---4","summaries\u002F58aa82efe57a452b-vector-search-explained-from-brute-force-to-ann-summary",[119,120,121],"llm","ai-tools","data-science","Vector search scales by replacing linear scans with 'aisles'—grouping similar vectors into clusters defined by centroids—allowing systems to ignore irrelevant data and return results in 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When a model fails, engineers often struggle to bridge the gap between a high-level benchmark failure (e.g., a drop in BBH scores) and the specific data corpus intervention required to fix it. This process is usually driven by intuition rather than a systematic, auditable methodology.",[17,5718,5720],{"id":5719},"the-capability-slice-framework","The Capability Slice Framework",[22,5722,5723],{},"To solve this, the authors introduce the \"capability slice\": a granular unit of evaluation that groups samples by background condition, task type, solving operation, and output constraint. This unit is designed to be:",[33,5725,5726,5732],{},[36,5727,5728,5731],{},[39,5729,5730],{},"Specific enough"," to localize a single model weakness.",[36,5733,5734,5737],{},[39,5735,5736],{},"Stable enough"," to survive aggregation across larger datasets.",[22,5739,5740],{},"By combining these slices with a structured evaluation taxonomy and a non-instruction data taxonomy, the authors create a closed-loop system. This system allows developers to map specific benchmark failures directly to targeted data interventions, turning debugging into an experimental, repeatable process.",[17,5742,5744],{"id":5743},"validating-the-loop","Validating the Loop",[22,5746,5747],{},"The authors demonstrate the effectiveness of this loop through two contrasting case studies:",[33,5749,5750,5761],{},[36,5751,5752,5755,5756,5760],{},[39,5753,5754],{},"Ruling out data interventions:"," When continued pre-training caused a -46.82% drop in BBH performance, the loop diagnosed the issue as a single masked ",[5757,5758,5759],"code",{},"\u003CEOS>"," loss rather than a reasoning failure. Restoring this loss recovered BBH to 66.44, surpassing the original checkpoint without changing the training data.",[36,5762,5763,5766],{},[39,5764,5765],{},"Targeted data interventions:"," For a persistent math-reasoning weakness, the loop decomposed the failure by solving operation. By applying a weakness-targeted sampling procedure, the authors increased AIME2025\u002FAIME2026 Pass@128 scores from 6.67\u002F0.00 to 26.67 each.",[22,5768,5769],{},"These results demonstrate that evaluation-to-data inference can be routine and experimentally validated, moving beyond the guesswork common in current LLM development workflows.",{"title":83,"searchDepth":84,"depth":84,"links":5771},[5772,5773,5774],{"id":5712,"depth":84,"text":5713},{"id":5719,"depth":84,"text":5720},{"id":5743,"depth":84,"text":5744},[90],{"content_references":5777,"triage":5778},[],{"relevance":102,"novelty":104,"quality":104,"actionability":104,"composite":5779,"reasoning":5780},4.35,"Category: AI & LLMs. The article introduces a novel framework ('capability slices') that directly addresses a common pain point for AI developers: linking model evaluation to actionable data interventions. This practical approach provides a structured methodology that engineers can implement to improve model performance.","\u002Fsummaries\u002Faac1e0a4d1f9f899-closing-the-loop-between-model-evaluation-and-data-summary","2026-06-30 12:57:17",{"title":5702,"description":83},{"loc":5781},"aac1e0a4d1f9f899","arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.28471","summaries\u002Faac1e0a4d1f9f899-closing-the-loop-between-model-evaluation-and-data-summary",[119,5790,121,120],"machine-learning","By introducing 'capability slices'—groups of evaluation samples categorized by task and operation—engineers can transform benchmark failures into precise, actionable data interventions rather than relying on intuition.",[],"ZemHrAtADRDkjl5KUkD-tKwJG0m6Zf2VNCFN1K48EZ0",{"id":5795,"title":5796,"ai":5797,"body":5802,"categories":5869,"created_at":91,"date_modified":91,"description":83,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":5870,"navigation":107,"path":5882,"published_at":5883,"question":91,"scraped_at":5884,"seo":5885,"sitemap":5886,"source_id":5887,"source_name":5888,"source_type":5889,"source_url":5890,"stem":5891,"tags":5892,"thumbnail_url":5894,"tldr":5895,"tweet":5896,"unknown_tags":5897,"__hash__":5898},"summaries\u002Fsummaries\u002F960b93971621a7f9-governing-ai-agents-with-looker-and-mcp-summary.md","Governing AI Agents with Looker and MCP",{"provider":7,"model":8,"input_tokens":5798,"output_tokens":5799,"processing_time_ms":5800,"cost_usd":5801},4394,634,3094,0.0020495,{"type":14,"value":5803,"toc":5864},[5804,5808,5811,5815,5818,5832,5836,5839],[17,5805,5807],{"id":5806},"moving-from-raw-sql-to-governed-data-models","Moving from Raw SQL to Governed Data Models",[22,5809,5810],{},"Building enterprise AI agents that interact directly with raw databases often leads to fragile, unmaintainable SQL. The core challenge is that LLMs lack context regarding business logic, definitions, and access controls. By bridging agents to Looker’s governed semantic layer, developers ensure that AI-driven insights are grounded in a single, validated source of truth. This architecture separates business logic (maintained in Looker) from execution (handled by the agent).",[17,5812,5814],{"id":5813},"the-role-of-mcp-and-the-agent-development-kit","The Role of MCP and the Agent Development Kit",[22,5816,5817],{},"The Model Context Protocol (MCP) acts as a universal language, allowing agents to discover and invoke capabilities within Looker without needing to understand complex API calls.",[33,5819,5820,5826],{},[36,5821,5822,5825],{},[39,5823,5824],{},"MCP Toolbox",": An open-source, production-ready resource that exposes data sources as standardized tools. It includes a pre-built Looker toolset, allowing developers to perform tasks like auditing LookML project files or executing governed queries without writing custom integration code for every project.",[36,5827,5828,5831],{},[39,5829,5830],{},"Agent Development Kit (ADK)",": This layer orchestrates the LLM and the agent. It allows the agent to discover tools via MCP and execute technical tasks. Because the MCP bridge stays in sync with LookML, any changes made to the underlying business model are automatically reflected in the agent's context.",[17,5833,5835],{"id":5834},"modular-architecture-for-scalability","Modular Architecture for Scalability",[22,5837,5838],{},"This approach provides a flexible, modular framework for building AI applications:",[5840,5841,5842,5848,5858],"ol",{},[36,5843,5844,5847],{},[39,5845,5846],{},"Looker Layer",": Centralizes business logic, dimensions, measures, and access filters.",[36,5849,5850,5853,5854,5857],{},[39,5851,5852],{},"Transport Layer",": Powered by MCP and the AI toolbox, it acts as a translator, exposing internal functions (metadata retrieval, query execution) as discoverable tools defined in a ",[5757,5855,5856],{},"tools.yaml"," configuration.",[36,5859,5860,5863],{},[39,5861,5862],{},"Application Layer",": Utilizes the ADK to orchestrate the LLM, allowing developers to choose their preferred model while maintaining strict governance over the data the agent can access.",{"title":83,"searchDepth":84,"depth":84,"links":5865},[5866,5867,5868],{"id":5806,"depth":84,"text":5807},{"id":5813,"depth":84,"text":5814},{"id":5834,"depth":84,"text":5835},[90],{"content_references":5871,"triage":5880},[5872,5876,5878],{"type":97,"title":5873,"url":5874,"context":5875},"Looker MCP Server","https:\u002F\u002Fgoo.gle\u002F4a4g4us","recommended",{"type":97,"title":5877,"context":100},"Model Context Protocol (MCP)",{"type":97,"title":5879,"context":100},"Google Agent Development Kit (ADK)",{"relevance":102,"novelty":104,"quality":104,"actionability":104,"composite":5779,"reasoning":5881},"Category: AI & LLMs. The article provides a detailed framework for integrating AI agents with Looker's semantic layer, addressing a specific pain point of fragile SQL generation. It offers actionable insights through the introduction of the Model Context Protocol (MCP) and the Agent Development Kit (ADK), which developers can implement to enhance their AI applications.","\u002Fsummaries\u002F960b93971621a7f9-governing-ai-agents-with-looker-and-mcp-summary","2026-06-19 16:00:01","2026-06-20 12:56:24",{"title":5796,"description":83},{"loc":5882},"960b93971621a7f9","Google Cloud Tech","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CgwslR4pgL4","summaries\u002F960b93971621a7f9-governing-ai-agents-with-looker-and-mcp-summary",[119,5893,120,121],"agents","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FCgwslR4pgL4\u002Fhqdefault.jpg","By using the Model Context Protocol (MCP) to connect AI agents to Looker's semantic layer, developers can replace fragile raw SQL generation with governed, model-aware data interactions.","This video explains how to connect AI agents to enterprise data by using the [Looker MCP Server](https:\u002F\u002Fgoo.gle\u002F4a4g4us). It demonstrates how to use the Model Context Protocol to expose Looker's semantic layer as a set of tools, allowing agents to query data through governed business logic rather than raw SQL.",[],"o5r6ShZF72LourkfhxXaJEqvurd9JgnGSKaF4CDR5Qs",{"id":5900,"title":5901,"ai":5902,"body":5907,"categories":5951,"created_at":91,"date_modified":91,"description":83,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":5952,"navigation":107,"path":5961,"published_at":5962,"question":91,"scraped_at":5962,"seo":5963,"sitemap":5964,"source_id":5965,"source_name":5966,"source_type":115,"source_url":5967,"stem":5968,"tags":5969,"thumbnail_url":91,"tldr":5971,"tweet":91,"unknown_tags":5972,"__hash__":5973},"summaries\u002Fsummaries\u002F6b157013391437be-google-s-gemini-sql2-sets-new-bird-benchmark-recor-summary.md","Google's Gemini-SQL2 Sets New BIRD Benchmark Record",{"provider":7,"model":8,"input_tokens":5903,"output_tokens":5904,"processing_time_ms":5905,"cost_usd":5906},9187,619,3793,0.00322525,{"type":14,"value":5908,"toc":5947},[5909,5913,5916,5919,5923,5930,5933,5944],[17,5910,5912],{"id":5911},"performance-and-benchmarking","Performance and Benchmarking",[22,5914,5915],{},"Google Research has introduced Gemini-SQL2, a specialized text-to-SQL capability built on the Gemini 3.1 Pro foundation model. It achieved an 80.04% execution accuracy on the BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) benchmark, specifically within the Single Trained Model track. This track is significant because it prohibits the use of complex ensembling or external agentic frameworks, isolating the model's core ability to translate natural language into valid, executable SQL.",[22,5917,5918],{},"For context, the BIRD benchmark is considered a rigorous industry standard, containing 12,751 question-SQL pairs across 37 domains. Unlike older benchmarks, it requires models to handle 'dirty' database values and incorporate external knowledge. While Gemini-SQL2 leads the current leaderboard, it remains approximately 12.92 percentage points behind the estimated human performance of 92.96%.",[17,5920,5922],{"id":5921},"technical-implementation-and-integration","Technical Implementation and Integration",[22,5924,5925,5926,5929],{},"Gemini-SQL2 is designed to address the inherent difficulty of mapping natural language to complex business contexts. While Google has not yet released a standalone model string or API endpoint for Gemini-SQL2, the current implementation pattern for Gemini-based SQL generation involves schema-grounded prompting. Developers are encouraged to use the ",[5757,5927,5928],{},"google-genai"," SDK to pass schema definitions and natural language questions to the model.",[22,5931,5932],{},"To achieve production-grade reliability, the article recommends implementing an execution verification loop:",[5840,5934,5935,5938,5941],{},[36,5936,5937],{},"Generate the SQL query.",[36,5939,5940],{},"Attempt to execute the query against the database.",[36,5942,5943],{},"If an error occurs, capture the error message and feed it back into the model for a corrective retry.",[22,5945,5946],{},"This iterative approach mirrors the logic used by the BIRD benchmark to validate execution accuracy, ensuring that the generated SQL is not just syntactically correct, but functionally valid against the specific database schema.",{"title":83,"searchDepth":84,"depth":84,"links":5948},[5949,5950],{"id":5911,"depth":84,"text":5912},{"id":5921,"depth":84,"text":5922},[90],{"content_references":5953,"triage":5959},[5954,5957],{"type":5955,"title":5956,"context":100},"other","BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)",{"type":97,"title":5958,"context":5875},"google-genai SDK",{"relevance":102,"novelty":104,"quality":104,"actionability":104,"composite":5779,"reasoning":5960},"Category: AI & LLMs. The article provides in-depth insights into Google's Gemini-SQL2 and its performance on the BIRD benchmark, which is highly relevant for developers looking to integrate AI into SQL generation. It includes a practical implementation pattern and an execution verification loop, making it actionable for the audience.","\u002Fsummaries\u002F6b157013391437be-google-s-gemini-sql2-sets-new-bird-benchmark-recor-summary","2026-06-13 12:56:21",{"title":5901,"description":83},{"loc":5961},"6b157013391437be","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F12\u002Fgoogle-releases-gemini-sql2-gemini-3-1-pro-text-to-sql-scores-80-04-on-bird-single-model-leaderboard\u002F","summaries\u002F6b157013391437be-google-s-gemini-sql2-sets-new-bird-benchmark-recor-summary",[119,120,121,5970],"coding","Google's Gemini-SQL2, powered by Gemini 3.1 Pro, achieved an 80.04% execution accuracy on the BIRD text-to-SQL benchmark, outperforming all other single-model entries.",[],"cubai3C6QRAzLzgCu0xp0QD9R843yOLFdD2jQrMFCEs",{"id":5975,"title":5976,"ai":5977,"body":5982,"categories":6053,"created_at":91,"date_modified":91,"description":83,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":6054,"navigation":107,"path":6068,"published_at":6069,"question":91,"scraped_at":6070,"seo":6071,"sitemap":6072,"source_id":6073,"source_name":6074,"source_type":5889,"source_url":6075,"stem":6076,"tags":6077,"thumbnail_url":6078,"tldr":6079,"tweet":6080,"unknown_tags":6081,"__hash__":6082},"summaries\u002Fsummaries\u002F65fa4393974ae2b6-rag-is-not-dead-the-shift-to-iterative-agentic-ret-summary.md","RAG is Not Dead: The Shift to Iterative Agentic Retrieval",{"provider":7,"model":8,"input_tokens":5978,"output_tokens":5979,"processing_time_ms":5980,"cost_usd":5981},6273,789,4376,0.00275175,{"type":14,"value":5983,"toc":6047},[5984,5988,5995,5999,6002,6016,6019,6023,6026,6037,6040,6044],[17,5985,5987],{"id":5986},"the-evolution-of-retrieval","The Evolution of Retrieval",[22,5989,5990,5991,5994],{},"Despite the \"RAG is dead\" discourse on social media, actual usage metrics show a massive inflection point in search volume starting in mid-2025. The confusion stems from a narrow definition of RAG as a single, static vector search call. In production, sophisticated systems have moved toward ",[39,5992,5993],{},"Agentic Retrieval","—an iterative process where agents use a mix of vector search, full-text search (BM25), regex, and filters to progressively reason over context until they have the necessary information to complete a task.",[17,5996,5998],{"id":5997},"embeddings-as-cached-compute","Embeddings as Cached Compute",[22,6000,6001],{},"Architectural decisions between tools like Cursor and Claude Code highlight a fundamental trade-off in AI engineering:",[33,6003,6004,6010],{},[36,6005,6006,6009],{},[39,6007,6008],{},"Per-session discovery (e.g., Claude Code):"," Uses grep-based retrieval without upfront indexing. This avoids indexing costs but incurs higher latency and token costs per run because the agent must re-discover information every time.",[36,6011,6012,6015],{},[39,6013,6014],{},"Upfront indexing (e.g., Cursor):"," Treats embeddings as \"cached compute.\" While there is an initial cost to parse, chunk, and embed a codebase, it enables lightweight, high-speed retrieval at runtime.",[22,6017,6018],{},"For teams working on shared codebases, this can be optimized further using Merkle trees to identify and re-index only the changed files, significantly reducing redundant compute.",[17,6020,6022],{"id":6021},"impact-and-performance","Impact and Performance",[22,6024,6025],{},"Data from Cursor’s implementation of semantic search demonstrates that even small percentage gains in accuracy and retention are significant when weighted by query relevance. Specifically, semantic search integration led to:",[33,6027,6028,6031,6034],{},[36,6029,6030],{},"A 24% increase in answer accuracy for their composer model.",[36,6032,6033],{},"A 2.6% gain in code retention for large codebases.",[36,6035,6036],{},"A 2.2% drop in dissatisfied user requests.",[22,6038,6039],{},"These gains are notable because semantic search is not triggered on every query; the impact is concentrated on the subset of queries where retrieval is actually necessary.",[17,6041,6043],{"id":6042},"the-right-million-strategy","The \"Right Million\" Strategy",[22,6045,6046],{},"As context windows grow, the engineering challenge shifts from capacity to precision. Echoing Jeff Dean’s philosophy, the goal is not to feed an LLM a trillion tokens at once, but to use an iterative retrieval mechanism to narrow down a massive corpus to the \"right million\" tokens. This approach optimizes for both cost and agent performance, ensuring the model receives the most relevant context without the noise of a full-context dump.",{"title":83,"searchDepth":84,"depth":84,"links":6048},[6049,6050,6051,6052],{"id":5986,"depth":84,"text":5987},{"id":5997,"depth":84,"text":5998},{"id":6021,"depth":84,"text":6022},{"id":6042,"depth":84,"text":6043},[90],{"content_references":6055,"triage":6066},[6056,6059,6063],{"type":97,"title":6057,"url":6058,"context":100},"Turbopuffer","https:\u002F\u002Fturbopuffer.com",{"type":97,"title":6060,"url":6061,"context":6062},"Cursor","https:\u002F\u002Fcursor.com","reviewed",{"type":97,"title":6064,"url":6065,"context":6062},"Claude Code","https:\u002F\u002Fclaude.ai",{"relevance":102,"novelty":104,"quality":104,"actionability":104,"composite":5779,"reasoning":6067},"Category: AI & LLMs. The article discusses the evolution of Retrieval-Augmented Generation (RAG) into iterative agentic retrieval, addressing a specific audience pain point about moving from basic implementations to more sophisticated systems. It provides concrete examples of architectural decisions and performance metrics that can be directly applied by product builders in AI engineering.","\u002Fsummaries\u002F65fa4393974ae2b6-rag-is-not-dead-the-shift-to-iterative-agentic-ret-summary","2026-06-09 17:00:27","2026-06-10 12:56:17",{"title":5976,"description":83},{"loc":6068},"65fa4393974ae2b6","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UM6sFg_jdlE","summaries\u002F65fa4393974ae2b6-rag-is-not-dead-the-shift-to-iterative-agentic-ret-summary",[120,5893,119,121],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FUM6sFg_jdlE\u002Fhqdefault.jpg","RAG isn't dying; it's evolving from simple vector search into iterative, agentic retrieval. The key is treating semantic search as 'cached compute' that allows agents to narrow down massive datasets to the 'right million' tokens efficiently.","This talk argues that the \"RAG is dead\" discourse is a misunderstanding of what retrieval actually is. The speaker frames vector search as a form of \"cached compute\" and contrasts the upfront indexing approach used by [Cursor](https:\u002F\u002Fcursor.com) with the per-session grep-based approach of [Claude Code](https:\u002F\u002Fclaude.com\u002Fclaude-code), noting that both are valid architectural tradeoffs depending on query volume.",[],"rDigj_PV_GejslQxvaDWzEw8_v7Tv-p-r0YZQf2iYNk"]