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Natural language processing for word sense disambiguation and information extraction
arxiv.org·19h·
Discuss: r/compsci
📥Feed Aggregation
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Kibana: Visualizing Your Data Story
dev.to·14h·
Discuss: DEV
🏺ZIP Archaeology
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Yann LeCun’s VL-JEPA: The breakthrough that gives AI a "Mind's Eye" (instead of just a mouth).
hisohan.substack.com·10h·
Discuss: Substack
🔲Cellular Automata
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Inferal Workspace Architecture: How We Work at Inferal
gist.github.com·2d·
Discuss: Hacker News
📦Git LFS
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The Transformer Architecture: A Deep Dive into How LLMs Actually Work
dev.to·8h·
Discuss: DEV
📝Text Parsing
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The Case for Snake Case: A Kolmogorov Complexity Argument
modulovalue.com·1d·
Discuss: Hacker News
λLambda Encodings
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Stop Fighting Context Limits: How Multi-Agent Systems Solved My Development Chaos(Part 1)
dev.to·2h·
Discuss: DEV
🔌Interface Evolution
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Knowledge-Driven 3D Semantic Spectrum Map: KE-VQ-Transformer Based UAV Semantic Communication and Map Completion
arxiv.org·2d
🧮Kolmogorov Complexity
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Stanford CS 224N | Natural Language Processing with Deep Learning
web.stanford.edu·1d
🧠Machine Learning
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AI Infrastructure Basics: How MCP Works
newsletter.systemdesign.one
·1d·
Discuss: r/programming
🌊Stream Processing
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Cross-Modal Knowledge Distillation for deep-sea exploration habitat design with embodied agent feedback loops
dev.to·1d·
Discuss: DEV
🔲Cellular Automata
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aashirpersonal/semantic-coverage: Automated detection of knowledge gaps and blind spots in RAG vector stores.
github.com·3d·
Discuss: Hacker News
🌀Brotli Internals
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Do we need semantic layer anymore? What are the limitations of LLMs?
motherduck.com·3d·
Discuss: Hacker News
🧮Datalog
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<div xmlns="http://www.w3.org/1999/xhtml"> A decade of katriawm </div>
uninformativ.de·4d
🌐Web Archives
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SMART SLM: Structured Memory and Reasoning Transformer, A Small Language Model for Accurate Document Assistance
arxiv.org·2d
📋Document Grammar
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The Concept of Bias: A Baseline Mechanism for Efficient Intelligence
theminddeveloper.github.io·3d·
Discuss: Hacker News
🧠Intelligence Compression
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MCAP Indexing — Monday Morning Haskell
mmhaskell.com·5d
🔄Burrows-Wheeler
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Data Quality in the Age of AI: Addressing the Challenge of "Dirty Data"
info.aiim.org·4d
🔗Data Provenance
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Subgroup Discovery with the Cox Model
arxiv.org·2d
🧠Machine Learning
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3 Smart Ways to Encode Categorical Features for Machine Learning - MachineLearningMastery.com
machinelearningmastery.com·5d
🧠Machine Learning
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