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The Transformer Architecture: A Deep Dive into How LLMs Actually Work
dev.to·4h·
Discuss: DEV
📝Text Parsing
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Used-by: Context aware tech stack recommendations from crawled real world usage
news.ycombinator.com·12h·
Discuss: Hacker News
🎯Content Recommendation
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Why MAP and MRR Fail for Search Ranking (and What to Use Instead)
towardsdatascience.com·2d
📊Search Ranking
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How to Build AI-Based Recommendation Systems in Mobile Apps (2026 Guide)
vibe.forem.com·1d·
Discuss: DEV
🎛️Feed Filtering
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Measuring True Campaign Uplift in Noisy E-Commerce Data: A Practical Heuristic Approach
hackernoon.com·1d
📊Feed Optimization
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Differentially Private Federated Learning: A Client Level Perspective
paperium.net·1d·
Discuss: DEV
🛡️Differential Privacy
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Voice Search Optimization: The Next Big SEO Shift
future.forem.com·1d·
Discuss: DEV
📈Search Quality
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Natural language processing for word sense disambiguation and information extraction
arxiv.org·15h·
Discuss: r/compsci
📥Feed Aggregation
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MMSRARec: Summarization and Retrieval Augumented Sequential Recommendation Based on Multimodal Large Language Model
arxiv.org·2d
🔍Information Retrieval
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Top 5 best WordPress SEO tools in 2026
dev.to·9h·
Discuss: DEV
📊Feed Optimization
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Regulating Algorithmic Content Distribution and Moderation by Online Platforms
rstreet.org·3d·
Discuss: Hacker News
📰Content Curation
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I Built a Minimal Website and Google Ignored It
gokhanarkan.com·2d·
Discuss: Hacker News
🌀Brotli Internals
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Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies: This repository contains a curated collection of 300+ case studies from over 80 companies, detailing practical applications and insights into machine learning (ML) system design. The contents are organized to help you easily find relevant case studies based on industry or specific ML use cases.
github.com·4d
🧭Content Discovery
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Rethinking data in NL2SQL: a survey of what we have and what we expect | Vicinagearth
link.springer.com·4d
📋Document Grammar
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Retrieval-Augmented Generation for Large Language Models: A Survey
paperium.net·5d·
Discuss: DEV
🌀Brotli Internals
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Interpolative Decoding: Exploring the Spectrum of Personality Traits in LLMs
arxiv.org·3d
🧠Intelligence Compression
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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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🧠 Beyond Chatbots: Building 'Echo-Learn', an Agentic AI Tutor with Biological Memory
dev.to·1d·
Discuss: DEV
🎙️Whisper
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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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Attention from First Principles
metaworld.me·3d·
Discuss: Lobsters, Hacker News
🧠Machine Learning
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