GenAI's Excel moment might actually just be… spreadsheets. For most businesses, that's still how they manage their data. There's a ton of value to be created there. Try it out here:
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AI Agents: GenSpark’s AI Sheets for Spreadsheet Analysis
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Everybody is doing AI agents these days. Here's a great example of an application that gets it right: @genspark_ai AI Sheets lets you literally talk to your spreadsheets.
— François Chollet (@fchollet) 19 mai 2025
Upload your files, ask any data analysis question, and it automatically analyzes everything, pulls the info,… pic.twitter.com/rEG48pwEj8Everybody is doing AI agents these days. Here's a great example of an application that gets it right:
@genspark_ai AI Sheets lets you literally talk to your spreadsheets. Upload your files, ask any data analysis question, and it automatically analyzes everything, pulls the info, -
AI Model Predicts Failures Through Energy Fingerprint Analysis
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But something wild happens :
The model adapts.
It predicts what fails next.
Then reconstructs missing data by cross-referencing old energy fingerprints. Not analytics.
Forensics. -
ChatGPT Privacy Risks: Five Critical Data Safety Warnings
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Chat-GPT Danger: 5 Things You Should Never Tell The AI Bot #AI bot #ChatGPT has taken over our lives with a billion daily questions, but experts now warn it's a "privacy black hole" that could expose your most #personalinformation. This shocking exposé reveals exactly what you
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DiskANN Vector Search Integration in Azure Cosmos DB
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8. Cost-Efficient, Low-Latency Vector Search Integrates DiskANN (a vector indexing library) inside of Azure Cosmos DB NoSQL (an operational dataset) that uses a single vector index per partition stored in existing index trees.
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HealthBench: 5,000 Multi-Turn Medical Conversations Benchmark
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5. HealthBench HealthBench is a benchmark of 5,000 multi-turn health conversations graded against 48,562 rubric criteria written by 262 physicians across 60 countries.
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Docker Bloatware Debate in Core Data Science Work
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Because Docker is an utterly unnecessary bloatware for the kind of work at the *CORE* of Data Science.
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Modern Data Stack Consolidation Through M&A Activity
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I’m back to (recreationally) writing while figuring out what’s next. First thing looking what’s happening with the modern data (and MLOps) stack. With the flurry of M&A (and M&A talks), the long-coming and frequent joke that the MDS consolidation may have finally arrived!
→ View original post on X — @mattlynley, 2025-05-17 18:58 UTC
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Venture mania in data/ML space: overcrowded and overfunded market
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In the later 10s/early 20s there was practically a venture mania for companies in the orbit of Snowflake and Databricks, with some (like Dbt, Weights & Biases, and so on) reaching lofty valuations. But most you’d talk to would consider it massively overcrowded (and overfunded).
→ View original post on X — @mattlynley, 2025-05-17 18:58 UTC
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Are LLMs Plateauing? Scaling Laws Running Out of Gas
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Are LLMs plateauing? Are Scaling Laws Running Out of Gas? Many think so because: Data ceiling – We’ve scraped most of the internet. Synthetic data helps…but models aren’t great at grading their own homework. Compute pain – Transistors are approaching atomic-scale; energy