History — 2026 week 23 (Jun 1 – Jun 7, 2026)
Newest first.
2026-06-02 — Block-Max WAND Cluster (~7 notes)
Articles — WAND and Block-Max WAND - Efficient Algorithms for Top-k Document Retrieval (Jithendrasaikilaru, Medium, 2025-10-31). Paywalled; content from public summary. Introductory overview of dynamic pruning for top-k retrieval, covering WAND (Broder et al. 2003), Block-Max WAND (Ding & Suel 2011), MaxScore lineage, and DAAT context. BlockMax WAND - How Weaviate Achieved 10x Faster Keyword Search (by André Mourão and Joon-Pil (JP) Hwang, Weaviate blog, 2025-02-26). Technical implementation in Weaviate v1.29 (technical preview): 5–10× p50 latency speedup over plain WAND, 50–91% index size reduction via variable-length block encoding and delta compression of doc IDs; MS MARCO and Fever benchmark results. Faster Retrieval of Top Hits in Elasticsearch with Block-Max WAND (by Adrien Grand, Elastic blog, 2019-02-05). Original integration into Lucene 8.0 / Elasticsearch 7.0; lineage from MAXSCORE (1995) through WAND (2003); 3×–15× speedup; breaking API changes (non-negative scores, approximate hit counts via track_total_hits).
People — Jithendrasaikilaru (Medium writer, IR/AI/cybersecurity), André Mourão (Weaviate), Joon-Pil (JP) Hwang (Weaviate), Adrien Grand (Elastic, core Lucene committer).
Updated — WAND, Block-Max WAND concept notes linked to new articles.
2026-06-01 — History Audit & Comprehensive Catch-Up (~212 notes)
Catch-up log — notes present in the vault but not individually recorded in prior entries (initial import, May 16 batch, and later additions). Also includes post-catchup articles added later the same day.
Articles — RRF is Not Enough (by Doug Turnbull, 2024-11-03), Agentic search models (by Doug Turnbull, 2026-05-11), Agents turn simple keyword search into compelling search experiences (by Doug Turnbull), Beyond Semantic Similarity - Rethinking Retrieval for Agentic Search via Direct Corpus Interaction (TIGER-Lab, 2026-05-08), Superintelligent Retrieval Agent SIRA (Meta / Rice University, 2026), You Say Search I Say Recs - Spotify Agentic Query Understanding (Spotify Research, 2025), Qwen3 Embedding Series (Alibaba Qwen Team, 2025-06-05), Fine-Tuning Qwen3 Embeddings for Product Category Classification, LLM-as-a-Judge When to Use Reasoning CoT and Explanations (by Aparna Dhinakaran, 2025-08-20), Incremental AI Adoption for E-commerce Search (2026-01-18), On Search Leadership (by Daniel Tunkelang), Canva Search Pipeline Part I, Bayesian BM25 is Cool, Can BM25 be a Probability, Broad and Ambiguous Search Queries, Click Residual - A Query Success Metric, Deconstructing E-Commerce Search - The 12 Query Types, Diversity Metrics for Recommender Systems, Ecommerce Search Governance - Move Faster Not Slower, How Context-Aware Embeddings Are Transforming Enterprise Search, Late Interaction Models - How to Scale and Optimize in Elasticsearch, LLM-Powered Query Extraction for Autocomplete, Metadata - The 3rd Kind of Retrieval, Mirror Mirror - All About Search Suggestions, Mutually Assured Distraction, My Journey Building Elasticsearch for Retail, Patterns for Personalization, Probability-Proportional-to-Size Sampling for Relevance Evaluation, Real Talk About Synonyms and Search, Searching for Goldilocks, Session vs Query based Search Evals, Statistical and Human-Centered Approaches to Search Improvement, Three Pillars of Search Quality - Findability, Three Pillars of Search Quality - Discovery and Inspiration, Autosuggest Ranking, Autosuggest Retrieval Data Structures and Algorithms.
Post-catchup articles (same day) — SEARCH-R1 - Reinforcement Learning-Enhanced Multi-Turn Search and Reasoning for LLMs (by QvickRead, Medium, 2025-03-19). RL-based multi-turn search framework; token-level loss masking; PPO and GRPO training; +10–26% on QA benchmarks. From RAG to Search-R1 - Evolving Language Models from Knowledge Retrieval to Autonomous Reasoning (by Lakshmi Devi Prakash, Medium, 2025-04-11). Accessible comparison of RAG vs. Search-R1 architectures. Observability for AI Workloads A New Paradigm for a New Era (by Dotan Horovits, 2026). Observability patterns for AI/search workloads; OpenTelemetry framing. Elasticsearch vs. OpenSearch (2025) The Definitive Showdown (by Jagadeesh Chandra, Medium). ES vs. OS feature/ecosystem comparison. OpenSearch vs. Elasticsearch A Comprehensive Comparison in 2025 (by Frank Goortani, Medium). Balanced architecture and licensing comparison. Plus 7 dimensionality reduction articles: t-SNE Clearly Explained (Kemal Erdem), t-SNE Explained - Math and Intuition (Achinoam Soroker), t-SNE Explained - Visualising High-Dimensional Data (Billy Chan), PCA vs t-SNE - Which One Should You Use for Visualization (Namratesh Shrivastav), PCA vs t-SNE vs UMAP - Visualizing the Invisible (Lakhan Bukkawar), Understanding Principal Component Analysis (PCA) (Roshmita Dey), Principal Component Analysis (PCA) In Depth (Fraidoon Omarzai).
New Section — Case Studies (9 notes) — Airbnb - ML-Powered Experiences Ranking, Canva - Search Pipeline Modernization, Etsy - Search Quality and Query Understanding, Kleinanzeigen - Vespa Migration for Homepage Feed, Netflix - Content Search Architecture, Skyscanner - Learning to Rank for Flights, Slack - Enterprise Message Search with LTR, Uber Eats - Scaling Search for Food Delivery, Zalando - Self-DoS via Facet Aggregation. Structured use-case notes synthesising engineering lessons from each company’s published work.
New Section — Datasets (4 notes) — Amazon ESCI Dataset, ESCI-S Dataset, Home Depot Product Search Relevance, WANDS Dataset. Benchmark datasets for search relevance evaluation.
Concepts — Bag-of-Documents Model, Clean Context, Click Signals, Direct Corpus Interaction, GGUF, Hit Rate at K, Intent Drift, IVF, LoRA, PEFT, QLoRA, SIRA, Session-Based Evaluation, Task-Aware Embeddings, Token Pooling, UDCG, WAND, UMAP, Search Observability, PCA, t-SNE, Reinforcement Learning for Search, Search-R1.
People — Omar Khattab (Stanford, ColBERT author), Aparna Dhinakaran (Arize AI), Thibault Formal (SPLADE co-author), Stéphane Clinchant (SPLADE co-author), Antoine Chaffin, Florent Krzakala, Han Xiao (Jina AI), James Briggs, Shaw Talebi, Matei Zaharia (Databricks / MLflow), Laura Ham (Weaviate), Marianne Haugvaldstad, Lester Solbakken, Brage Vik, Rudolf Batt, Nicolò Rinaldi, Luca Arnaboldi, Hugo Galvão, Honza Král, Jodi Sloan, Michael Ryaboy, Michael Hannecke, Peter Straßer, Siegfried Schüle, Andrea Schütt, Andreas Wagner, Alexander Marquardt, Amélie Chatelain, Asif Makhani, Benjamin Trent, Dai Sugimori, Daniel Doro, David Argüello Sánchez, Dima Kan, Dmitriy Meyerzon, Jaideep Ray, Maryna Kryvko, Prateek Chandra Jha, Ravindra Harige, Taylor Roy, Trey Grainger, Jagadeesh Chandra, Frank Goortani, Shay Banon (Elasticsearch creator), Dotan Horovits (observability), Lakshmi Devi Prakash, QvickRead, Kemal Erdem, Achinoam Soroker, Billy Chan, Namratesh Shrivastav, Lakhan Bukkawar, Roshmita Dey, Fraidoon Omarzai, Geoffrey Hinton (t-SNE co-creator), Laurens van der Maaten (t-SNE co-creator), Chris Fournie (Reddit), Ritik Jain, Venkat Ram Rao, Freddy Domínguez.
Companies — Empathy, Jina AI, LightOn AI, Meta, Sease, searchHub, Shopify, Reddit.
Topics — Elasticsearch vs OpenSearch, Multilingual Search, Personalization in Search, Relevance Program Setup, Search Platforms, Search UX, Spelling Correction in Search.
Tools — OpenSearch, Milvus Vector DB.