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  1. 💡 Vector databases explained simply:

    Traditional DB: "Find me rows WHERE name = 'cats'"
    Vector DB: "Find me the 10 most similar things to THIS cat"

    Under the hood:
    → Text → Embedding (1536-dim float array)
    → Store in pgvector / Pinecone / Qdrant
    → Query = cosine similarity search

    For most apps: pgvector (Postgres extension) is enough. No new infra needed.

    #VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

  2. 💡 Vector databases explained simply:

    Traditional DB: "Find me rows WHERE name = 'cats'"
    Vector DB: "Find me the 10 most similar things to THIS cat"

    Under the hood:
    → Text → Embedding (1536-dim float array)
    → Store in pgvector / Pinecone / Qdrant
    → Query = cosine similarity search

    For most apps: pgvector (Postgres extension) is enough. No new infra needed.

    #VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

  3. 💡 Vector databases explained simply:

    Traditional DB: "Find me rows WHERE name = 'cats'"
    Vector DB: "Find me the 10 most similar things to THIS cat"

    Under the hood:
    → Text → Embedding (1536-dim float array)
    → Store in pgvector / Pinecone / Qdrant
    → Query = cosine similarity search

    For most apps: pgvector (Postgres extension) is enough. No new infra needed.

    #VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

  4. 💡 Vector databases explained simply:

    Traditional DB: "Find me rows WHERE name = 'cats'"
    Vector DB: "Find me the 10 most similar things to THIS cat"

    Under the hood:
    → Text → Embedding (1536-dim float array)
    → Store in pgvector / Pinecone / Qdrant
    → Query = cosine similarity search

    For most apps: pgvector (Postgres extension) is enough. No new infra needed.

    #VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

  5. 💡 Vector databases explained simply:

    Traditional DB: "Find me rows WHERE name = 'cats'"
    Vector DB: "Find me the 10 most similar things to THIS cat"

    Under the hood:
    → Text → Embedding (1536-dim float array)
    → Store in pgvector / Pinecone / Qdrant
    → Query = cosine similarity search

    For most apps: pgvector (Postgres extension) is enough. No new infra needed.

    #VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

  6. Escape the proprietary AI cloud tax!

    Moving your vector search from Pinecone to open-source Qdrant on Bare Metal is a massive performance & cost upgrade.

    Our SRE guide on making the switch:
    🔹 In-graph filtering > post-filtering
    🔹 INT8 cuts RAM footprint by 400%
    🔹 Direct NVMe > Network Storage

    Includes full Docker configs & Linux kernel tuning tips to squeeze every drop of IOPS.

    Read the playbook here:
    servermo.com/howto/migrate-pin

    #OpenSource #SelfHosted #Qdrant #DevOps #VectorDB

  7. Escape the proprietary AI cloud tax!

    Moving your vector search from Pinecone to open-source Qdrant on Bare Metal is a massive performance & cost upgrade.

    Our SRE guide on making the switch:
    🔹 In-graph filtering > post-filtering
    🔹 INT8 cuts RAM footprint by 400%
    🔹 Direct NVMe > Network Storage

    Includes full Docker configs & Linux kernel tuning tips to squeeze every drop of IOPS.

    Read the playbook here:
    servermo.com/howto/migrate-pin

    #OpenSource #SelfHosted #Qdrant #DevOps #VectorDB

  8. Escape the proprietary AI cloud tax!

    Moving your vector search from Pinecone to open-source Qdrant on Bare Metal is a massive performance & cost upgrade.

    Our SRE guide on making the switch:
    🔹 In-graph filtering > post-filtering
    🔹 INT8 cuts RAM footprint by 400%
    🔹 Direct NVMe > Network Storage

    Includes full Docker configs & Linux kernel tuning tips to squeeze every drop of IOPS.

    Read the playbook here:
    servermo.com/howto/migrate-pin

    #OpenSource #SelfHosted #Qdrant #DevOps #VectorDB

  9. Interessante Erklärung von Databricks zu Vektordatenbanken.
    🔑 **Kern-Einblick:** Traditionelle DBs finden exakte Treffer, Vektordatenbanken verstehen *Bedeutung* durch Embeddings – essenziell für semantische Suche & KI-Anwendungen.
    - Handhabt hochdimensionale Vektoreffizient
    - Beschleunigt Retrieval-Augmented Generation (RAG)
    - Unterstützt komplexe Ähnlichkeitssuchen

    #VectorDB #MachineLearning #KünstlicheIntelligenz #DataEngineering #Databricks

    🔗 news.google.com/rss/articles/C

  10. Have pushed 0.9.5-dev branch to codeberg of foxing ( codeberg.org/aenertia/foxing/s ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

  11. Have pushed 0.9.5-dev branch to codeberg of foxing ( codeberg.org/aenertia/foxing/s ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

  12. Have pushed 0.9.5-dev branch to codeberg of foxing ( codeberg.org/aenertia/foxing/s ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

  13. Have pushed 0.9.5-dev branch to codeberg of foxing ( codeberg.org/aenertia/foxing/s ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

  14. Have pushed 0.9.5-dev branch to codeberg of foxing ( codeberg.org/aenertia/foxing/s ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

  15. @OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

    My #OpenSearch report highlights:
    ✅ Platform play
    ✅ Search variety
    ✅ Business criteria
    ✅ Security
    And I'd add - it's OPEN SOURCE @linuxfoundation !!
    opensearch.org/gigaom-radar-ve

    #gigaom #vectorDB

  16. @OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

    My report highlights:
    ✅ Platform play
    ✅ Search variety
    ✅ Business criteria
    ✅ Security
    And I'd add - it's OPEN SOURCE @linuxfoundation !!
    opensearch.org/gigaom-radar-ve

  17. @OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

    My #OpenSearch report highlights:
    ✅ Platform play
    ✅ Search variety
    ✅ Business criteria
    ✅ Security
    And I'd add - it's OPEN SOURCE @linuxfoundation !!
    opensearch.org/gigaom-radar-ve

    #gigaom #vectorDB

  18. @OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

    My #OpenSearch report highlights:
    ✅ Platform play
    ✅ Search variety
    ✅ Business criteria
    ✅ Security
    And I'd add - it's OPEN SOURCE @linuxfoundation !!
    opensearch.org/gigaom-radar-ve

    #gigaom #vectorDB

  19. @OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

    My #OpenSearch report highlights:
    ✅ Platform play
    ✅ Search variety
    ✅ Business criteria
    ✅ Security
    And I'd add - it's OPEN SOURCE @linuxfoundation !!
    opensearch.org/gigaom-radar-ve

    #gigaom #vectorDB

  20. 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 usage

    linkedin.com/feed/update/urn:l

    #OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

  21. Stoked seeing the OpenSearch Project featured by Jensen Huang on keynote! 😍

    One of the innovations in V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our 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 usage

    linkedin.com/feed/update/urn:l

  22. 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 usage

    linkedin.com/feed/update/urn:l

    #OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

  23. 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 usage

    linkedin.com/feed/update/urn:l

    #OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

  24. 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 usage

    linkedin.com/feed/update/urn:l

    #OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

  25. 310% throughput increase and 300% latency reduction!
    Great work by the AWS engineers with bulk SIMD brings these performance gains in @OpenSearchProject 's vector search 👏
    And it's all under @linuxfoundation 🤩
    opensearch.org/blog/accelerati

  26. Chunking: an essential concept to understand for Retrieval-Augmented Generation (#RAG). It is the process of dividing large documents into smaller, manageable segments called “chunks.” Effective chunking preserves semantic meaning while ensuring content fits within model context limits.

    Proper chunking is essential, as it directly affects retrieval quality. Well-structured chunks improve precision and support more accurate responses.

    

#OpenSource #devops #vectordb #programming #vector #search

  27. Chunking: an essential concept to understand for Retrieval-Augmented Generation (#RAG). It is the process of dividing large documents into smaller, manageable segments called “chunks.” Effective chunking preserves semantic meaning while ensuring content fits within model context limits.

    Proper chunking is essential, as it directly affects retrieval quality. Well-structured chunks improve precision and support more accurate responses.

    

#OpenSource #devops #vectordb #programming #vector #search

  28. Chunking: an essential concept to understand for Retrieval-Augmented Generation (#RAG). It is the process of dividing large documents into smaller, manageable segments called “chunks.” Effective chunking preserves semantic meaning while ensuring content fits within model context limits.

    Proper chunking is essential, as it directly affects retrieval quality. Well-structured chunks improve precision and support more accurate responses.

    

#OpenSource #devops #vectordb #programming #vector #search

  29. Can't wait to to have a great ride at #JavaLand ! 🇩🇪

    #OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

    If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

    See you 11th March in @JavaLandConf 🎡

    🔸Agenda: meine.doag.org/events/javaland

    🔸Tickets: javaland.eu/

  30. Can't wait to to have a great ride at ! 🇩🇪

    sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

    If you're there and want to learn about search and about the @OpenSearchProject, check out my talk 🙂

    See you 11th March in @JavaLandConf 🎡

    🔸Agenda: meine.doag.org/events/javaland

    🔸Tickets: javaland.eu/

  31. Can't wait to to have a great ride at #JavaLand ! 🇩🇪

    #OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

    If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

    See you 11th March in @JavaLandConf 🎡

    🔸Agenda: meine.doag.org/events/javaland

    🔸Tickets: javaland.eu/

  32. Can't wait to to have a great ride at #JavaLand ! 🇩🇪

    #OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

    If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

    See you 11th March in @JavaLandConf 🎡

    🔸Agenda: meine.doag.org/events/javaland

    🔸Tickets: javaland.eu/

  33. Can't wait to to have a great ride at #JavaLand ! 🇩🇪

    #OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

    If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

    See you 11th March in @JavaLandConf 🎡

    🔸Agenda: meine.doag.org/events/javaland

    🔸Tickets: javaland.eu/

  34. 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!
    youtube.com/watch?v=buKXHi6kFw

    #OpenSearch #vectorSearch #vectorDB #opensource #search

  35. 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!
    youtube.com/watch?v=buKXHi6kFw

  36. 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!
    youtube.com/watch?v=buKXHi6kFw

    #OpenSearch #vectorSearch #vectorDB #opensource #search

  37. 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!
    youtube.com/watch?v=buKXHi6kFw

    #OpenSearch #vectorSearch #vectorDB #opensource #search

  38. 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!
    youtube.com/watch?v=buKXHi6kFw

    #OpenSearch #vectorSearch #vectorDB #opensource #search

  39. Tìm hiểu kiến trúc LLM cục bộ kết hợp **MSSQL** (dữ liệu cấu trúc) và **Vector DB** (dữ liệu phi cấu trúc) với giao diện ChatGPT! Hệ thống kết hợp **RAG pipeline** để xử lý truy vấn:
    - **Tạo câu lệnh SQL từ ngôn ngữ tự nhiên** cho MSSQL
    - **Tìm kiếm ngữ nghĩa** trên dữ liệu tài liệu, email, chính sách qua vector database (FAISS/Qdrant…).
    Đồng thời quản lý quyền, dữ liệu an toàn trong nội mạng.

    #AI #MSSQL #VectorDB #RAG #Chatbot #DữLiệuCấuTrúc #VectorEmbedding #HệThốngLLM #KiếnTrúcPhầnMề

  40. pgedge-vectorizer: #Postgres extension that automatically vectorizes document contents and keeps vector embeddings current when the underlying content changes.

    Unlike other solutions, no external services or third party pipelines are required. It's also 100% open source under the #PostgreSQL license. ✨

    Check it out on GitHub: 👉 github.com/pgEdge/pgedge-vecto

    #programming #vector #vectordatabase #vectorsearch #vectordb #ai #llm #aiengineering #aidev #dba

  41. pgedge-vectorizer: #Postgres extension that automatically vectorizes document contents and keeps vector embeddings current when the underlying content changes.

    Unlike other solutions, no external services or third party pipelines are required. It's also 100% open source under the #PostgreSQL license. ✨

    Check it out on GitHub: 👉 github.com/pgEdge/pgedge-vecto

    #programming #vector #vectordatabase #vectorsearch #vectordb #ai #llm #aiengineering #aidev #dba