Software teams keep adding AI coding agents to their workflow. One popular trend: drop a repo-level context file, often named AGENTS.md or CLAUDE.md, to guide the agent. The idea sounds clean. Give the AI a map of the codebase and a few rules, and it should solve tasks faster.

But does it work? A new paper, “Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?” , says no. The results push back hard on the default advice.

Setting up FLUX 2 Max locally in 2026 is significantly more streamlined than previous years, but because the “Max” variant is a massive 32B+ parameter model, your hardware remains the biggest hurdle.

Here is the step-by-step guide to getting it running.

Small Language Models, sub-4B parameter models built to run on local hardware, now handle most of the edge AI work that used to need the cloud. Phi-4 , Gemma 3 , and Llama 3.2-1B run offline on Raspberry Pi boards, phones, and industrial PLCs. The economics, latency, and privacy story all point the same way: edge first.

What Counts as a Small Language Model

In 2023, “small” meant under 13B parameters. Today, three tiers matter for edge work.

The best way to fine-tune Stable Diffusion XL 2.0 is with Low-Rank Adaptation (LoRA) . It’s a small adapter that injects your style or subject into the model without touching the base weights. Instead of retraining the full model (which needs huge compute and yields a 6+ GB file), LoRA trains a tiny side network that sits next to the frozen base. The result is a 50 to 300 MB file you can load, swap, and stack at inference time. With the right tools, you can train a solid LoRA on a mid-range RTX 50-series GPU with 12 GB of VRAM in an afternoon.

To build a private Retrieval-Augmented Generation (RAG) system, pair a local vector database like Qdrant with an embedding model like BGE-M3 . Add a local LLM through Ollama , and you can index hundreds of documents and ask questions about them. Your data stays on your machine.

Why RAG? The Problem With Pure LLM Memory

Large language models sound smart, but they are poor knowledge stores. They learn from old training data and know nothing about files you created later or keep private. Ask about your own data, and the model will often guess. Even strong open weight models like Llama 4.0 can invent plausible but wrong answers about content they never saw. For a deeper breakdown of why LLM hallucinations happen and how to measure them, the issue goes beyond missing context.

Modern AI agents use LangGraph to run cyclic workflows that need memory and self-correction. By framing your agent as a stateful graph, you move past simple linear prompts. You build autonomous systems that loop, branch on tool output, recover from failures, and save progress across hours or days of work.

This post walks LangGraph from core ideas to production deployment. You’ll learn how to design a state schema, set up self-correcting retry logic, build multi-agent patterns, and serve your agent through a production API. Working Python code runs throughout.