0xgetz/nebula-code — AI coding agent with federated learning, skill — E8 Intelligence Research

Andrew Stewart Caldin

doi:10.5281/zenodo.20387508

May 26, 2026Open Access

0xgetz/nebula-code — AI coding agent with federated learning, skill — E8 Intelligence Research

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Key Points

The research aims to connect AI coding agents with federated learning, enhancing coding efficiency.
Explores integration of multiple breakthroughs from GitHub.
Assesses performance scores of the coding agent in a marketplace setting.
Average performance score of 0.25 is reported.
Establishes a connection with multiple breakthroughs to enhance functionality.

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Implication

Independent research explores an AI coding agent that utilizes federated learning, suggesting innovative coding solutions.

Abstract

Connects to 22 breakthroughs. marketplace, and multi-agent orc From GitHub (117,134,136,137,138,140,156,157,171,172,173,176,177,178,179,180,181,186,187,188,200,203). Avg score: 0.25 Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

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Cite This Study

Andrew Stewart Caldin (2026) studied this question.

synapsesocial.com/papers/6a168b430c924ddd1bd5a27c https://doi.org/10.5281/zenodo.20387508

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0xgetz/nebula-code — AI coding agent with federated learning, skill — E8 Intelligence Research

Key Points

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Abstract

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0xgetz/nebula-code — AI coding agent with federated learning, skill — E8 Intelligence Research

Key Points

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Implication

Abstract

Discussion

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