Mikhail Shibanov

🧑‍💻🤖 We're rolling out our next-generation custom chips, designed to supercharge AI across Facebook and Instagram. These enhancements mean faster and more accurate ranking and recommendations, improving your experience every day. Dive into how our latest AI innovations are powering better, smarter interactions. #Meta #AI

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New blog post by Alex Monahan: Benchmarking Ourselves over Time at DuckDB The DuckDB team's philosophy is to ensure correctness first, then iterate and optimize to improve performance. This blog explores how this happened over the last three years when DuckDB became approximately 3-25x faster and 10x more scalable. Read more at https://lnkd.in/dnQ7Nya8

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🦁 Leo AI and Ollama bring @NVIDIA RTX-accelerated local LLMs to the Brave browser. 🧑‍💻 Summarize webpages, generate content, run models locally, and more. Learn more in this week's #AIDecoded!

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Our new research report, "Key Value Stores: Adoption Trends Through a Valkey Lens," reveals industry-first insights into customer sentiment regarding Redis's decision to revoke the open source status of its key-value data store software. Explore key findings and takeaways below. https://bit.ly/3Y7Havn via TFiR #Valkey #OpenSource #database

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AVAILABLE NOW! Read the complete series: VERA: The Cloud Native Engine Revolutionizing Apache Flink® This 3-part blog and video series introduces #VERA (Ververica Runtime Assembly), the ultra-high performance engine that powers Ververica's Streaming Data Platform. In this series, you'll: ✔️ Learn why VERA was created. ✔️ Meet the 3 Core Pillars that make VERA such a powerful #streamingdata engine. ✔️ Get to know the capabilities, benefits, and features that VERA provides. ✔️ Discover how VERA removes the complexity of open source #ApacheFlink, all while maintaining 100% compatibility with Flink. Streaming Data projects, whether for #batchprocessing or #realtime use cases can be hard to set up, run, and maintain. Learn how VERA is revolutionizing Apache Flink, and making it easier for businesses to access and use their data. Get started with Part One: From Steam to Stream, An Introduction to VERA: https://hubs.li/Q02LBqVf0

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Curious about whether the recent Redis license change impacts you? Get the details in our latest blog and see how Redis stacks up compared to other open source data technologies >> https://ter.li/lnapcg #opensource #databases

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Follow here or on YouTube for our weekly Foundry & AIP tutorials!

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Real-world data is complex, and traditional large language models fall short when using structured data like prices and product categories. With Superlinked and Redis, you can integrate structured and unstructured data for high-quality information retrieval. This means faster, more accurate recommendations that truly resonate with your customers' needs. Read more here. https://lnkd.in/erngxz-n

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Let's transform raw page view data from Apache Kafka into aggregated insights directly loaded into Databricks Delta tables. 🧱 We explore real-time data aggregation, demonstrating how to downsample page view data across a 10-second time window using just a few SQL queries. By processing data before it reaches Databricks, you can dramatically reduce storage and compute costs while minimizing latency. It's all here 👇 https://lnkd.in/g7QwbSUD

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Managing inventory—both online and in stores—is an extremely complex challenge for retailers. Real-time local inventory search helps companies with things like over and under-stocking, fulfillment, inventory costs, and building localized customer experiences. This is where we come in. See how our geospatial search capabilities can help keep store inventories up-to-date in real-time, process event streams fast, reduce fulfillment costs, and enhance your customers’ experiences: https://lnkd.in/g2PF7qjV 

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We demonstrate how to store data similar to that of a schema-less graph database and execute traversal queries using the latest property graph features of relational databases.

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Have you run into issues with balancing schema flexibility and data consistency as your app grows? With Fauna’s adaptive schema enforcement, you can start with a schemaless collection, progressively add strict schema over time, and evolve it with zero-downtime migrations to meet growing data requirements. This means you gain all the benefits of strict data consistency when you need it—without compromising flexibility in the early stages of development. If you’re curious about how it works, check out our step-by-step tutorial on progressively defining and enforcing document types in Fauna. 👉 Dive into the tutorial here: https://lnkd.in/d78g4i6G #databaseschema #serverless

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Watch Palantir Developer Chris Jeganathan as he builds with AIP in real-time. In this video, he walks through writing TypeScript code to fetch data, how to stream data into AIP, containerizing a TypeScript ingestor using Compute Modules, and using pipeline builder to hydrate the ontology. https://lnkd.in/gxvxNpAX

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🏠🤖 We're pushing the boundaries of AI with our new OpenEQA benchmark! How well can AI understand and navigate physical spaces? Let's explore the challenges and what's next for vision+language models. Check out the details here. #AI #Meta 🌟

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Building resilient Kafka applications? Don't let message failures disrupt your data pipelines!     This blog post explores Spring Kafka's retry mechanism to ensure data integrity and application resilience. Learn about exponential backoff, fixed delays, and dead letter topics (DLT).     Ensure your Kafka apps can handle failures gracefully. Explore our blog for more insights https://lnkd.in/gTCd8D74   #EdstemTechnologies #ApacheKafka #SpringKafka #DataPipelines #FaultTolerance #DevOps

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Learn How to Run Hugging Face Models Programmatically Using Ollama  and #Testcontainers 🤖💻 Dive into the future of AI/ML with easy model deployment 👋👋 Say goodbye to complex setups and hello to seamless integration 👉👉 Docker, Inc blog https://lnkd.in/g_F89xCk by Ignacio López Luna

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We appreciate Yahoo Finance's coverage of the latest MLPerf Storage 1.0 benchmark results. Their report showcases how far we've come in optimizing data infrastructure for AI workloads. https://lnkd.in/ezJysDHB?

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