Mathstodon

Hacker NewsEmbeddings Are Underrated<a href="https://technicalwriting.dev/ml/embeddings/overview.html" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://technicalwriting.dev/ml/embeddings/overview.html</a><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#HackerNews</a> <a href="https://mastodon.social/tags/Embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Embeddings</a> <a href="https://mastodon.social/tags/Underrated" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Underrated</a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MachineLearning</a> <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DataScience</a> <a href="https://mastodon.social/tags/AIInsights" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AIInsights</a>

Markus EiseleFrom Strings to Semantics: Comparing Text with Java, Quarkus, and Embeddings Learn how to build an AI-powered text similarity service using Quarkus, LangChain4j, and local embedding models. <a href="https://myfear.substack.com/p/java-quarkus-text-embeddings-similarity" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://myfear.substack.com/p/java-quarkus-text-embeddings-similarity</a> <a href="https://mastodon.online/tags/Java" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Java</a> <a href="https://mastodon.online/tags/Quarkus" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Quarkus</a> <a href="https://mastodon.online/tags/Embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Embeddings</a> <a href="https://mastodon.online/tags/Ollama" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Ollama</a> <a href="https://mastodon.online/tags/LangChain4j" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LangChain4j</a>

JMLR'Variance-Aware Estimation of Kernel Mean Embedding', by Geoffrey Wolfer, Pierre Alquier.<a href="http://jmlr.org/papers/v26/23-0161.html" rel="nofollow noopener noreferrer" translate="no" target="_blank">http://jmlr.org/papers/v26/23-0161.html</a> <a href="https://sigmoid.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://sigmoid.social/tags/embedding" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embedding</a> <a href="https://sigmoid.social/tags/empirical" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#empirical</a>

Hacker NewsHNSW index for vector embeddings in approx 500 LOC<a href="https://github.com/dicroce/hnsw" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/dicroce/hnsw</a><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#HackerNews</a> <a href="https://mastodon.social/tags/HNSW" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#HNSW</a> <a href="https://mastodon.social/tags/vector" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#vector</a> <a href="https://mastodon.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#machinelearning</a> <a href="https://mastodon.social/tags/GitHub" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GitHub</a> <a href="https://mastodon.social/tags/500LOC" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#500LOC</a>

MSvanaBig update to my Embeddings Playground. I added support for the first free-to-use embedding model: "all-MiniLM-L6-v2" from Sentence transformers (<a href="https://www.sbert.net/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.sbert.net/</a>).Try the Embeddings playground here: <a href="https://embeddings.svana.name" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://embeddings.svana.name</a><a href="https://mastodon.social/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ai</a> <a href="https://mastodon.social/tags/ml" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ml</a> <a href="https://mastodon.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://mastodon.social/tags/programming" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#programming</a>

FIZ ISE Research GroupWe are very happy that our colleage <a href="https://sigmoid.social/@GenAsefa" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@GenAsefa</a> has contributed the chapter on "Neurosymbolic Methods for Dynamic Knowledge Graphs" for the newly published Handbook on Neurosymbolic AI and Knowledge Graphs together with Mehwish Alam and Pierre-Henri Paris.Handbook: <a href="https://ebooks.iospress.nl/doi/10.3233/FAIA400" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://ebooks.iospress.nl/doi/10.3233/FAIA400</a> our own chapter on arxive: <a href="https://arxiv.org/abs/2409.04572" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://arxiv.org/abs/2409.04572</a><a href="https://sigmoid.social/tags/neurosymbolicAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#neurosymbolicAI</a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://sigmoid.social/tags/generativeAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#generativeAI</a> <a href="https://sigmoid.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLMs</a> <a href="https://sigmoid.social/tags/knowledgegraphs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#knowledgegraphs</a> <a href="https://sigmoid.social/tags/semanticweb" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#semanticweb</a> <a href="https://sigmoid.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://sigmoid.social/tags/graphembeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#graphembeddings</a>

Judith van StegerenShould you use OpenAI (or other closed-source) embeddings?1. Try the lightest embedding model first 2. If it doesn’t work, try a beefier model and do a blind comparison 3. If you are already using a relatively large model, only then try some blind test against a proprietary model. If you really find it that the closed-source model is better for your application, then go for it.Paraphrased from <a href="https://iamnotarobot.substack.com/p/should-you-use-openais-embeddings" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://iamnotarobot.substack.com/p/should-you-use-openais-embeddings</a><a href="https://fosstodon.org/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://fosstodon.org/tags/genai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#genai</a> <a href="https://fosstodon.org/tags/openai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#openai</a> <a href="https://fosstodon.org/tags/ada" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ada</a>

FIZ ISE Research GroupPoster from our colleague <a href="https://blog.epoz.org/" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@epoz</a> from UGent-IMEC Linked Data & Solid course. "Exploding Mittens - Getting to grips with huge SKOS datasets" on semantic embeddings enhanced SPARQL queries for ICONCLASS data. Congrats for the 'best poster' award ;-) poster: <a href="https://zenodo.org/records/14887544" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://zenodo.org/records/14887544</a> iconclass on GitHub: <a href="https://github.com/iconclass" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/iconclass</a><a href="https://sigmoid.social/tags/rdf2vec" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#rdf2vec</a> <a href="https://sigmoid.social/tags/bert" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#bert</a> <a href="https://sigmoid.social/tags/llm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#llm</a> <a href="https://sigmoid.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://sigmoid.social/tags/iconclass" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#iconclass</a> <a href="https://sigmoid.social/tags/semanticweb" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#semanticweb</a> <a href="https://sigmoid.social/tags/lod" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#lod</a> <a href="https://sigmoid.social/tags/linkeddata" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#linkeddata</a> <a href="https://sigmoid.social/tags/knowledgegraphs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#knowledgegraphs</a> <a href="https://sigmoid.social/tags/dh" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#dh</a> <a href="https://nfdi.social/@nfdi4culture" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@nfdi4culture</a> <a href="https://wisskomm.social/@fiz_karlsruhe" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@fiz_karlsruhe</a> <a href="https://sigmoid.social/tags/iconclass" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#iconclass</a>

lqdevNomic Embed Text V2: An Open Source, Multilingual, Mixture-of-Experts Embedding Model <a href="https://toot.lqdev.tech/tags/nomic" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#nomic</a> <a href="https://toot.lqdev.tech/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ai</a> <a href="https://toot.lqdev.tech/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://www.luisquintanilla.me/feed/nomic-embed-text-v2?utm_medium=feed" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.luisquintanilla.me/feed/nomic-embed-text-v2?utm_medium=feed</a>

Jennifer LinHere's a new post on my first encounter with building a simple deep learning model on manually-compiled adverse drug reactions data (thanks to <a href="https://mstdn.science/@baoilleach" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@baoilleach</a> for feedback) - <a href="https://jhylin.github.io/Data_in_life_blog/posts/22_Simple_dnn_adrs/2_ADR_regressor.html" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://jhylin.github.io/Data_in_life_blog/posts/22_Simple_dnn_adrs/2_ADR_regressor.html</a>Notes re. data - <a href="https://jhylin.github.io/Data_in_life_blog/posts/22_Simple_dnn_adrs/1_ADR_data.html" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://jhylin.github.io/Data_in_life_blog/posts/22_Simple_dnn_adrs/1_ADR_data.html</a><a href="https://fosstodon.org/tags/PyTorch" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PyTorch</a> <a href="https://fosstodon.org/tags/RDKit" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#RDKit</a> <a href="https://fosstodon.org/tags/ChEMBL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ChEMBL</a> <a href="https://fosstodon.org/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://fosstodon.org/tags/cheminformatics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#cheminformatics</a>

Mark IgraIs there a consensus process or good paper on state of the art on using <a href="https://sciences.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> & <a href="https://sciences.social/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a> to do the kinds of things that were being done with topic models? I imagine for tasks with pre-defined classifications, prompts are sufficient, but any recommendations for identifying latent classes? After reading the paper below I think I'll want to use local models. <a href="https://sciences.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#machinelearning</a> <a href="https://drive.google.com/file/d/1wNDIkMZfAGoh4Oaojrgll9SPg3eT-YXz/view" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://drive.google.com/file/d/1wNDIkMZfAGoh4Oaojrgll9SPg3eT-YXz/view</a>

marmelab🗞️ Great read on binary vector embeddings & why they are so impressive.In short, they can retain 95+% retrieval accuracy with 32x compression and ~25x retrieval speedup. 🤯🔗 <a href="https://emschwartz.me/binary-vector-embeddings-are-so-cool/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://emschwartz.me/binary-vector-embeddings-are-so-cool/</a> ✍️ Evan Schwartz <a href="https://mastodon.social/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ai</a> <a href="https://mastodon.social/tags/appreciation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#appreciation</a> <a href="https://mastodon.social/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a> <a href="https://mastodon.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> <a href="https://mastodon.social/tags/scour" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#scour</a> <a href="https://mastodon.social/tags/search" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#search</a>

Alessio Pomaro🧠 Sentiamo sempre più spesso parlare di <a href="https://mastodon.uno/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a>: di cosa si tratta, come si generano, e come possono essere utili nei flussi operativi? 🔗 Una spiegazione semplice, con alcuni esempi di utilizzo: <a href="https://www.alessiopomaro.it/embeddings-cosa-sono-esempi/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.alessiopomaro.it/embeddings-cosa-sono-esempi/</a>. ✨ Facciamo anche alcune importanti riflessioni sull'importanza della consapevolezza di questi sistemi per ottenere performance. <a href="https://mastodon.uno/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://mastodon.uno/tags/GenAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GenAI</a> <a href="https://mastodon.uno/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GenerativeAI</a> <a href="https://mastodon.uno/tags/IntelligenzaArtificiale" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#IntelligenzaArtificiale</a> <a href="https://mastodon.uno/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a>

Andrew Spirit Wooldridge ⚔️Embeddings are cool <a href="https://technicalwriting.dev/data/embeddings.html" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://technicalwriting.dev/data/embeddings.html</a> <a href="https://social.lol/tags/llm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#llm</a> <a href="https://social.lol/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a>

Alessio Pomaro🐸 Screaming Frog introduce le API per l'interfacciamento con i modelli di <a href="https://mastodon.uno/tags/OpenAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#OpenAI</a>, <a href="https://mastodon.uno/tags/Google" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Google</a> e con <a href="https://mastodon.uno/tags/Ollama" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Ollama</a>.  ✨ Lavora sull'HTML salvato in fase di scansione, mentre nella versione precedente si usavano snippet JavaScript personalizzati eseguiti durante il rendering delle pagine. 👉 È possibile generare <a href="https://mastodon.uno/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> e contenuti con prompt personalizzati su contesti selezionabili (attraverso estrattori predefiniti e custom).<a href="https://mastodon.uno/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://mastodon.uno/tags/GenAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GenAI</a> <a href="https://mastodon.uno/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GenerativeAI</a> <a href="https://mastodon.uno/tags/IntelligenzaArtificiale" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#IntelligenzaArtificiale</a> <a href="https://mastodon.uno/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a> <a href="https://mastodon.uno/tags/SEO" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#SEO</a>

michabbb🔍 <a href="https://social.vivaldi.net/tags/txtai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#txtai</a> - All-in-one <a href="https://social.vivaldi.net/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> database combining vector indexes, graph networks & relational databases💡 Key Features: • Vector search with SQL support, object storage, topic modeling & multimodal indexing for text, documents, audio, images & video • Built-in <a href="https://social.vivaldi.net/tags/RAG" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#RAG</a> capabilities with citation support & autonomous <a href="https://social.vivaldi.net/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> agents for complex problem-solving • <a href="https://social.vivaldi.net/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a> orchestration supporting multiple frameworks including <a href="https://social.vivaldi.net/tags/HuggingFace" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#HuggingFace</a>, <a href="https://social.vivaldi.net/tags/OpenAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#OpenAI</a> & AWS Bedrock • Seamless integration with <a href="https://social.vivaldi.net/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Python</a> 3.9+, built on <a href="https://social.vivaldi.net/tags/FastAPI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#FastAPI</a> & Sentence Transformers🛠️ Technical Highlights: • Supports multiple programming languages through API bindings (<a href="https://social.vivaldi.net/tags/JavaScript" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#JavaScript</a>, <a href="https://social.vivaldi.net/tags/Java" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Java</a>, <a href="https://social.vivaldi.net/tags/Rust" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Rust</a>, <a href="https://social.vivaldi.net/tags/Go" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Go</a>) • Easy deployment: run locally or scale with container orchestration • <a href="https://social.vivaldi.net/tags/opensource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#opensource</a> under Apache 2.0 license • Minimal setup: installation via pip or Docker🔄 Use Cases: • Semantic search applications • Knowledge base construction • Multi-model workflows • Speech-to-speech processing • Document analysis & summarizationLearn more: <a href="https://github.com/neuml/txtai" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/neuml/txtai</a>

Alessio Pomaro🧠 Ieri, al Festival Biblico Tech, la protagonista è stata l'<a href="https://mastodon.uno/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a>, ma soprattutto la riflessione e lo spirito critico. ⭐ Con una grande conduzione di Massimo Cerofolini e Roberta Rocelli, e con compagni di viaggio d'eccezione. 💡 Porto a casa nuovi stimoli, nuovi pensieri, e, da buon nerd, un test da mettere in atto sugli <a href="https://mastodon.uno/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> e la valutazione dei bias dei <a href="https://mastodon.uno/tags/LLM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#LLM</a>, discusso con Paolo Benanti. <a href="https://mastodon.uno/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://mastodon.uno/tags/GenAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GenAI</a> <a href="https://mastodon.uno/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GenerativeAI</a> <a href="https://mastodon.uno/tags/IntelligenzaArtificiale" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#IntelligenzaArtificiale</a> <a href="https://mastodon.uno/tags/futuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#futuro</a>

रञ्जित (Ranjit Mathew)Wasn’t this…obvious? 🤔“Vector Databases Are The Wrong Abstraction”, Timescale (<a href="https://www.timescale.com/blog/vector-databases-are-the-wrong-abstraction/" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://www.timescale.com/blog/vector-databases-are-the-wrong-abstraction/</a>).Via HN: <a href="https://news.ycombinator.com/item?id=41985176" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://news.ycombinator.com/item?id=41985176</a><a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#MachineLearning</a> <a href="https://mastodon.social/tags/Databases" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Databases</a> <a href="https://mastodon.social/tags/Embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#Embeddings</a> <a href="https://mastodon.social/tags/VectorDB" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#VectorDB</a> <a href="https://mastodon.social/tags/DB" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#DB</a> <a href="https://mastodon.social/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ML</a> <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://mastodon.social/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ArtificialIntelligence</a> <a href="https://mastodon.social/tags/VectorDatabases" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#VectorDatabases</a>

Talk to Me About TechBrand new <a href="https://hachyderm.io/tags/OpenSource" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#OpenSource</a> tool for <a href="https://hachyderm.io/tags/PostgreSQL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#PostgreSQL</a> - pgai Vectorizer - just launched today from <a href="https://hachyderm.io/tags/TimescaleDB" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#TimescaleDB</a>! Manage <a href="https://hachyderm.io/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a> with just one <a href="https://hachyderm.io/tags/SQL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#SQL</a> command to keep embeddings in sync with your data in a far easier fashion. Learn more on the <a href="https://hachyderm.io/tags/GitHub" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GitHub</a> repository here: <a href="https://github.com/timescale/pgai/blob/main/docs/vectorizer.md" rel="nofollow noopener noreferrer" translate="no" target="_blank">https://github.com/timescale/pgai/blob/main/docs/vectorizer.md</a><a href="https://hachyderm.io/tags/postgres" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#postgres</a> <a href="https://hachyderm.io/tags/timescale" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#timescale</a> <a href="https://hachyderm.io/tags/pgai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#pgai</a> <a href="https://hachyderm.io/tags/pgvector" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#pgvector</a> <a href="https://hachyderm.io/tags/vector" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#vector</a> <a href="https://hachyderm.io/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#ai</a> <a href="https://hachyderm.io/tags/llama" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#llama</a>

Alessio Pomaro📆 Domani, all'Advanced SEO Tool vedremo una pillola tecnica dal titolo "Embeddings e SEO: è QUASI magia". ✨ È possibile rimuovere quel "QUASI"? Secondo me sì.. con la consapevolezza di questi strumenti, che proveremo ad acquisire.  ⚙️ Vedremo esempi pratici di utilizzo,  test e considerazioni. 💡 Per poi scoprire che non si tratta di "magia"!<a href="https://mastodon.uno/tags/advSEOTool" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#advSEOTool</a> <a href="https://mastodon.uno/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#AI</a> <a href="https://mastodon.uno/tags/SEO" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#SEO</a> <a href="https://mastodon.uno/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#GenerativeAI</a> <a href="https://mastodon.uno/tags/embeddings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#embeddings</a>

Recent searches

Search options

Administered by:

Server stats:

#embeddings