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Practical AI, built and tested. Field notes from AgenticForge Labs on local AI, agents, robotics, automation, and generative systems — what we are actually building, what works, what it costs, and where human judgment still matters.
From the workbench
We use Forge Notes to document workflows we are actually testing: how agents use tools, how local and cloud models fit together, where persistent memory helps, how humans stay in the loop, and how software can connect to robotics and other equipment.
The focus is practical rather than promotional. We care about systems that survive contact with real work: workflows that can be checked, costs that can be measured, security boundaries that remain visible, and automation that still leaves the right decisions with people.
Research · Forge Bench · AI efficiency
Our first Forge Bench research release shows why coding-agent efficiency depends on model, cache economics, tools, providers, and their interactions.
Read the Forge Bench results →
Local AI · agents · practical setup
A practical guide to local AI agents: common building blocks, a simple Hermes and OpenRouter setup, remote chat access, and safety basics.
Read the local-agent guide →
Local AI · Linux · coding agents
AI coding agents are useful for more than writing software. They can also remove much of the friction that once made Linux intimidating.
Read Vibe Bashing →
AI efficiency · coding agents
A practical AgenticForge Labs workflow for stretching AI coding budgets with model routing, Codex credits, GitHub handoffs, and regular ChatGPT web conversations.
Read the coding-budget workflow →