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Next Tuesday, September 8, we're hosting the September LA Agentic AI Meetup, 5 to 7pm at Gulp in Playa Vista. Come hang out with builders, engineers, founders, and the AI-curious for networking, live

Jörg Schad is the VP of Engineering at @pinecone. He joins @kbal11 to discuss moving beyond RAG with precomputed context, context artifacts, semantic layers, and AI retrieval. @joerg_schad https://sof

The LA Agentic AI Meetup in on the Tech Week calendar. Join us Tuesday, October 13, 5 to 7pm at Gulp in Playa Vista, for a few live demos, drinks, and real people comparing notes on what they're build

A database knows where your data is. A vector database understands what your data means. It finds information based on similarity and meaning, not just exact keywords, powering semantic search, RAG, r

A new release of our Agent Skills and Docs eval library, Cultivar is available! Now, use @typesafeai's Jev model to evaluate how well your agents use docs and Skill against specified tasks in Modal sa

LA Tech Week is coming up in two weeks, and we’re doing our monthly LA Agentic AI Meetup during it. Tuesday, October 13 at Gulp in Playa Vista. 5–7pm. A few live demos, drinks, and a bunch of real peo

LA Tech Week starts Monday, and our monthly LA Agentic AI Meetup is on day two: Tuesday, October 13, 5 to 7pm at Gulp in Playa Vista. Community member PJ Kedilaya is demoing ModelVantage, which tracks

What a great night at #SFTechWeek! We had an incredible time bringing together AI builders, engineers, and founders for our Agentic Knowledge Happy Hour! The evening was filled with great conversation

This article from Apify's blog covers the process of training AI chatbots, likely including techniques for data preparation, model selection, and fine-tuning approaches. The content is shared by Pinec

in 1996 mitzenmacher showed that sampling two backends and picking the better one drops max load exponentially vs. random selection one extra comparison. that's the whole trick we built pinecone assis

Confidently wrong is worse than just wrong. When an AI searches one giant knowledge base for everything, it finds something—and serves it up like it's the answer. That's not a model problem. That's a

Full text search is in Public Preview on Pinecone. Check out the bird search demo built by Senior Developer Advocate, Arjun Patel which indexes 2,000 Wikipedia bird articles in one index across four f

Building search across text, images, audio, and video? Multimodal search uses vector embeddings to retrieve content based on meaning, not just keywords or metadata. Less manual tagging. More flexible

in 1996 mitzenmacher showed that sampling two backends and picking the better one drops max load exponentially vs. random selection one extra comparison. that's the whole trick we built pinecone assis

Nebius AI announced the Agents Blueprint, a framework integrating multiple tools including LangChain Deep Agents, Tavily AI, Pinecone for vector storage, and Guardrails for building AI agents. This bl

Proud to have Pinecone Nexus be a part of this along with our friends at @LangChain, @tavilyai, and @guardrails_ai! Most #AIAgents don't fail because of the model. They fail because of the infrastruct