GitHub

CHESS: Contextual Harnessing for Efficient SQL Synthesis

This repository contains the code and data for the paper "CHESS: Contextual Harnessing for Efficient SQL Synthesis." Translating natural language questions into SQL queries, known as text-to-SQL, is a ...

New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.

NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — using step-by-step reasoning.

DeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%

DSpark can make decoding faster, but acceptance quality still determines how much speed the system actually realizes.
XDA Developers on MSN

I quantized a local LLM on my home server and ditched cloud AI for smart home control entirely

My Proxmox node now powers my entire smart home without touching a single cloud service ...
GitHub

Text-to-SQL Business Assistant

A public, reusable Text-to-SQL business analytics assistant built with FastAPI, Gemini, LangChain, MySQL, SQLAlchemy and Streamlit. The app lets non-technical users ask business questions in plain ...