Vector Podcast

Turbopuffer search engine supports such products as Cursor, Notion, Linear, Superhuman and Readwise.This episode on YouTube: https://youtu.be/I8ZtqajighgMedium: https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3Dev: https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfaIf you are on Lucene / OpenSearch stack, you can go managed by signing up here: https://console.aiven.io/signup?utm_source=youtube&utm_medium=&&utm_content=vectorpodcastTime codes:00:00 Intro00:15 Napkin Problem 4: Throughput of Redis01:35 Episode intro02:45 Simon's background, including implementation of Turbopuffer09:23 How Cursor became an early client11:25 How to test pre-launch14:38 Why a new vector DB deserves to exist?20:39 Latency aspect26:27 Implementation language for Turbopuffer28:11 Impact of LLM coding tools on programmer craft30:02 Engineer 2 CEO transition35:10 Architecture of Turbopuffer43:25 Disk vs S3 latency, NVMe disks, DRAM48:27 Multitenancy50:29 Recall@N benchmarking59:38 filtered ANN and Big-ANN Benchmarks1:00:54 What users care about more (than Recall@N benchmarking)1:01:28 Spicy question about benchmarking in competition1:06:01 Interesting challenges ahead to tackle1:10:13 Simon's announcementShow notes:- Turbopuffer in Cursor: https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223stranscript: https://lexfridman.com/cursor-team-transcript- https://turbopuffer.com/- Napkin Math: https://sirupsen.com/napkin- Follow Simon on X: https://x.com/Sirupsen- Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696/

Vector Podcast website: https://vectorpodcast.comHaystack US 2025: https://haystackconf.com/2025/Federated search, Keyword & Neural Search, ML Optimisation, Pros and Cons of Hybrid searchIt is fascinating and funny how things develop, but also turn around. In 2022-23 everyone was buzzing about hybrid search. In 2024 the conversation shifted to RAG, RAG, RAG. And now we are in 2025 and back to hybrid search - on a different level: finally there are strides and contributions towards making hybrid search parameters learnt with ML. How cool is that?Design: Saurabh Rai, https://www.linkedin.com/in/srbhr/The design of this episode is inspired by a scene in Blade Runner 2049. There's a clear path leading towards where people want to go to, yet they're searching for something.00:00 Intro00:54 Eric's intro and Daniel's background02:50 Importance of Hybrid search: Daniel's take07:26 Eric's take10:57 Dmitry's take11:41 Eric's predictions13:47 Doug's blog on RRF is not enough16:18 How to not fall short of the blind picking in RRF: score normalization, combinations and weights25:03 The role of query understanding: feature groups35:11 Lesson 1 from Daniel: Simple models might be all you need36:30 Lesson 2: query features might be all you need38:30 Reasoning capabilities in search40:02 Question from Eric: how is this different from Learning To Rank?42:46 Carrying the past in Learning To Rank / any rank44:21 Demo!51:52 How to consume this in OpenSearch55:15 What's next58:44 Haystack US 2025

https://www.vectorpodcast.com/I had fun interacting with NotebookLM - mostly for self-educational purposes. I think this tool can help by bringing an additional perspective over a textual content. It ties to what RAG (Retrieval Augmented Generation) can do to content generation in another modality. In this case, text is used to augment the generation of a podcast episode. This episode is based on my blog post: https://dmitry-kan.medium.com/the-rise-fall-and-future-of-vector-databases-how-to-pick-the-one-that-lasts-6b9fbb43bbbeTime codes:00:00 Intro to the topic1:11 Dmitry's knowledge in the space1:54 Unpacking the Rise & Fall idea3:14 How attention got back to Vector DBs for a bit4:18 Getting practical: Dmitry's guide for choosing the right Vector Database4:39 FAISS5:34 What if you need fine-grained keyword search? Look at Apache Lucene-based engines6:41 Exception to the rule: Late-interaction models8:30 Latency and QPS: GSI APU, Vespa, Hyperspace9:28 Strategic approach9:55 Cloud solutions: CosmosDB, Vertex AI, Pinecone, Weaviate Cloud10:14 Community voice: pgvector10:48 Picture of the fascinating future of the field12:23 Question to the audience12:44 Taking a step back: key points13:45 Don't get caught up in trendy shiny new tech

Vector Podcast website: https://vectorpodcast.comGet your copy of John's new book "Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications": https://amzn.to/4fMj2EfJohn Berryman is the founder and principal consultant of Arcturus Labs, where he specializes in AI application development (Agency and RAG). As an early engineer on GitHub Copilot, John contributed to the development of its completions and chat functionalities, working at the forefront of AI-assisted coding tools. John is coauthor of "Prompt Engineering for LLMs" (O'Reilly).Before his work on Copilot, John's focus was search technology. His diverse experience includes helping to develop next-generation search system for the US Patent Office, building search and recommendations for Eventbrite, and contributing to GitHub's code search infrastructure. John is also coauthor of "Relevant Search" (Manning), a book that distills his expertise in the field.John's unique background, spanning both cutting-edge AI applications and foundational search technologies, positions him at the forefront of innovation in LLM applications and information retrieval.00:00 Intro02:19 John's background and story in search and ML06:03 Is RAG just a prompt engineering technique?10:15 John's progression from a search engineer to ML researcher13:40 LLM predictability vs more traditional programming22:31 Code assist with GitHub Copilot29:44 Role of keyword search for code at GitHub35:01 GenAI: existential risk or pure magic? AI Natives39:40 What are Artifacts46:59 Demo!55:13 Typed artifacts, tools, accordion artifacts56:21 From Web 2.0 to Idea exchange57:51 Spam will transform into Slop58:56 John's new book and Acturus Labs introShow notes:- John Berryman on X: https://x.com/JnBrymn- Acturus Labs: https://arcturus-labs.com/- John's blog on Artifacts (see demo in the episode): https://arcturus-labs.com/blog/2024/11/11/cut-the-chit-chat-with-artifacts/Watch on https://youtu.be/60HAtHVBYj8

00:00 Intro01:31 Leo's story09:59 SPLADE: single model to solve both dense and sparse?21:06 DeepImpact29:58 NMSLIB: what are non-metric spaces34:21 How HNSW and NMSLIB joined forces41:11 Why FAISS did not choose NMSLIB's algorithm43:36 Serendipity of discovery and the creation of industries47:06 Vector Search: intellectually rewarding, professionally undervalued52:37 Why RDBMS Still Struggles with Scalable Vector and Free-Text Search1:00:16 Leo's recent favorite papersLeo Boytsov on LinkedIn: https://www.linkedin.com/in/leonidboytsov/ and X: https://x.com/srchvrsLeo Boytsov’s paper list: https://scholar.google.com/citations?hl=en&user=I79y2i4AAAAJ&view_op=list_works&sortby=pubdateLots of papers and other material from Leo: https://www.youtube.com/watch?v=gzWErcOXIKk

Alessandro's talk on Hybrid Search with Apache Solr Reciprocal Rank Fusion: https://www.youtube.com/watch?v=8x2cbT5CCEM&list=PLq-odUc2x7i8jHpa6PHGzmxfAPEz-c-on&index=500:00 Intro00:50 Alessandro's take on the bbuzz'24 conference01:25 What and value of hybrid search04:55 Explainability of vector search results to users09:27 Explainability of vector search results to search engineers13:12 State of hybrid search in Apache Solr14:32 What's in Reciprocal Rank Fusion beyond round-robin?18:30 Open source for LLMs22:48 How we should approach this issue in business and research26:12 How to maintain the status of an open-source LLM / system 30:06 Prompt engineering (hope and determinism)34:03 DSpy35:16 What's next in Solr

Video: https://youtu.be/dVIPBxHJ1kQ00:00 Intro00:15 Greets for Sonam01:02 Importance of metric learning3:37 Sonam's background: Rasa, Qdrant4:31 What's EmbedAnything5:52 What a user gets8:48 Do I need to know Rust?10:18 Call-out to the community10:35 Multimodality12:32 How to evaluate quality of LLM-based systems16:38 QA for multimodal use cases18:17 Place for a human in the LLM craze19:00 Use cases for EmbedAnything20:54 Closing theme (a longer one - enjoy!)Show notes:- GitHub: https://github.com/StarlightSearch/EmbedAnything- HuggingFace Candle: https://github.com/huggingface/candle- Sonam's talk on Berlin Buzzwords 2024: https://www.youtube.com/watch?v=YfR3kuSo-XQ- Removing GIL from Python: https://peps.python.org/pep-0703- Blind pairs in CLIP: https://arxiv.org/abs/2401.06209- Dark matter of intelligence: https://ai.meta.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/- Rasa chatbots: https://github.com/RasaHQ/rasa- Prometheus: https://github.com/prometheus-eval/prometheus-eval- Dino: https://github.com/facebookresearch/dino

00:00 Intro00:30 Greets for Doug01:46 Apache Solr and stuff03:08 Hello LTR project04:42 Secret sauce of Doug's continuous blogging08:50 SearchArray13:22 Running complex ML experiments17:29 Efficient search orgs22:58 Writing a book on search and AIShow notes:- Doug's talk on Learning To Rank at Reddit delivered at the Berlin Buzzwords 2024 conference: https://www.youtube.com/watch?v=gUtF1gyHsSM- Hello LTR: https://github.com/o19s/hello-ltr- Lexical search for pandas with SearchArray: https://github.com/softwaredoug/searcharray- https://softwaredoug.com/- What AI Engineers Should Know about Search: https://softwaredoug.com/blog/2024/06/25/what-ai-engineers-need-to-know-search- AI Powered Search: https://www.manning.com/books/ai-powered-search- Quepid: https://github.com/o19s/quepid- Branching out in your ML / search experiments: https://dvc.org/doc/use-cases- Doug on Twitter: https://x.com/softwaredoug- Doug on LinkedIn: https://www.linkedin.com/in/softwaredoug/

00:00 Intro00:21 Guest Introduction: Eric Pugh03:00 Eric's story in search and the evolution of search technology7:27 Quepid: Improving Search Relevancy10:08 When to use Quepid14:53 Flash back to Apache Solr 1.4 and the book (of which Eric is one author)17:49 Quepid Demo and Future Enhancements23:57 Real-Time Query Doc Pairs with WebSockets24:16 Integrating Quepid with Search Engines25:57 Introducing LLM-Based Judgments28:05 Scaling Up Judgments with AI28:48 Data Science Notebooks in Quepid33:23 Custom Scoring in Quepid39:23 API and Developer Tools42:17 The Future of Search and Personal ReflectionsShow notes:- Hosted Quepid: https://app.quepid.com/- Ragas: Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines https://github.com/explodinggradients...- Why Quepid: https://quepid.com/why-quepid/- Quepid on Github: https://github.com/o19s/quepid

00:00 Intro01:54 Reflection on the past year in AI08:08 Reader LLM (and RAG)12:36 Does it need fine-tuning to a domain?14:20 How LLMs can lie17:32 What if data isn't perfect21:21 SWIRL's secret sauce with Reader LLM23:55 Feedback loop26:14 Some surprising client perspective31:17 How Gen AI can change communication interfaces34:11 Call-out to the Community

00:00 Intro00:42 Louis's background05:39 From Facebook to Rockset07:41 Embeddings prior to deep learning / LLM era12:35 What's Rockset as a product15:27 Use cases18:04 RocksDB as part of Rockset20:33 AI capabilities: ANN index, hybrid search25:11 Types of hybrid search28:05 Can one learn the alpha?30:03 Louis's prediction of the future of vector search33:55 RAG and other AI capabilities41:46 Call out to the Vector Search community46:16 Vector Databases vs Databases49:16 Question of WHY

Topics:00:00 Intro - how do you like our new design?00:52 Greets01:55 Saurabh's background03:04 Resume Matcher: 4.5K stars, 800 community members, 1.5K forks04:11 How did you grow the project?05:42 Target audience and how to use Resume Matcher09:00 How did you attract so many contributors?12:47 Architecture aspects15:10 Cloud or not16:12 Challenges in maintaining OS projects17:56 Developer marketing with Swirl AI Connect21:13 What you (listener) can help with22:52 What drives you?Show notes:- Resume Matcher: https://github.com/srbhr/Resume-Matcherwebsite: https://resumematcher.fyi/- Ultimate CV by Martin John Yate: https://www.amazon.com/Ultimate-CV-Cr...- fastembed: https://github.com/qdrant/fastembed- Swirl: https://github.com/swirlai/swirl-search

Topics:00:00 Intro00:22 Quick demo of SWIRL on the summary transcript of this episode01:29 Sid’s background08:50 Enterprise vs Federated search17:48 How vector search covers for missing folksonomy in enterprise data26:07 Relevancy from vector search standpoint31:58 How ChatGPT improves programmer’s productivity32:57 Demo!45:23 Google PSE53:10 Ideal user of SWIRL57:22 Where SWIRL sits architecturally1:01:46 How to evolve SWIRL with domain expertise1:04:59 Reasons to go open source1:10:54 How SWIRL and Sid interact with ChatGPT1:23:22 The magical question of WHY1:27:58 Sid’s announcements to the communityYouTube version: https://www.youtube.com/watch?v=vhQ5LM5pK_YDesign by Saurabh Rai: https://twitter.com/_srbhr_ Check out his Resume Matcher project: https://www.resumematcher.fyi/

Topics:00:00 Intro02:20 Atita’s path into search engineering09:00 When it’s time to contribute to open source12:08 Taking management role vs software development14:36 Knowing what you like (and coming up with a Solr course)19:16 Read the source code (and cook)23:32 Open Bistro Innovations Lab and moving to Germany26:04 Affinity to Search world and working as a Search Relevance Consultant28:39 Bringing vector search to Chorus and Querqy34:09 What Atita learnt from Eric Pugh’s approach to improving Quepid36:53 Making vector search with Solr & Elasticsearch accessible through tooling and documentation41:09 Demystifying data embedding for clients (and for Java based search engines)43:10 Shifting away from generic to domain-specific in search+vector saga46:06 Hybrid search: where it will be useful to combine keyword with semantic search50:53 Choosing between new vector DBs and “old” keyword engines58:35 Women of Search1:14:03 Important (and friendly) People of Open Source1:22:38 Reinforcement learning applied to our careers1:26:57 The magical question of WHY1:29:26 AnnouncementsSee show notes on YouTube: https://www.youtube.com/watch?v=BVM6TUSfn3E

Topics:00:00 Intro01:54 Things Connor learnt in the past year that changed his perception of Vector Search02:42 Is search becoming conversational?05:46 Connor asks Dmitry: How Large Language Models will change Search?08:39 Vector Search Pyramid09:53 Large models, data, Form vs Meaning and octopus underneath the ocean13:25 Examples of getting help from ChatGPT and how it compares to web search today18:32 Classical search engines with URLs for verification vs ChatGPT-style answers20:15 Hybrid search: keywords + semantic retrieval23:12 Connor asks Dmitry about his experience with sparse retrieval28:08 SPLADE vectors34:10 OOD-DiskANN: handling the out-of-distribution queries, and nuances of sparse vs dense indexing and search39:54 Ways to debug a query case in dense retrieval (spoiler: it is a challenge!)44:47 Intricacies of teaching ML models to understand your data and re-vectorization49:23 Local IDF vs global IDF and how dense search can approach this issue54:00 Realtime index59:01 Natural language to SQL1:04:47 Turning text into a causal DAG1:10:41 Engineering and Research as two highly intelligent disciplines1:18:34 Podcast search1:25:24 Ref2Vec for recommender systems1:29:48 AnnouncementsFor Show Notes, please check out the YouTube episode below.This episode on YouTube: https://www.youtube.com/watch?v=2Q-7taLZ374Podcast design: Saurabh Rai: https://twitter.com/srvbhr

Toloka’s support for Academia: grants and educator partnershipshttps://toloka.ai/collaboration-with-educators-formhttps://toloka.ai/research-grants-formThese are pages leading to them:https://toloka.ai/academy/education-partnershipshttps://toloka.ai/grantsTopics:00:00 Intro01:25 Jenny’s path from graduating in ML to a Data Advocate role07:50 What goes into the labeling process with Toloka11:27 How to prepare data for labeling and design tasks16:01 Jenny’s take on why Relevancy needs more data in addition to clicks in Search18:23 Dmitry plays the Devil’s Advocate for a moment22:41 Implicit signals vs user behavior and offline A/B testing26:54 Dmitry goes back to advocating for good search practices27:42 Flower search as a concrete example of labeling for relevancy39:12 NDCG, ERR as ranking quality metrics44:27 Cross-annotator agreement, perfect list for NDCG and Aggregations47:17 On measuring and ensuring the quality of annotators with honeypots54:48 Deep-dive into aggregations59:55 Bias in data, SERP, labeling and A/B tests1:16:10 Is unbiased data attainable?1:23:20 AnnouncementsThis episode on YouTube: https://youtu.be/Xsw9vPFqGf4Podcast design: Saurabh Rai: https://twitter.com/srvbhr

00:00 Introduction01:11 Yaniv’s background and intro to Searchium & GSI04:12 Ways to consume the APU acceleration for vector search05:39 Power consumption dimension in vector search 7:40 Place of the platform in terms of applications, use cases and developer experience12:06 Advantages of APU Vector Search Plugins for Elasticsearch and OpenSearch compared to their own implementations17:54 Everyone needs to save: the economic profile of the APU solution20:51 Features and ANN algorithms in the solution24:23 Consumers most interested in dedicated hardware for vector search vs SaaS27:08 Vector Database or a relevance oriented application?33:51 Where to go with vector search?42:38 How Vector Search fits into Search48:58 Role of the human in the AI loop58:05 The missing bit in the AI/ML/Search space1:06:37 Magical WHY question1:09:54 Announcements- Searchium vector search: https://searchium.ai/- Dr. Avidan Akerib, founder behind the APU technology: https://www.linkedin.com/in/avidan-akerib-phd-bbb35b12/- OpenSearch benchmark for performance tuning: https://betterprogramming.pub/tired-of-troubleshooting-idle-search-resources-use-opensearch-benchmark-for-performance-tuning-d4277c9f724- APU KNN plugin for OpenSearch: https://towardsdatascience.com/bolster-opensearch-performance-with-5-simple-steps-ca7d21234f6b- Multilingual and Multimodal Search with Hardware Acceleration: https://blog.muves.io/multilingual-and-multimodal-vector-search-with-hardware-acceleration-2091a825de78- Muves talk at Berlin Buzzwords, where we have utilized GSI APU: https://blog.muves.io/muves-at-berlin-buzzwords-2022-3150eef01c4- Not All Vector Databases are made equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696Episode on YouTube: https://youtu.be/EerdWRPuqd4Podcast design: Saurabh Rai: https://twitter.com/srvbhr

Topics:00:00 Intro01:30 Doug’s story in Search04:55 How Quepid came about10:57 Relevance as product at Shopify: challenge, process, tools, evaluation15:36 Search abandonment in Ecommerce21:30 Rigor in A/B testing23:53 Turn user intent and content meaning into tokens, not words into tokens32:11 Use case for vector search in Maps. What about search in other domains?38:05 Expanding on dense approaches40:52 Sparse, dense, hybrid anyone?48:18 Role of HNSW, scalability and new vector databases vs Elasticsearch / Solr dense search52:12 Doug’s advice to vector database makers58:19 Learning to Rank: how to start, how to collect data with active learning, what are the ML methods and a mindset1:12:10 Blending search and recommendation1:16:08 Search engineer role and key ingredients of managing search projects today1:20:34 What does a Product Manager do on a Search team?1:26:50 The magical question of WHY1:29:08 Doug’s announcementsShow notes:Doug’s course: https://www.getsphere.com/ml-engineering/ml-powered-search?source=Instructor-Other-070922-vector-podUpcoming book: https://www.manning.com/books/ai-powered-search?aaid=1&abid=e47ada24&chan=aipsDoug’s post in Shopify’s blog “Search at Shopify—Range in Data and Engineering is the Future”: https://shopify.engineering/search-at-shopifyDoug’s own blog: https://softwaredoug.com/Using Bayesian optimization for Elasticsearch relevance: https://www.youtube.com/watch?v=yDcYi-ANJwE&t=1sHello LTR: https://github.com/o19s/hello-ltrVector Databases: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696Research: Search abandonment has a lasting impact on brand loyalty: https://cloud.google.com/blog/topics/retail/search-abandonment-impacts-retail-sales-brand-loyaltyQuepid: https://quepid.com/Podcast design: Saurabh Rai [https://twitter.com/srvbhr]

Topics:00:00 Introduction01:12 Malte’s background07:58 NLP crossing paths with Search11:20 Product discovery: early stage repetitive use cases pre-dating Haystack16:25 Acyclic directed graph for modeling a complex search pipeline18:22 Early integrations with Vector Databases20:09 Aha!-use case in Haystack23:23 Capabilities of Haystack today30:11 Deepset Cloud: end-to-end deployment, experiment tracking, observability, evaluation, debugging and communicating with stakeholders39:00 Examples of value for the end-users of Deepset Cloud46:00 Success metrics50:35 Where Haystack is taking us beyond MLOps for search experimentation57:13 Haystack as a smart assistant to guide experiments1:02:49 Multimodality1:05:53 Future of the Vector Search / NLP field: large language models1:15:13 Incorporating knowledge into Language Models & an Open NLP Meetup on this topic1:16:25 The magical question of WHY1:23:47 Announcements from MalteShow notes:- Haystack: https://github.com/deepset-ai/haystack/- Deepset Cloud: https://www.deepset.ai/deepset-cloud- Tutorial: Build Your First QA System: https://haystack.deepset.ai/tutorials/v0.5.0/first-qa-system- Open NLP Meetup on Sep 29th (Nils Reimers talking about “Incorporating New Knowledge Into LMs”): https://www.meetup.com/open-nlp-meetup/events/287159377/- Atlas Paper (Few shot learning with retrieval augmented large language models): https://arxiv.org/abs/2208.03299- Tweet from Patrick Lewis: https://twitter.com/PSH_Lewis/status/1556642671569125378- Zero click search: https://www.searchmetrics.com/glossary/zero-click-searches/Very large LMs:- 540B PaLM by Google: https://lnkd.in/eajsjCMr- 11B Atlas by Meta: https://lnkd.in/eENzNkrG- 20B AlexaTM by Amazon: https://lnkd.in/eyBaZDTy- Players in Vector Search: https://www.youtube.com/watch?v=8IOpgmXf5r8 https://dmitry-kan.medium.com/players-in-vector-search-video-2fd390d00d6- Click Residual: A Query Success Metric: https://observer.wunderwood.org/2022/08/08/click-residual-a-query-success-metric/- Tutorials and papers around incorporating Knowledge into Language Models: https://cs.stanford.edu/people/cgzhu/Podcast design: Saurabh Rai https://twitter.com/srvbhr

00:00 Introduction01:10 Max's deep experience in search and how he transitioned from structured data08:28 Query-term dependence problem and Max's perception of the Vector Search field12:46 Is vector search a solution looking for a problem?20:16 How to move embeddings computation from GPU to CPU and retain GPU latency?27:51 Plug-in neural model into Java? Example with a Hugging Face model33:02 Web-server Mighty and its philosophy35:33 How Mighty compares to in-DB embedding layer, like Weavite or Vespa39:40 The importance of fault-tolerance in search backends43:31 Unit economics of Mighty50:18 Mighty distribution and supported operating systems54:57 The secret sauce behind Mighty's insane fast-ness59:48 What a customer is paying for when buying Mighty1:01:45 How will Max track the usage of Mighty: is it commercial or research use?1:04:39 Role of Open Source Community to grow business1:10:58 Max's vision for Mighty connectors to popular vector databases1:18:09 What tooling is missing beyond Mighty in vector search pipelines1:22:34 Fine-tuning models, metric learning and Max's call for partnerships1:26:37 MLOps perspective of neural pipelines and Mighty's role in it1:30:04 Mighty vs AWS Inferentia vs Hugging Face Infinity1:35:50 What's left in ML for those who are not into Python1:40:50 The philosophical (and magical) question of WHY1:48:15 Announcements from Max25% discount for the first year of using Mighty in your great product / project with promo code VECTOR:https://bit.ly/3QekTWEShow notes:- Max's blog about BERT and search relevance: https://opensourceconnections.com/blog/2019/11/05/understanding-bert-and-search-relevance/- Case study and unit economics of Mighty: https://max.io/blog/encoding-the-federal-register.html- Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696Watch on YouTube: https://youtu.be/LnF4hbl1cE4