Exciting news: @satyanadella
, Chairman and CEO of @Microsoft
, will be joining the #DataAISummit keynote virtually for a special pre-recorded conversation with Databricks CEO @alighodsi on the future of AI development and its impact on global productivity: https://
databricks.com/dataaisummit?u
tm_source=twitter&utm_medium=organic-social&utm_scid=701vp00000d7qrxia3
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DATA
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Satya Nadella joins Databricks AI Summit keynote conversation
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Building Live Sentiment Analysis Tools with X Data
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Finally! Given X has unique live data, it’s now much easier to build tools that can give you live sentiment on topics. What would you try to build with this?
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MCP Scaling: Decoupling Data from Logic for Agentic Tools
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We've been building a lot with MCP and figuring out how to get it to work with large responses / real world data. One of the key things we've been finding is that decoupling data from logic (orchestration) is important for scaling up agentic use of tools. Sharing an article
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Data Fabric Architecture Enables Enterprise Data Accessibility
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Organizations need a modular data architecture that supports complex enterprise environments while delivering data access to business users. A Data Fabric connects distributed data to make it usable and accessible. Source @Gartner_inc Link https://
gtnr.it/3F4BDyI via @antgrasso -
Microsoft Recall: AI Integration Threatens Desktop Privacy
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The rushed integration of AI into everything is a major threat to privacy, as we've been saying. Microsoft Recall is a great (not great!) example of exactly why–it screenshots everything on your desktop, to give AI the data to tell you…what you were doing on desktop.
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Embedding Inversion Threat: Decoding Text Without Model Access
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and practically, this is bad for vector databases. this means that even if you fine-tune your own model, and keep the model secret, someone with access to embeddings alone can decode their text embedding inversion without model access
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All Embedding Models Learn The Same Thing
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excited to finally share on arxiv what we've known for a while now:
— dr. jack morris (@jxmnop) 21 mai 2025
All Embedding Models Learn The Same Thing
embeddings from different models are SO similar that we can map between them based on structure alone. without *any* paired data
feels like magic, but it's real:🧵 https://t.co/Cwj1LytGosexcited to finally share on arxiv what we've known for a while now: All Embedding Models Learn The Same Thing embeddings from different models are SO similar that we can map between them based on structure alone. without *any* paired data feels like magic, but it's real:
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Danfoss Standardizes MES and Legacy Systems Integration
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Danfoss began its journey in 2019, standardizing MES & connecting legacy systems. The result? Lower costs, better quality, and end-to-end automation. https://t.co/x9q7kfCOkU
— Lucian Fogoros (@fogoros) 21 mai 2025
Want to learn how? Join @CriticalMFG at #MESI2025! https://t.co/gbDV45Lyyl #sponsored #criticalmfg_iiot pic.twitter.com/XOet0drkOTDanfoss began its journey in 2019, standardizing MES & connecting legacy systems. The result? Lower costs, better quality, and end-to-end automation. https://
buff.ly/ac6oBb6 Want to learn how? Join @CriticalMFG at #MESI2025! https://
buff.ly/hAammgV #sponsored #criticalmfg_iiot -
MES transforms raw manufacturing data into real-time insights
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Raw data without a map is just meaningless coordinates.
Nearly 90% of #manufacturers admit their data lakes lack context.
Use #MES as your context engine. Turn raw data into real-time insights. https://
buff.ly/86jMqrk #sponsored #criticalmfg_iiot #Industry40 @IIoT_World