Replies to post #761 on Giga Factories and Battery Companies
05/04/26 9:44 AM
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05/05/26 3:54 PM
05/06/26 7:51 AM
MCP, RAG, and Skills are not alternatives. They solve different parts of the same problem.
Most people confuse them because they all involve "giving AI more capability." But they operate at completely different layers.
MCP: The Connection Layer MCP is about connectivity. Your model can't talk to Slack, search engines, or databases on its own.
MCP fixes that with a standardized protocol:
? Query comes in
? MCP Client picks the right server
? Server fetches data or triggers an action
? Everything gets sent back to the model
05/06/26 11:10 AM
05/07/26 12:49 AM
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05/15/26 3:36 PM
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05/27/26 9:03 PM
MU-StarSeeker in MU-3.0 release expands Molecular Universe from AI-assisted research workflow to agent-managed materials discovery automation, from molecule search and formulation optimization to cell performance prediction and manufacturing quality guidance.
Traditional battery R&D is slow, fragmented, and resource-intensive. Developing and commercializing a new chemistry could take 10 years to go from lab-scale R&D through A-sample, B-sample, C-sample and finally SOP, involving tedious manual iterations across materials search, formulation, simulations, testing, and validation. Starting with MU-3.0 release, MU-StarSeeker is built to disrupt that traditional model by turning isolated research steps into automated and continuously learning agent-managed workflow.
Key Advances in MU-3.0 Include:
1. MU-StarSeeker, Agent-Managed Material Discovery Workflow Automation
2. MU-3.0 Supports Both Lithium and Sodium Chemistries
3. Closed-Loop Dry and Wet Data Through Autonomous Labs (“A-Labs”)
05/28/26 12:36 PM
06/01/26 7:41 AM
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