🕸️ Sparse Vectors - emschwartz · Scour

STORM: Stepwise Token Optimization with Reward-Guided Beam Search

🔎Tantivy Academic

bymilon/post-desk: AI-native X content engine built with Hono, Turso, Drizzle, Vertical Slice Architecture (VSA), and CQRS. Capture inspiration, manage post pipelines, and perform lightning-fast BM25/FTS5 content retrieval through production-grade command/query boundaries.

🗄️libSQL Code

github.com··Hacker News

Measuring Embedding Drift: Why Hybrid Search Saves Stale Models.

🔗Hybrid Search

pub.towardsai.net

·

Agentic search - retrieval, harness, or model?

📊Search Ranking Blog

softwaredoug.com

··Hacker News

Charge as a Construct-Validity Factor in Chinese Legal Case Retrieval: A Cross-Benchmark Audit

🔎Tantivy Academic

AI context windows: Why context quality beats context size

🪄Prompt Engineering Blog

Best practices for building a modern app with vector search

🏗️Search Infrastructure Blog

Less-relevant results

I built a graph-memory layer on top of turbovec for local/constrained RAG — looking for feedback

🎯Vector Search Code

github.com··r/LocalLLaMA

Talk more to your coding agents

💻Claude Code

datawill.io··Lobsters

Your AI agent reads the fine print: building a RAG pipeline over EU regulations with Elasticsearch and OGX

🏗️Search Infrastructure Blog

How Fine-Grained Should a RAG Benchmark Be? A Hierarchical Framework for Synthetic Question Generation

🔄LLM RAG Pipelines Academic

Agent Memory Database: Build It on TiDB with SQL and Python

🎯Vector Search Blog

DeytaHQ/khora: Library for creating knowledge repositories from multi-source data and expose a single query substrate

🚀Astral Code

github.com··Hacker News

MiniMax Sparse Attention

⚡Hardware Acceleration Academic

arxiv.org··Cited by 1 article

Fine-Tuning vs. RAG vs. Prompting: the Definitive Decision Framework for 2026

🪄Prompt Engineering

pub.towardsai.net

·

Towards Retrieving Interaction Spaces for Agentic Search

🔎Tantivy Academic

synpulse8-opensource/pulse8-ai-cortex-knowledge-vault: Agent-native knowledge OS built on Markdown. A shared vault for AI agents and humans, backed by a typed knowledge graph, full-text search, and an LLM-powered compiler, all accessible through MCP. Drop files in, let agents read, write, search, link, and compile knowledge. No database required.

📋MCP Code

github.com··Hacker News

Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering

🔄LLM RAG Pipelines Academic

The Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Content

🤖AI Academic

YasserCR/galdor: A Go-native framework for LLM agents, with OpenTelemetry observability built in.

📋MCP Code

github.com··Hacker News

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