punkpeye/awesome-mcp-servers
Mar 2026 – Mar 2026
1 Month active
Languages Used
Markdown
Technical Skills
AI model fine-tuningGitHub integrationsdocumentationorganizationproject managementreadability
VC
PROFILE
Vc
During March 2026, Bazooka720 enhanced the punkpeye/awesome-mcp-servers repository by delivering comprehensive documentation and discoverability improvements for Tuning Engines MCP and CLI. Leveraging Markdown and project management skills, Bazooka720 introduced a new entry for LLM fine-tuning with training agents Cody and SIERA, added a visibility badge, and corrected repository metadata to ensure accuracy and clarity. The README was reorganized for better navigation, including refined tool ordering and domain-specific notes supporting sovereign-model fine-tuning with zero infrastructure. These updates improved onboarding and developer adoption by providing clearer guidance for integrating AI model fine-tuning capabilities into MCP/CLI workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
9
Activity Months1
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Work History
March 2026
5 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for punkpeye/awesome-mcp-servers. Delivered documentation and discoverability improvements for Tuning Engines MCP/CLI, including a new LLM fine-tuning entry with training agents Cody and SIERA, introduction of a visibility badge, and corrections to repository metadata. Also aligned README content, tool ordering, and domain-specific notes to support sovereign-model fine-tuning with zero infrastructure. Result: improved onboarding and faster developer adoption, with clearer guidance for integrating LLM capabilities into MCP/CLI workflows.
5 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for punkpeye/awesome-mcp-servers. Delivered documentation and discoverability improvements for Tuning Engines MCP/CLI, including a new LLM fine-tuning entry with training agents Cody and SIERA, introduction of a visibility badge, and corrections to repository metadata. Also aligned README content, tool ordering, and domain-specific notes to support sovereign-model fine-tuning with zero infrastructure. Result: improved onboarding and faster developer adoption, with clearer guidance for integrating LLM capabilities into MCP/CLI workflows.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage48.0%
Skills & Technologies
Programming Languages
Markdown
Technical Skills
AI model fine-tuningGitHub integrationsdocumentationorganizationproject managementreadabilityreadme enhancement
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