#vectorsearch — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #vectorsearch, aggregated by home.social.
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StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).
Biggest win is dropping the ONNX dependency.
Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.
It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.
The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.
So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.
StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.
Now out in Release Candidate https://github.com/scottgal/stylobot/releases
Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)
#BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource
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StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).
Biggest win is dropping the ONNX dependency.
Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.
It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.
The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.
So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.
StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.
Now out in Release Candidate https://github.com/scottgal/stylobot/releases
Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)
#BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource
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StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).
Biggest win is dropping the ONNX dependency.
Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.
It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.
The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.
So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.
StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.
Now out in Release Candidate https://github.com/scottgal/stylobot/releases
Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)
#BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource
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StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).
Biggest win is dropping the ONNX dependency.
Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.
It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.
The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.
So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.
StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.
Now out in Release Candidate https://github.com/scottgal/stylobot/releases
Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)
#BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource
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StyloBot free day as I ran myself ragged trying to get it going in my free time (very little of which I HAD finishing up 2x contracts!).
Biggest win is dropping the ONNX dependency.
Earlier versions used ONNX embeddings as a shortcut: turn a client signature into a vector and compare it.
It worked, but it was never quite the right abstraction. Embeddings are built for language. StyloBot’s inputs are behavioural structures.
The new version defines that behavioural vector space directly. Requests, sessions, browsers, bots, scrapers, and odd clients are placed into a real StyloBot-native space. The system ships with archetype centroids, then adapts those centroids to the actual traffic it sees.
So instead of asking a model what a client 'means', StyloBot learns what your traffic looks like.
StyloBot is REALLY a conceptually unfolded ML model so it sort of trains itself on real traffic around centroids and updates as it goes. It's ODD.
Now out in Release Candidate https://github.com/scottgal/stylobot/releases
Plan is still for full release June 1st but the FOSS client MAY reach RTM quality before that (lots of manual testing!)
#BotDetection #CyberSecurity #DotNet #SQLiteVec #VectorSearch #BehaviouralInference #AIInfrastructure #OpenSource
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Qdrant's vector search powers Sapu's AI platform to index 28 million PubMed abstracts, accelerating cancer research and contributing to peer-reviewed publications. #AI #VectorSearch
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#VirtualThreads aren’t just a #Java hype feature. This article shows them powering agent calls safely in production-style #Microservices—with fallback + observability.
Steal the blueprint by @sibaspadhi: https://javapro.io/2026/01/22/java-25-genai-a-new-era-for-microservices-in-finance/
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📖 Perfect prep for this #JCON2025 workshop:
🔹 Building Powerful GenAI Apps with Pure Java (16:00)@RichardFichtner’s article “Build Vector Database Apps with Pure Java”
👉 https://javapro.io/2026/04/02/build-vector-database-apps-with-pure-java/#Java #EclipseStore #MicroStream #GenAI #LLM #Vectors #VectorSearch
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📖 Perfect prep for this #JCON2025 workshop:
🔹 Building Powerful GenAI Apps with Pure Java (16:00)@RichardFichtner’s article “Build Vector Database Apps with Pure Java”
👉 https://javapro.io/2026/04/02/build-vector-database-apps-with-pure-java/#Java #EclipseStore #MicroStream #GenAI #LLM #Vectors #VectorSearch
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📖 Perfect prep for this #JCON2025 workshop:
🔹 Building Powerful GenAI Apps with Pure Java (16:00)@RichardFichtner’s article “Build Vector Database Apps with Pure Java”
👉 https://javapro.io/2026/04/02/build-vector-database-apps-with-pure-java/#Java #EclipseStore #MicroStream #GenAI #LLM #Vectors #VectorSearch
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📖 Perfect prep for this #JCON2025 workshop:
🔹 Building Powerful GenAI Apps with Pure Java (16:00)@RichardFichtner’s article “Build Vector Database Apps with Pure Java”
👉 https://javapro.io/2026/04/02/build-vector-database-apps-with-pure-java/#Java #EclipseStore #MicroStream #GenAI #LLM #Vectors #VectorSearch
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📖 Perfect prep for this #JCON2025 workshop:
🔹 Building Powerful GenAI Apps with Pure Java (16:00)@RichardFichtner’s article “Build Vector Database Apps with Pure Java”
👉 https://javapro.io/2026/04/02/build-vector-database-apps-with-pure-java/#Java #EclipseStore #MicroStream #GenAI #LLM #Vectors #VectorSearch
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🔥 Dive into powerful #GenAI apps with pure #Java — hands-on at #JCON2026! 2h live coding with CTO @FHHabermann – no extra infra, just #JVector + #EclipseStore. #VectorSearch + persistence in one stack.
🎟️ Tickets: https://2026.europe.jcon.one/workshops
⚠️ Limited spots – seats almost sold out -
🔥 Dive into powerful #GenAI apps with pure #Java — hands-on at #JCON2026! 2h live coding with CTO @FHHabermann – no extra infra, just #JVector + #EclipseStore. #VectorSearch + persistence in one stack.
🎟️ Tickets: https://2026.europe.jcon.one/workshops
⚠️ Limited spots – seats almost sold out -
Why are #GenAI systems still slow—despite #Vector DBs, caches & scaling? Because architecture is fragmented. Gerald K. breaks down a #JavaNative alternative: #VectorSearch, state & Persistence in one model. Understand the trade-offs before scaling further: https://javapro.io/2026/04/08/high-performance-vector-search-grids-with-java/
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🔥 Dive into powerful #GenAI apps with pure #Java — hands-on at #JCON2026! 2h live coding with CTO @FHHabermann – no extra infra, just #JVector + #EclipseStore. #VectorSearch + persistence in one stack.
🎟️ Tickets: https://2026.europe.jcon.one/workshops
⚠️ Limited spots – seats almost sold out -
🔥 Dive into powerful #GenAI apps with pure #Java — hands-on at #JCON2026! 2h live coding with CTO @FHHabermann – no extra infra, just #JVector + #EclipseStore. #VectorSearch + persistence in one stack.
🎟️ Tickets: https://2026.europe.jcon.one/workshops
⚠️ Limited spots – seats almost sold out -
I spent some time trying to make search behavior visible in one small Quarkus app.
Full-text is good at exact terms. Vector search helps when user language and catalog language drift apart. Hybrid is usually the one I’d trust first in a real product search.
This article walks through all three with Quarkus, PostgreSQL, Elasticsearch, Hibernate Search, and local embeddings.
https://www.the-main-thread.com/p/full-text-vector-hybrid-search-quarkus-java
#Java #Quarkus #PostgreSQL #Elasticsearch #SemanticSearch #HibernateSearch #VectorSearch
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Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍
One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usagehttps://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/
#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb
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Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍
One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usagehttps://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/
#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb
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Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍
One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usagehttps://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/
#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb
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Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍
One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usagehttps://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/
#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb
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Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍
One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usagehttps://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/
#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb
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Agents are now favoring vector search over classic RAG, as memory frameworks shift to vector storage for rapid similarity lookup. Learn how this changes retrieval infrastructure for LLM‑powered agents and what it means for future AI memory design. #VectorSearch #RAG #AIMemory #AgentSystems
🔗 https://aidailypost.com/news/agents-favor-vector-search-over-rag-noting-memory-frameworks-use
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Agents are now favoring vector search over classic RAG, as memory frameworks shift to vector storage for rapid similarity lookup. Learn how this changes retrieval infrastructure for LLM‑powered agents and what it means for future AI memory design. #VectorSearch #RAG #AIMemory #AgentSystems
🔗 https://aidailypost.com/news/agents-favor-vector-search-over-rag-noting-memory-frameworks-use
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Agents are now favoring vector search over classic RAG, as memory frameworks shift to vector storage for rapid similarity lookup. Learn how this changes retrieval infrastructure for LLM‑powered agents and what it means for future AI memory design. #VectorSearch #RAG #AIMemory #AgentSystems
🔗 https://aidailypost.com/news/agents-favor-vector-search-over-rag-noting-memory-frameworks-use
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https://www.europesays.com/ie/380208/ Google debuts Gemini Embedding 2 multimodal AI model #AccessControl #ComputerVision #CustomerSupport #DataClassification #DataPrivacy #DeveloperTools #DigitalTransformation #Éire #Google #GoogleCloud #GoogleGemini #Haystack #IE #Ireland #KnowledgeManagement #LangChain #LargeLanguageModels(LLMs) #MachineLearning(ML) #NaturalLanguageProcessing(NLP) #Technology #UnstructuredData #VectorSearch
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SurrealDB 3.0 now packs agent memory, business logic, and multimodal data into a single Rust‑powered engine. It blends graph queries, vector search and retrieval‑augmented generation, letting AI agents store context and act without juggling separate stores. Dive into the details and see how this open‑source DB could reshape your stack. #SurrealDB #RetrievalAugmentedGeneration #VectorSearch #MultiModalData
🔗 https://aidailypost.com/news/surrealdb-30-stores-agent-memory-business-logic-multimodal-data-one-db
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SurrealDB 3.0 now packs agent memory, business logic, and multimodal data into a single Rust‑powered engine. It blends graph queries, vector search and retrieval‑augmented generation, letting AI agents store context and act without juggling separate stores. Dive into the details and see how this open‑source DB could reshape your stack. #SurrealDB #RetrievalAugmentedGeneration #VectorSearch #MultiModalData
🔗 https://aidailypost.com/news/surrealdb-30-stores-agent-memory-business-logic-multimodal-data-one-db
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SurrealDB 3.0 now packs agent memory, business logic, and multimodal data into a single Rust‑powered engine. It blends graph queries, vector search and retrieval‑augmented generation, letting AI agents store context and act without juggling separate stores. Dive into the details and see how this open‑source DB could reshape your stack. #SurrealDB #RetrievalAugmentedGeneration #VectorSearch #MultiModalData
🔗 https://aidailypost.com/news/surrealdb-30-stores-agent-memory-business-logic-multimodal-data-one-db
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The first beta of #EclipseStore v4 is now online — featuring the new Vector Index for GigaMap. HNSW-based similarity search with persistent storage, on-disk scalability, PQ compression & more.
Docs: https://docs.eclipsestore.io/manual/gigamap/indexing/jvector/index.html
Code: https://github.com/eclipse-store/store/tree/main/gigamap/jvector -
The first beta of #EclipseStore v4 is now online — featuring the new Vector Index for GigaMap. HNSW-based similarity search with persistent storage, on-disk scalability, PQ compression & more.
Docs: https://docs.eclipsestore.io/manual/gigamap/indexing/jvector/index.html
Code: https://github.com/eclipse-store/store/tree/main/gigamap/jvector -
The first beta of #EclipseStore v4 is now online — featuring the new Vector Index for GigaMap. HNSW-based similarity search with persistent storage, on-disk scalability, PQ compression & more.
Docs: https://docs.eclipsestore.io/manual/gigamap/indexing/jvector/index.html
Code: https://github.com/eclipse-store/store/tree/main/gigamap/jvector -
The first beta of #EclipseStore v4 is now online — featuring the new Vector Index for GigaMap. HNSW-based similarity search with persistent storage, on-disk scalability, PQ compression & more.
Docs: https://docs.eclipsestore.io/manual/gigamap/indexing/jvector/index.html
Code: https://github.com/eclipse-store/store/tree/main/gigamap/jvector -
Secure and Intelligent: Queryable Encryption and Vector Search in MongoDB EF Core Provider
https://devblogs.microsoft.com/dotnet/mongodb-efcore-provider-queryable-encryption-vector-search/#microsoft #NET #Entity_Framework #efcore #mongodb #rag #vectorsearch
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How did Java go from applets to #SpringData & vector #Databases? From EJBs to JPA, the tools have evolved. What’s next in a world of #AI & #VectorSearch?
Raphael DeLio & Brian Sam-Bodden explore the journey ahead.Read now: https://javapro.io/2025/05/21/a-look-back-at-javas-30-year-journey-with-databases/
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#VirtualThreads aren’t just a #Java hype feature. This article shows them powering agent calls safely in production-style #Microservices—with fallback + observability.
Steal the blueprint by @sibaspadhi: https://javapro.io/2026/01/22/java-25-genai-a-new-era-for-microservices-in-finance/
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via @dotnet : Secure and Intelligent: Queryable Encryption and Vector Search in MongoDB EF Core Provider
https://ift.tt/sOE3jLd
#MongoDB #EFCore #QueryableEncryption #VectorSearch #DotNet #CSharp #DataSecurity #DataPrivacy #SoftwareDevelopment #DeveloperExperien… -
ArcadeDB v26.1.1 is out! New Native OpenCypher Engine, huge LSM Vector updates (Quantization, PQ and much more), 92 total issues resolved (!) github.com/ArcadeData/a... #ArcadeDB #GraphDB #OpenCypher #VectorSearch #Database #OpenSource
Release 26.1.1 · ArcadeData/ar... -
🔍 If you're interested in language models and finding the right info fast, check out 💻 :
Our revised QuickStart tutorial for Weaviate! 🚀
We worked hard on it and think you’ll like it ☺️
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🔎🔥Summoning all #dataengineers, #AI enthusiasts, and #RAG masterminds - join us at #VSCON25 June 6th to expand your knowledge and network with global developers from #Google, #Oracle, and #Microsoft!
Sign up at: http://vsearchcon.com
#tech #vectorsearch #postgresql #pgvector #LLMs #MySQL #Vespa #Couchbase
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🎉 CrateDB v5.5 is out on the stable channels and it's packed with exiting updates!
Discover the power of vector storage and similarity search features, revolutionizing complex data analytics, pattern recognition, and AI 🚀 Try out the new drop column feature and much more!
Find out more about what's new👇
https://hubs.ly/Q0283pLg0Download CrateDB https://hubs.ly/Q0283pbF0 🐐
#cratedb #db #database #vectordatabase #vectordb #vector #vectorsearch #similaritysearch #ai #ml #data #dataanalytics
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Implementing RAG from scratch with Python, Qdrant, and Docling
https://techlife.blog/posts/implementing-rag-from-scratch-qdrant/
#RAG #VectorSearch #Qdrant #Embeddings #SemanticSearch #LLM #Python
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Guest @FranckPachot from #yugabyte joins our very own @noctarius2k in this episode of the weekly, 20 min #CloudCommute #podcast, talking about #distributedsql, #postgresql , #vectordatabases, and more. Tune in!
The 🎙️ is available on Spotify, iTunes, Pandora, Amazon Music, and more.
🎥👉 https://youtu.be/1EAKqwcP2SY
#vectordatabase #vectorsearch #vectordb #database #databases #postgres #postgressql
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I joined InstaBlinks podcast to talk about vector search, difference from lexical search, and how the @OpenSearchProject facilitates both in a hybrid model.
Thanks NetApp Instaclustr for having me!
https://www.youtube.com/watch?v=buKXHi6kFwY&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=63
