@rohanpaul_ai: 3. The setup is small. Install neo-mcp, create a NEO secret key, register it with Claude Code, and then ask the agent t…

"I don't prompt Claude anymore. I write loops and the loops do the work. My job is to write loops." - Boris Cherny, creator of Claude Code. @withneo 's newly launched MCP server gives Claude Code a local AI engineering worker through neo-mcp. It lets Claude Code hand off complex AI/ML tasks to Neo, which can implement code, run experiments, evaluate results, debug failed runs, and return the full execution trail. I.e. Claude Code talks to you, Neo does the longer ML work. In one benchmark, Claude Code + NEO cut task cost from $1.96 to $0.74, made runtime 37% faster, and switched the backend from PyTorch to ONNX Runtime for CPU-optimized execution. Connect Claude Code, Cursor, VS Code, and other MCP clients to NEO. The editor stays in control of the conversation. NEO does the AI engineering. So Neo becomes the execution layer for AI engineering work. Claude Code hands off the task. Neo plans, runs experiments, monitors progress, evaluates results, and returns the trail: transcripts, files, metrics, reports, or repo changes. That feedback loop matters. Instead of burning Claude Code’s limited iterations babysitting long-running tasks, Claude can use Neo’s MCP tools to check status, pause, resume, inspect the trail, or ask for input at decision points. Claude stays focused on decisions, not execution noise.