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Scoured 15855 posts in 797.9 ms
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization
arxiv.org·14h
🎛️Feed Filtering
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From Retrieval to Relevance: 5 Reranker Types Defining Modern Search Systems
pub.towardsai.net
·1d
🎯Retrieval Systems
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Incremental AI Adoption for E-Commerce
arcturus-labs.com·15h·
Discuss: Hacker News
🤖AI Curation
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Automatic Prompt Optimization for Dataset-Level Feature Discovery
arxiv.org·14h
🤖Grammar Induction
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Automating Privacy Code Reviews by Mapping How Software Handles Personal Data
hackernoon.com·10h
🔐Privacy Codecs
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Building ClipSearch: AI-Powered Product Sourcing for Interior Designers
dev.to·21h·
Discuss: DEV
🌀Brotli Internals
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X's algorithm source code drops: what it reveals about the platform's feed mechanics
ppc.land·6h
📊Feed Optimization
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jaylenhester/aiasuka-data-project: Data and analysis for a social experiment investigating the impact of an AI persona on follower growth in the Web3 space. Coded in Python.
github.com·2h·
Discuss: Hacker News
🏛Digital humanities
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AI Powered SEO Optimisatitoin with Google Search Console
climbseo.dev·2d·
Discuss: Hacker News
📊Feed Optimization
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Qdrant - Vector Database
qdrant.tech·20h
🗂️Vector Databases
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Waiting for dawn in search: Search index, Google rulings and impact on Kagi
blog.kagi.com·19h·
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🌐Federated Search
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How Etsy Uses LLMs to Improve Search Relevance
etsy.com·4d·
Discuss: Hacker News
📊Search Ranking
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Building a Google Places Extraction Tool That Scales
blog.domharvest.dev·2d·
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🔍Search Indexing
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Filtering as domain logic
blog.ploeh.dk·1d·
Discuss: r/programming
🗄️Database Internals
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Databases are magic ... until ...
silvestreperret.com·10h·
Discuss: Hacker News
🗄️Database Internals
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Everything Moe
ianbarber.blog·15h·
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🧠Learned Compression
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Without benchmarking LLMs, you're likely overpaying 5-10x
karllorey.com·1d·
Discuss: Hacker News
📏Code Metrics
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**Designing an Explainable AI System for Personalized Cancer
dev.to·1d·
Discuss: DEV
⧗Information Bottleneck
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On Google 2026 Announcements
future.forem.com·19h·
Discuss: DEV
🖥️Terminal Renaissance
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We Tuned 4 Classifiers on the Same Dataset: None Actually Improved
kdnuggets.com·1d
🧠Intelligence Compression
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