Ai

Cursor vs. VS Code Copilot: Best AI Coding Editor 2026

Cursor wins for most coders in 2026. If you write code daily and you’re not using it, you’re leaving real speed on the table. GitHub Copilot in VS Code still wins in specific cases. What decides it isn’t the model. It’s how deep the tool reads your code, and the agent loop around it.

What “Agentic” Means in 2026

“Agentic” gets slapped on every AI coding tool with a chat box, so it helps to be precise. The capability ladder runs from tab completion at the bottom, to inline chat for single-block edits, to multi-file edit suggestions, and at the top, a real agent loop. That top loop reads your project index, edits across ten or twenty files, runs your linter and tests, reads the errors, fixes them, and keeps going until everything is green. That top tier is where Cursor and Copilot diverge most.

Local AI Security Cameras: Frigate with Google Coral TPU

Cloud security camera fees have quietly become one of the priciest bills in the smart home. At $10 to $30 per camera each month, a full setup runs $500 to $1,000 a year. You pay that to have your own footage handled on someone else’s servers. Frigate NVR changes the math. Paired with a Google Coral TPU , it runs real-time AI person and object detection across many 4K streams. Inference times stay in the single-digit milliseconds. It all runs on hardware you own, on a network that never phones home.

Building Multi-Step AI Agents with LangGraph

AI agents built on LangGraph run as stateful graphs, not linear prompts. The graph can loop, branch on tool output, retry after a failure, and save its progress. That structure is what lets one agent handle long, multi-step tasks reliably.

Key Takeaways

LangGraph models an agent as a stateful graph, so it can loop, retry, and recover.
The state schema you design up front decides how stable the agent turns out.
Built-in checkpointing lets an agent crash, pause for approval, and resume without lost work.
Conditional edges turn failures into retries instead of dead ends.
One agent task can fire dozens of LLM calls, so plan for cost before you deploy.

Prerequisites

You should know Python 3.11+ and the LangChain basics: LLMs, tools, prompts. The code below uses these versions: