Schema-first at ingestion time is the right framing. Most teams discover this after building the retrieval layer and wondering why structured queries return noise. The 10/10/10 constraint in Graphiti helps too. Forces you to model the 80% that matters rather than attempting
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Knowledge Graph Extraction and Schema Design for AI Systems
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Exactly. The extraction step decides everything downstream, and most systems leave it completely unguided. The moment you add typed entities and constrained edges, the graph stops behaving like a vector store and starts being queryable. Schema should be step one, not an
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Graphiti hybrid ontology: prescribed and learned schema discovery
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Graphiti supports both prescribed and learned ontology, so it can discover new types alongside your defined schema. The sweet spot is a hybrid, fixed schema for core domain, system proposes new types for patterns outside that boundary. Full auto-schema loops back to the
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LLM Multi-Hop Retrieval: Schema vs Sequential Queries
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That works when the graph is small and the hops are predictable. At scale, sequential LLM queries for every multi-hop retrieval add latency and token cost per question. The schema lets you answer it in one structured traversal instead of chaining open-ended lookups. Not
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LLM Context Cost Optimization and Efficiency Strategy
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// The Efficiency Frontier in LLMs // (bookmark this one) How much are you overpaying for context you do not need? It turns out that context costs dominate production LLM bills, and the right strategy depends on how often you reuse preprocessing. Modeling that explicitly lets
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GeoAI Machine Learning Predicts Traffic Patterns
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GeoAI! #MachineLearning Help Predict Traffic Carmageddon! by @rachelevagordon. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #GeoSpatial #Linux
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Industrial AI in Production: Data Infrastructure to AI Copilots
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Just published on @iiot_world: Industrial AI in Production booklet covers six companies showing production results from data infrastructure through AI copilots and digital twins at Hannover Messe 2026. #highbyte_iiot #influxdata_iiot #cybus_iiot pic.twitter.com/b9vJZwgyLA
— Lucian Fogoros (@fogoros) 24 mai 2026Just published on @iiot_world
: Industrial AI in Production booklet covers six companies showing production results from data infrastructure through AI copilots and digital twins at Hannover Messe 2026. #highbyte_iiot #influxdata_iiot #cybus_iiot -
Training NLP Models on Amazon Reviews with Python
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Training AI on Amazon Electronic Reviews Using #Python for Natural Language! – by – @gp_pulipaka
! JupyterLab/Jupyter Notebook WordNet, Lexical Semantic Relation Analyzer
Thesaurus, 155,000 Words
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Validating Data Integrity for AI Models
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How do you validate the integrity of data feeding your AI models?
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Backstage on Databricks Lakebase: testing database branching
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For decades, operational and analytical databases lived as separate systems because they had to. This walkthrough explores what happens when Backstage, @Spotify
’s internal developer portal, runs on Databricks Lakebase instead of Postgres, testing how database branching changes