Anchor Detection for RAG: Parallel Detectors and One LLM Call
A three-stage RAG retrieval pipeline runs keyword and embedding detectors in parallel, then resolves candidates with a single LLM call.
A three-stage RAG retrieval pipeline runs keyword and embedding detectors in parallel, then resolves candidates with a single LLM call.
Harness-1 separates query generation from state tracking to build a leaner retrieval agent. It outperforms larger systems across eight benchmark domains.
Seven open-weight coding models worth running locally in 2026, from efficient MoE models to multimodal options, all tested on consumer GPU hardware.
Outliers can skew statistics and break predictive models. This article compares five detection methods with Python examples for each.
AI tools now let anyone build agents without Python. But prompting frameworks and hardware specs still separate casual users from serious practitioners.
Sakana Fugu coordinates multiple expert agents internally while exposing a single OpenAI-compatible API. Here's how it works, what it costs, and when to use it.
Agentic loops in Claude Code let AI work autonomously end-to-end. Here's how the /goal command makes that happen.
Four math disciplines separate data scientists who understand models from those who just run them. Here's what to learn and in what order.
Ollama, Gemma 4, and OpenCode combine into a local AI coding stack that keeps your code off the cloud entirely.
WebMCP is a proposed open web standard that lets websites expose structured, callable tools directly to browser-based AI agents, eliminating unreliable click-ba
A real data preprocessing task shows where human problem-solving ends and AI assistance begins - and why the gap matters.