🚨 Your Claude Code credits are burning!! Because Claude Code is not built for AI/ML tasks. SO I found a way to save 75% of my AI credits by offloading complex AI/ML tasks to cost-effective agents. Here's a step-by-step tutorial. 👇 This MCP literally upgrades your Claude Code

Here's how it works: You ask your coding agent, like Claude Code (or) Codex, to use Neo for performing an AI/ML task. After that: → Your agent calls Neo through the MCP. → Neo plans, runs & debugs → Everything lands in your repo It’s like having an AI engineer running long iterative experiments and grounded evaluations instead of just chatting!

You no longer need to write a prompt (or) debug. Claude is EXPENSIVE for: → Building AI Agents/Pipelines → Fine-tuning / optimising AI models → Processing large datasets → Evaluating latest AI models → Testing different AI models → Building speech-to-text or computer vision apps These tasks eat through your credits in minutes. NEO handles these heavy jobs for a fraction of the cost!!

The setup is INSANELY simple: 1️⃣ Get a Neo API key. 2️⃣ Run: pip install neo-mcp 3️⃣ Add config to your editor. Now when you ask your Claude Code (or) Codex to build a complex AI project that requires exploration, experimentation (or) evaluation It routes to NEO automatically. And your Claude credits stay safe!!

Try out @withneo MCP for FREE: 👉 Neo MCP is the future of AI/ML development. Stop burning credits on tasks Claude wasn't built for!!

This is a seriously useful Claude Code workflow 🔥 Instead of burning expensive AI credits on complex AI/ML tasks, offload them to specialized, cost-effective agents and keep Claude Code focused on what it does best. Saving up to 75% on credits while getting the same work done is a huge win. 👀

@LearnWithBishal interesting approach! curious how you handle the agents' reliability and uptime?

This is a serious cost optimization. 🔥 Offloading the heavy AI/ML work instead of burning Claude Code credits on everything makes a lot of sense. 75% savings is huge.

A smarter workflow is to let Claude Code handle what it does best, while offloading complex AI/ML tasks to cost-effective agents through MCP.

Offloading heavy tasks to cost-effective agents while cutting AI credit usage by up to 75% is seriously impressive. Definitely worth trying!

75% saving is huge.

This is a clever way to reserve Claude credits for coding while offloading heavy experimentation elsewhere.

cost effective shows up clearly in the Claude Code part, and the presentation is clean.

Saving 75% on credits while upgrading the workflow? That’s worth a look. 🔥

This is a smart way to cut costs without sacrificing performance. 🔥

Does quality hold up with cheaper agents? Curious

seems cost effective.

75% savings is huge. Offloading the heavy AI/ML work instead of burning Claude Code credits makes a lot of sense. This MCP looks like a serious upgrade.

Well shared and meaningful Kept it real and classy.

75% savings on ML inference means the agents are doing 60-80% of the heavy lifting. Claude Code is now just a syntax editor with expensive taste in models.

This is such a smart way to cut unnecessary Claude Code usage. 🔥 Offloading the heavy AI/ML work while keeping the main workflow in Claude Code could make a huge difference in both cost and efficiency.

Great share

Interesting approach. Have you compared the quality of outputs between Neo and Claude Code for complex ML workflows?

Awesome share

👍

Saving 75% credits with MCP setup is huge! Thanks for the breakdown, checking out the tutorial right now...

This is a smart way to optimize Claude Code usage. Offloading the heavier AI/ML workloads to specialized agents can make a huge difference in both cost and efficiency.

Really interesting progress. AI is making things that once seemed impossible feel surprisingly accessible.

This is a game-changer for anyone using Claude Code for AI/ML workflows. Offloading heavy tasks to cost-effective agents while cutting AI credit usage by up to 75% is seriously impressive. Definitely worth trying!

This is a smart approach. Offloading the heavy AI/ML work instead of burning through Claude Code credits makes a lot of sense.

❤️✔️

Smart. Next: profile tasks by token + latency, then auto-route by cost

The smarter approach isn’t using one agent for everything. It’s giving each task to the agent best suited for it. That’s where agent orchestration gets interesting. @jarvixdotlive

75% credit savings is wild 👀 This MCP is definitely worth checking out. 🔥

Saving 75% credits with MCP setup is huge! Thanks for the breakdown, checking out the tutorial right now.

MCP as the dump valve for ML jobs is the honest architecture. Which cheap agent actually kept the notebook state?

Token costs on coding agents add up fast. Weve been tinkering with this internally, offloading heavy tasks to cheaper agents is the move. I came across McKinsey data, like 20% of orgs say AI operating costs constrain usage. smart approach with the MCP.