PXAI Audio Feed
25/03 07:30 dev.to

The One Concept Behind RAG, Search, and AI Systems

embeddingsvector representationsemantic searchRAGAI fundamentalsmachine learning
24/03 15:35 dev.to

I built an AI Second Brain on SurrealDB - and connected it to Claude via MCP

SurrealDBAI Second BrainMCPClaudeknowledge graphnatural language search
24/03 14:23 dev.to

Why Most RAG Systems Fail in Production (And How to Design One That Actually Works)

Retrieval-Augmented GenerationRAGproduction failuressystem designembeddingsvector database
24/03 14:04 dev.to

8º. Hybrid search with RRF: combining pgvector, tsvector, and a knowledge graph in one query

hybrid searchsemantic searchfull-text searchknowledge graphpgvectortsvector
24/03 00:53 dev.to

I Ran 60 Autoresearch Experiments on a Production Search Algorithm. Here's What Actually Happened.

autoresearchhybrid searchCohere embeddingskeyword re-rankingproduction searchsearch tuning
22/03 15:24 dev.to

Scaling PostgreSQL to 100M+ Vectors: Production Optimization Guide

PostgreSQLvector embeddingsAI scalingproduction optimizationAWS RDS100M vectors
20/03 10:53 dev.to

Which Embedding Model Should You Actually Use in 2026? I Benchmarked 10 Models to Find Out

embedding modelsGemini Embedding 2RAGvector searchbenchmarkMTEB
20/03 02:47 dev.to

What Is a Vector in AI and Why Does It Matter So Much?

vectorsembeddingssemantic searchrecommendation systemssimilarity matchingvector databases
19/03 17:00 towardsdatascience.com

Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines

RAGprompt cachingretrieval-augmented generationembeddingscaching layerslatency reduction
19/03 17:00 towardsdatascience.com

Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines

RAGprompt cachingretrieval-augmented generationembeddingscaching layerslatency reduction
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