GitHub MCP Server turns AI into repo ops
I break down GitHub’s MCP Server and give you a copy-ready config for wiring AI tools into repos, issues, PRs, and workflows.
Timeline
A practical breakdown of Databricks foundation-model querying, with the auth, SDKs, and a copy-ready request template.
I broke down GitHub Copilot SDK’s BYOK path, tool control, and multi-language setup into a copyable integration template.
AI Data Operations handles the data pipeline; MLOps handles model training and deployment. The split matters when production AI starts failing.
Databricks now logs model-serving requests and responses to Unity Catalog Delta tables for monitoring, debugging, and agent tracing.
I break down Alibaba Cloud’s HappyHorse 1.1 and give you a copy-ready way to wire enterprise AI video into a real workflow.
Databricks external model serving endpoints need centralized governance, not loose self-service.
A practical MLOps roadmap you can copy to go from basics to production-ready workflows in 2026.
Prompt versioning is production infrastructure, not a documentation habit.
I break down Databricks’ supported foundation models into a practical region-and-endpoint cheat sheet you can copy.
I break down Dometrain’s advanced system design course into a copyable template for distributed systems, rollout safety, and multi-tenant ops.
A practical breakdown of Databricks model serving setup, permissions, and endpoint config with a copy-ready template.
I break down Zvec’s in-process vector DB design and give you a copy-ready template for local hybrid search.
I break down Databricks Model Serving and give you a copy-ready deployment template for LLM endpoints.
I break down GitHub’s MCP Server and give you a copy-ready config for wiring AI tools into repos, issues, PRs, and workflows.