SurrealDB (@SurrealDB) - Twitter Profili | Zamantika Mersobahis Locabet

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SurrealDB@SurrealDB·11 Mar

SurrealDB Cloud is now on the AWS Marketplace! Deploy our fully managed multi-model database service with consolidated billing, faster procurement, and production-ready from day one. If you build on AWS, adoption just got a lot easier. Learn more. 👉 sdb.li/40pl8EG @AWS_Partners

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Chris Bruce@chrisabruce·9h

Just launched slatehub.com built entirely by single person, uses #rustlang and @SurrealDB, I have it running on dedicated servers using #Dockploy. It costs around $30/mo to operate this and can scale a tremendous amount before I would have to pay more. All server side rendering with htmx (#datastar). I hope to publish a detailed case study why a stack like this, in age of AI, is more ideal than JS/TS and using cloud services. Next step marketing.

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SurrealDB@SurrealDB·11h

Want to learn more? Check out the GitHub repo: github.com/j-d0g/taro

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SurrealDB@SurrealDB·11h

Team Taro took 1st place at the Agents & Knowledge Graphs Hackathon in London, hosted by @LangChain Community and SurrealDB. 🏆 Congrats to Jordan Tran, Baran Ibrahim, and Yitong (Charlotte) Gong who built a LangGraph ReAct agent on a unified SurrealDB backend. @LangChain_OSS

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SurrealDB@SurrealDB·13h

.@digitalkin_ai has built an Agentic Mesh ecosystem for AI agents. Curious why they chose our multi-model database to power the single responsive data layer for memory, cross-agent coordination and live UI state? Find out how it works. 👉 sdb.li/4byjm9n

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SurrealDB@SurrealDB·1d

How many databases does your team maintain? Most stacks carry more than they need. Discover how SurrealDB consolidates multi-model workloads, drawing from case studies and performance comparisons. See where it fits into your stack. 👉 sdb.li/4bPI4Dl

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SurrealDB@SurrealDB·2d

#NVIDIAGTC is live, and we're at booth #4026! Come meet founders @tobiemh & @jaimemh, and chat with Matthew Penaroza & Ignacio Paz to learn why SurrealDB is becoming the go-to database for AI agents, handling core state, contextual memory, and connected data in one place. 👋

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SurrealDB@SurrealDB·2d

Master the five core parts of building a production-ready database with the SurrealDB Fundamentals course. Start building your project today with practical, hands-on learning. 👉 sdb.li/4sOn06y

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SurrealDB@SurrealDB·3d

Did you know that in SurrealQL, the search::analyze() function lets you test a full-text analyzer to verify its output without needing to first define an index? Check our docs for more examples. 👉 sdb.li/4brpjoy

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SurrealDB@SurrealDB·5d

💜 Dev quote of the week.

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SurrealDB@SurrealDB·6d

Is your data stack ready for AI agents? @tobiemh discusses with @thenewstack why stitching together multiple databases adds complexity that holds AI agents back. 👉 sdb.li/4sK8EU9

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SurrealDB@SurrealDB·12 Mar

If you're exploring the context layer for AI agents, you probably have a knowledge graph and are thinking about how to leverage it. Check out these two SurrealQL query patterns and see what your agents have been missing. 👉 sdb.li/46VIiWT

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SurrealDB@SurrealDB·12 Mar

.@polyaivoice needed a fast and open way for customers to bring their own knowledge bases into their customer service AI agents. Integrating SurrealDB into their Agent Studio platform made it possible. Learn how. 👉 sdb.li/3P1qFyM

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Tobie Morgan Hitchcock@tobiemh·12 Mar

Big milestone: @SurrealDB Cloud is live on the @awscloud Marketplace. For AWS teams, this means no procurement friction, consolidated billing, and a fully managed multi-model DB ready to go. The barrier just got a lot lower. 🚀

SurrealDB@SurrealDB

SurrealDB Cloud is now on the AWS Marketplace! Deploy our fully managed multi-model database service with consolidated billing, faster procurement, and production-ready from day one. If you build on AWS, adoption just got a lot easier. Learn more. 👉 sdb.li/40pl8EG @AWS_Partners

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SurrealDB@SurrealDB·11 Mar

Ready for a different kind of database walkthrough? Build your understanding of our multi-model database and the key concepts with a fun, scenic tour set in the fictional city of Riverdale. Start your tour today. 👉 sdb.li/3NIMxhu

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Tobie Morgan Hitchcock@tobiemh·10 Mar

Love seeing the @llama_index ecosystem push the frontier 🦙🚀 Their new audio-kb demo uses @GoogleDeepMind Gemini Embedding 2 for 3072-dim vectors - stored and indexed locally in @SurrealDB with HNSW. Multi-model databases aren't just the future. They're what people are shipping with right now.

LlamaIndex 🦙@llama_index

🚀 The team at @GoogleDeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality. 👩‍💻 We built a demo showing how to integrate it across the LlamaIndex ecosystem, from LlamaParse to LlamaAgents: 𝗮𝘂𝗱𝗶𝗼-𝗸𝗯, a knowledge base for your audio notes. With audio-kb, you can: 🔹 Upload an MP3 or record directly from your terminal 🔹 LlamaParse extracts the transcript from the audio 🔹 Gemini Embedding 2 generates embeddings 🔹 Metadata + vectors are stored in @SurrealDB and indexed with HNSW 🔍 Once ingested, you can search all your audio notes directly from the terminal. 🎙️ Perfect for turning voice memos, meetings, or lectures into a searchable knowledge base. 📖 Full blog: llamaindex.ai/blog/build-a-s… 💻 GitHub: github.com/run-llama/audi… ⚡ Try LlamaParse: cloud.llamaindex.ai/signup

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SurrealDB@SurrealDB·10 Mar

Gemini Embedding 2 just dropped from @GoogleDeepMind. 3072 dimensions. State of the art semantic quality. The @llama_index team built audio-kb on top of it, an end to end knowledge base for your audio notes. The stack: • LlamaParse for audio transcription • Gemini Embedding 2 for vector generation • @SurrealDB for storage and HNSW indexing • LlamaIndex Workflows to orchestrate the whole pipeline Record a voice memo. Search it from your terminal. This is what AI native infrastructure looks like in practice. Thank you @llama_index and @itsclelia!

LlamaIndex 🦙@llama_index

🚀 The team at @GoogleDeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality. 👩‍💻 We built a demo showing how to integrate it across the LlamaIndex ecosystem, from LlamaParse to LlamaAgents: 𝗮𝘂𝗱𝗶𝗼-𝗸𝗯, a knowledge base for your audio notes. With audio-kb, you can: 🔹 Upload an MP3 or record directly from your terminal 🔹 LlamaParse extracts the transcript from the audio 🔹 Gemini Embedding 2 generates embeddings 🔹 Metadata + vectors are stored in @SurrealDB and indexed with HNSW 🔍 Once ingested, you can search all your audio notes directly from the terminal. 🎙️ Perfect for turning voice memos, meetings, or lectures into a searchable knowledge base. 📖 Full blog: llamaindex.ai/blog/build-a-s… 💻 GitHub: github.com/run-llama/audi… ⚡ Try LlamaParse: cloud.llamaindex.ai/signup

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SurrealDB@SurrealDB·10 Mar

@coderkr Hi, head over here: surrealdb.com/docs/surrealql…

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