Engineering · Hybrid AI
A hybrid local/cloud platform connecting troubleshooting context, structured IT knowledge, runbooks, coding agents, and operational integrations.
Active engineering and integration work
Useful AI assistance depends on structured context, repeatable workflows, and a clear path for review.
Hermes brings troubleshooting assistance, documentation generation, runbooks, ticket context, and automation into a hybrid local/cloud workflow. Its purpose is to make real operational knowledge usable across repeated technical tasks.
Structured project knowledge and runbooks supply context. Local inference through LM Studio supports local model workflows, while cloud AI and coding agents provide an escalation path. Git branches and worktrees isolate engineering changes for review.
The platform includes real integration development alongside experiments. This does not mean every automation is deployed in production or that generated recommendations are authorized actions. Herdr is a separate project focused on the reliability of coding-agent execution and review.
Much of my work has been getting the pieces to communicate: preparing context, handling bad API responses, choosing a model, and keeping code changes separate until they’ve been reviewed.
Examples describe engineering methods. Client identities, endpoint identifiers, credentials, and ticket contents are omitted.
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This is an example of my experience, not an endorsement from an independent client. If you need similar help, take a look at my services or send me a description of the problem.