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➡️ Building a compute framework for turning data into vector embeddings. 🤗 Connect your data infrastructure to your vector database with Superlinked. Join our product preview: 📋 Try it out in a Python notebook with no dependencies. ↗️ Design the perfect vectors for your retrieval use-case. 🔧 Mix and match streaming, batch, structured and unstructured data. 🚀 Launch to production with your favorite Vector Database. ➡️ Let's sort out information retrieval across your organization! . . . . Previous: Daniel is a Senior Software Engineer at Google, where he tech-leads, designs and builds forecasting & pricing systems for ads worth billions of dollars. These are distributed systems that run on thousands of machines, process terabytes of data per query and combine Machine Learning modeling with game-like simulation of tens of thousands ad campaigns - all happening in real-time to enable thousands of concurrent users of buying tools like AdWords and DBM. Daniel wields a rare combination of deep technical skills, a user-centric drive and an ability to align multiple teams towards a common goal - launching and landing high quality cutting edge products. Daniel co-founded a tech startup that brought machine vision to the consumer photo space 10 years ago, he was an invited speaker at an ML dev conference in Zurich and speaks at startup events regularly. He holds multiple patents with Google, is published in a peer-reviewed ML journal from his time at IBM Research and is a Google Code Jam on-site finalist. On weekends he flies, climbs, skis and bikes in the Alps, see: http://instagram.com/mountain.supo
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This paper is wild. 🤯 Stanford researchers found a very simple way to make any open LLM a strong reasoning model. They did two key things: 1️⃣ Used just 1,000 carefully chosen reasoning examples for training 2️⃣ Added a "Wait" command when the model tries to stop thinking too early Their results beat OpenAI's o1 model on math tests by up to 27%. The approach is also cheap and fast: ▪️ it took only 26 minutes of training ▪️ and cost less than $30. This shows that making AI better at reasoning doesn't need complex methods or huge datasets. Simple tricks can work remarkably well.
Multi-attribute Vector Search is The Future of E-commerce. Not Text-to-SQL 💡🤔 Search has evolved dramatically post-ChatGPT – and so have user expectations 🔍. Shoppers now expect to find their product with natural language search. I believe it's time to replace text-to-SQL approaches with something more powerful. Here's why: ▪️ Vector search systems understand natural language beyond simple keyword matching ▪️ Multi-attribute vectors combine text, numbers, and categories in one unified search ▪️ Natural language processing handles complex queries that break SQL approaches ▪️ Implementation is simpler and more flexible than maintaining separate indices The future of e-commerce search isn’t about translating text to SQL—it’s about multi-dimensional vector systems. ⚡ Our MongoDB/OpenAI implementation is available on GitHub now. 👇👇 Shoutout to Paul Iusztin for this value-packed tutorial!
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