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Information Retrieval
🔎 Information Retrieval
Search Systems, Indexing, Query Processing, Relevance Ranking
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201
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ms
RISE: A Rust Library for
Inverted
Index
Search
Engines
💎
Obsidian
Content type:
Academic
arxiv.org
·
2d
2 days ago
Actions for RISE: A Rust Library for Inverted Index Search Engines
Aperio: Lightweight
search
engine
in Rust – GBs of data in < 1ms, < 256MB RAM
📋
Bullet Journal
Content type:
Code
github.com
·
5d
5 days ago
·
Hacker News
,
r/opensource
Actions for Aperio: Lightweight search engine in Rust – GBs of data in < 1ms, < 256MB RAM
On Arithmetic Private
Information
Retrieval
: Why Code-Based PIR (Usually) Fails
🗂
PKM
eprint.iacr.org
·
15h
15 hours ago
Actions for On Arithmetic Private Information Retrieval: Why Code-Based PIR (Usually) Fails
Best practices for building a modern app with
vector
search
💎
Obsidian
Content type:
Blog
elastic.co
·
2d
2 days ago
Actions for Best practices for building a modern app with vector search
Explore OpenSearch 3.7
💎
Obsidian
Content type:
Blog
opensearch.org
·
22h
22 hours ago
Actions for Explore OpenSearch 3.7
An ode to self-optimizing
query
plans
📋
Bullet Journal
jpountz.github.io
·
6d
6 days ago
·
Lobsters
Actions for An ode to self-optimizing query plans
The Integrity Graph: The Missing Layer In Your AI Visibility Audit via @sejournal, @billhunt
🔗
Linking Strategies
searchenginejournal.com
·
7h
7 hours ago
Actions for The Integrity Graph: The Missing Layer In Your AI Visibility Audit via @sejournal, @billhunt
Agentic
search
-
retrieval
, harness, or model?
🕸️
Knowledge Graphs
Content type:
Blog
softwaredoug.com
·
2d
2 days ago
·
Hacker News
Actions for Agentic search - retrieval, harness, or model?
MongoDB as a
Vector
Database for AI Agents-MongoDB
🕸️
Knowledge Graphs
foojay.io
·
6d
6 days ago
Actions for MongoDB as a Vector Database for AI Agents-MongoDB
Quiz: Embeddings and
Vector
Databases With ChromaDB
🤖
NotebookLM
realpython.com
·
1d
1 day ago
Actions for Quiz: Embeddings and Vector Databases With ChromaDB
How to Build an Agentic RAG with RubyLLM and Rails
💎
Obsidian
Content type:
Blog
panasiti.me
·
13h
13 hours ago
·
Hacker News
Actions for How to Build an Agentic RAG with RubyLLM and Rails
Redis for Agent Memory
🕸️
Knowledge Graphs
Content type:
Blog
rephrase-it.com
·
4d
4 days ago
Actions for Redis for Agent Memory
STORM: Stepwise Token Optimization with Reward-Guided Beam
Search
🤖
NotebookLM
Content type:
Academic
arxiv.org
·
17h
17 hours ago
Actions for STORM: Stepwise Token Optimization with Reward-Guided Beam Search
Your AI agent reads the fine print: building a RAG pipeline over EU regulations with Elasticsearch and OGX
💎
Obsidian
Content type:
Blog
elastic.co
·
1d
1 day ago
Actions for Your AI agent reads the fine print: building a RAG pipeline over EU regulations with Elasticsearch and OGX
Understanding HNSW: The
Engine
Behind Fast
Vector
Search
🔗
Linking Strategies
chimchim89.github.io
·
3d
3 days ago
Actions for Understanding HNSW: The Engine Behind Fast Vector Search
Private
Information
Retrieval
: Share Conversions vs. Decoding Polynomials
🗃️
Zettelkasten
eprint.iacr.org
·
1d
1 day ago
Actions for Private Information Retrieval: Share Conversions vs. Decoding Polynomials
Show HN: LLM memory without context bleed; 100% precision vs. <10%
vector
search
🤖
NotebookLM
tenureai.dev
·
5d
5 days ago
·
Hacker News
,
Hacker News
Actions for Show HN: LLM memory without context bleed; 100% precision vs. <10% vector search
hashwnath/KMCP: Open-source MCP server for your docs. Zero LLM at
query
time. docker compose up and go.
🏗️
Information Architecture
Content type:
Code
github.com
·
20h
20 hours ago
·
Hacker News
Actions for hashwnath/KMCP: Open-source MCP server for your docs. Zero LLM at query time. docker compose up and go.
Ontologies for the Agentic Web
🕸️
Knowledge Graphs
Content type:
Blog
wordlift.io
·
5d
5 days ago
Actions for Ontologies for the Agentic Web
New comment by jasonlayton4323 in "Ask HN: Who wants to be hired? (June 2026)"
📋
Bullet Journal
drive.google.com
·
5d
5 days ago
·
Hacker News
Actions for New comment by jasonlayton4323 in "Ask HN: Who wants to be hired? (June 2026)"
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