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๐Ÿงญ Content Discovery

Feed Algorithms, Interest Mapping, Serendipity Engineering, Recommendation Systems

DRIFT: Data Reduction via Informative Feature Transformation- Generalization Begins Before Deep Learning starts
arxiv.orgยท14h
๐Ÿง Machine Learning
Show HN: Writer J โ€“ AI Content Generator with 7-Step SEO Workflow
writer-j.comยท1dยท
Discuss: Hacker News
๐Ÿ”ƒFeed Algorithms
Machine Learning Fundamentals: active learning project
dev.toยท3hยท
Discuss: DEV
๐Ÿง Machine Learning
Computer-vision research powers surveillance technology
nature.comยท2h
๐Ÿ”ŽOSINT Techniques
The Overlooked Power of Rails in the Age of AI
blog.codeminer42.comยท4h
๐ŸŒ€Brotli Internals
LLMs for Customized Marketing Content Generation and Evaluation at Scale
arxiv.orgยท1d
๐Ÿ“ŠFeed Optimization
Greedy Is Good. Less Greedy May Be Better
gojiberries.ioยท17hยท
Discuss: Hacker News
๐ŸงฎKolmogorov Complexity
Kumo Surfaces Structured Data Patterns Generative AI Misses
thenewstack.ioยท4h
๐Ÿ“ŠGraph Databases
An All-Around Better Horse
patrickhebron.comยท1hยท
Discuss: Hacker News
๐Ÿง Knowledge Management
10 FREE AI Tools Thatโ€™ll Save You 10+ Hours a Week
kdnuggets.comยท6h
๐ŸŽ™๏ธWhisper
2025-06-24: GPU Hours Granted on Hypothesis Generation by Oak Ridge Leadership Computing Facility
ws-dl.blogspot.comยท1dยท
Discuss: ws-dl.blogspot.com
๐Ÿด๓ ง๓ ข๓ ณ๓ ฃ๓ ด๓ ฟScottish Computing
Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model
arxiv.orgยท14h
๐ŸŽ›๏ธFeed Filtering
Predictive Analytics for Collaborators Answers, Code Quality, and Dropout on Stack Overflow
arxiv.orgยท1d
๐Ÿ“ŠFeed Optimization
GLIMPSE: Gradient-Layer Importance Mapping for Prompted Visual Saliency Explanation for Generative LVLMs
arxiv.orgยท14h
๐Ÿ“ŠLearned Metrics
Mapping the Evolution of Research Contributions using KnoVo
arxiv.orgยท1d
๐Ÿ“ŠCitation Graphs
Curating art exhibitions using machine learning
arxiv.orgยท14h
๐ŸบComputational Archaeology
What LLMs Know About Their Users
schneier.comยท7hยท
Discuss: Hacker News
๐Ÿ’ปLocal LLMs
Scaling Pinterest ML Infrastructure with Ray: From Training to End-to-End ML Pipelines
medium.comยท1dยท
Discuss: Hacker News
๐ŸงฎZ3 Applications
NaviAgent: Bilevel Planning on Tool Dependency Graphs for Function Calling
arxiv.orgยท14h
๐Ÿ”—Topological Sorting
Driving cost-efficiency and speed in claims data processing with Amazon Nova Micro and Amazon Nova Lite
aws.amazon.comยท1h
๐ŸŒŠStream Processing
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