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AI got cheap enough that Duolingo’s most expensive plan may not survive it. I read the earnings call transcript and opened the app to see what is actually changing for learners.
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L'Italiano nel mondo
Introduction to HTML5
Umano Digitale
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Discover real-world strategies for managing billion-scale vector databases in Elasticsearch and OpenSearch, moving beyond semantic search hype to tackle scalability and performance challenges.
Discover how to build multi-agent systems that automatically extract insights from user discussions across Discord, GitHub, and other platforms using open-source tools like Milvus and LangChain.
Explore advanced AI analytics on unstructured data using Aryn, moving beyond RAG to perform complex semantic analysis at scale with visual AI models and LLMs.
Explore how Mercari re-architected their search system using AI and LLMs, covering end-to-end architecture, successful applications, and business ROI considerations for e-commerce.
Explore how AI reshapes work and innovation for technologists, examining its potential to amplify human capabilities while navigating pitfalls and developing habits for thriving in an AI-infused future.
Explore how AI inference becomes a query-native primitive in Elasticsearch, covering semantic retrieval, re-ranking, and new ES|QL primitives with practical examples.
Explore advanced retrieval systems comparing BM25, vector, hybrid models, and mixture-of-encoders for multi-attribute search with real-world evaluation metrics.
Discover how AI combats misinformation through search, intelligent assistants, and RAG systems to empower journalists with fact-checking tools and rebuild trust in news.
Explore AI agents through three expert perspectives: investment hype, user needs vs promises, and technical limitations to build practical solutions over possibilities.
Discover how to transform e-commerce search with federated architecture, improving latency by triple digits while cutting costs 20% and boosting conversions for better customer experience.
Discover how to evaluate multimodal search systems using embedding models to measure semantic relevance across text and images, with practical open-source tools for optimization.
Discover a revolutionary approach to billion-scale hybrid retrieval that unifies dense & sparse vectors, keywords, and filters in a single distributed query without separate indexes.
Discover how LLM-based agents automate search engine tuning through interleaved A/B testing, making optimization faster and more accurate than manual methods.
Discover how to build scalable hybrid image search systems combining visual embeddings, LLM-based lexical search, and knowledge graphs for 10M+ products at industrial scale.
Discover practical strategies for implementing hybrid search systems that serve both RAG and conventional search, including query design, latency control, and enterprise rollout challenges.
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