Don't waste Claude limits babysitting AI experiments
Learn how to extend Claude limits by offloading AI tasks to NEO, enabling production AI deployment from your terminal. Includes installation steps and benchmark results showing 62% cost reduction and 37% speed improvement.
Claude Code + NEO: Ship production AI from your terminal
Extend Claude limits by offloading AI tasks to Neo
1
Install neo-mcp
Add NEO's MCP server to any environment with Python 3.11+.
Terminal
pip install neo-mcp
2
Create your Secret Key
Open your NEO dashboard, create a key, and copy it. Keys look like sk-v1-….
Get Access Key
3
Connect Claude Code
Register NEO with one command, then just ask in a new chat to ship work.
Command
claude mcp add --scope user neo \ -e NEO_SECRET_KEY=sk-v1-your-key \ -- python3 -m neo_mcp
Using Cursor, VS Code, or another MCP client? See the neo-mcp setup.
Ship production AI directly
claude session
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Independent benchmark
62% cheaper. Better answer.
The task: benchmark a speech-to-text model on a CPU-only Azure VM — 2 cores, 7.7 GB RAM, no GPU. Claude Code alone reached for the obvious HuggingFace + PyTorch path and iterated in real time. NEO spent two minutes researching first, then chose ONNX Runtime for its AVX2-optimized CPU kernels — same task, same machine.
Cost per task
$1.96→$0.74
62% cheaper
Runtime (RTF)
0.519→0.328
37% faster
Backend chosen
PyTorch→ONNX Runtime
CPU-optimized
Benchmark by Gaurav Vij · Read the full writeup
Start shipping.
Install once, then delegate ML work to NEO from any Claude Code session.
Read the setup guide