Naive RAG Is Dead. Here's What Replaced It.
Most RAG pipelines retrieve garbage, stuff it into context, and pray. Agentic RAG replaces the prayer with a judge, a retry loop, and a routing layer that actually works.
10 posts tagged with “llms”
Most RAG pipelines retrieve garbage, stuff it into context, and pray. Agentic RAG replaces the prayer with a judge, a retry loop, and a routing layer that actually works.
An 8B language model that fits in 1.15GB of RAM, runs 8x faster than full-precision, and matches its benchmark scores. Prism's Bonsai family just made 1-bit LLMs commercially viable — here is what that unlocks for developers.
Prompt engineering was the skill of 2023. Context engineering is the discipline of 2026 — and it's the difference between AI that impresses in demos and AI that ships in production.
ByteDance's DeerFlow 2.0 hit #1 on GitHub Trending with 39K stars in weeks. It's not another chatbot wrapper — it's a full-stack SuperAgent harness with sandboxed execution, persistent memory, sub-agents, and LangGraph orchestration. Here's everything you need to build with it.
Anthropic's Claude just became a remote digital operator for your Mac — clicking, typing, and navigating apps on your behalf. Here's how the tech works, what the privacy trade-offs are, and why this escalates the AI agent war.
ARC-AGI-3 just launched and current AI scores under 5%. The same week GPT-5.4 solved an open research math problem. This is not a contradiction. It is the most important insight about intelligence published this decade.
LiteLLM 1.82.7 and 1.82.8 contained a credential stealer that ran on every Python startup without a single import. Here is the full technical post-mortem and what every AI developer must do right now.
AI is leaving the cloud. The next revolution isn't AGI — it's a billion cheap, autonomous agents running on the device in your hand, your wall, and your factory floor.
The complete Claude Code reference for 2026 — CLAUDE.md architecture, MCP wiring, worktrees, slash commands, and the workflows that 10x your output.
Master the most effective prompt patterns — from chain-of-thought to few-shot learning — and learn when to use each one for maximum results.