PXAI
v8.42
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Version History
Version
Description
Date
8.42
[ADDED SOURCE REGISTRY LIST VIEW]
04/03/2026 16:19
8.41
[LOCALIZED INDEX.PHP MENU AND MODALS TO US ENGLISH]
04/03/2026 16:11
8.40
[TRANSLATED FRONTEND TO US ENGLISH]
04/03/2026 16:10
8.41
[RESTORED DETAILED SOURCES VIEW (ARTICLES + EVENTS) IN US ENGLISH]
04/03/2026 15:50
8.41
[RESTORED DETAILED SOURCES VIEW (ARTICLES + EVENTS) IN US ENGLISH]
04/03/2026 15:46
8.40
[REVERTED TO STANDARD TABLE LAYOUT (US ENGLISH)]
04/03/2026 15:40
8.39
[FIXED DB CONNECTION SCOPE IN SOURCES LOGIC]
04/03/2026 15:38
8.38
[TRANSLATED SOURCES VIEW TO US ENGLISH]
04/03/2026 15:01
8.37
[FULL FRONTEND TRANSLATION (MENU, FEED, COMMENTS, TTS) TO US ENGLISH.]
04/03/2026 14:58
8.67
[MANUAL OVERRIDE OF GREEK MENU ITEMS]
04/03/2026 13:36
8.66
[NGINX OPTIMIZED MENU FIX]
04/03/2026 13:33
8.65
[FIXED GREEK MENU ITEMS]
04/03/2026 13:32
8.60
[MENU & UI LOCALIZATION TO US ENGLISH]
04/03/2026 13:30
8.50
[FULL TRANSLATION TO US ENGLISH]
04/03/2026 13:27
9.75
[INJECTED EVENT FUSION_SUMMARY INTO FEED LOOPS TO DISPLAY AI TAGS CORRECTLY]
26/02/2026 14:44
PXAI Audio Feed
+5
ΟΛΑ
25/03 09:44
dev.to
Vector: The Data Pipeline for Observability
Vector
observability
data pipeline
logs
metrics
traces
25/03 08:14
dev.to
Most GenAI chatbot tutorials stop at “call an LLM get an answer.”
GenAI
chatbot
production
retrieval
guardrails
vector database
25/03 07:38
dev.to
Giving AI Agents “Live Memory” with Aurora Zero-ETL and Redshift Vector Search
AI agents
live memory
Aurora Zero‑ETL
Redshift vector search
situational awareness
goldfish problem
25/03 07:30
dev.to
The One Concept Behind RAG, Search, and AI Systems
embeddings
vector representation
semantic search
RAG
AI fundamentals
machine learning
24/03 23:05
dev.to
Engram: A new type of AI
Engram
vector database
agentic reasoning
DeepSeek
LLM hallucination
training
24/03 21:34
dev.to
ONNX Runtime + pgvector in Django: semantic search without PyTorch or external APIs
ONNX Runtime
pgvector
Django
semantic search
local inference
embedding APIs
24/03 20:00
dev.to
Implementing a RAG system: Crawl
Retrieval-Augmented Generation
RAG
HR chatbot
vector database
document embedding
British Columbia policies
24/03 15:28
dev.to
How I Deployed a RAG Engine to Production with Docker, Nginx and DigitalOcean
RAG engine
Docker
Nginx
PostgreSQL
pgvector
Redis
24/03 14:23
dev.to
Why Most RAG Systems Fail in Production (And How to Design One That Actually Works)
Retrieval-Augmented Generation
RAG
production failures
system design
embeddings
vector database
24/03 14:23
dev.to
Why Most RAG Systems Fail in Production (And How to Design One That Actually Works)
Retrieval-Augmented Generation
RAG
production failures
system design
embeddings
vector database
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