AI & Automation
My AI work focuses on connecting models to useful tools and workflows, not training foundation models. Through personal projects and experiments, I explore coding agents, persistent memory, integrations, and automation.
My contribution is the integration: connecting tools, designing workflows, managing context, and validating results. The underlying models and frameworks are existing tools, not my own inventions.
Example: a connected personal assistant
SELECT * FROM personal_projects WHERE topic = 'assistant'
One personal project has explored how an assistant can combine conversation, long-term context, external tools, and recurring tasks. Examples include preparing email drafts, producing source-linked digests, recalling project decisions, and answering voice queries.
It was built from existing open-source parts such as Hermes Agent for the assistant and Hindsight for memory, with Home Assistant for the voice side.
Areas of experience
SELECT area, summary FROM ai_experience
Agent-assisted software development
Splitting work into implementation, tests and review, with review done by an agent that didn’t write the code. Handing context between agents through written briefs and shared rules, and letting a stronger model plan while simpler tasks go to cheaper ones.
Memory & personalization
Extracting facts, recalling context and consolidating memory, while keeping where each fact came from and telling confirmed information apart from guesses and outdated details.
Tool integration
Connecting models to APIs, MCP servers, the web and real browsers, and to productivity and home systems.
Research & productivity
Source-linked digests, product comparisons, keeping track of information, draft replies and follow-up tasks.
Voice & home automation
Experiments with voice interfaces, keeping simple local commands deterministic and sending only harder questions to an AI model.
Evaluation & reliability
Comparing models and agents on concrete tasks by quality, speed, cost and reliability. Experiments have included agents that summarize alerts with a likely cause, for a person to decide on, plus checking results, monitoring and making sure things can be restored.
Principles & lessons
SELECT lesson FROM experience
I prefer limited permissions, human review for sensitive actions, and verification against real systems. For simple or critical operations, deterministic automation is often the better choice.
- AI helps most with messy input: reading, summarizing, finding context, drafting.
- Memory is useful only when it keeps where a fact came from and whether it’s still true.
- An agent saying “done” is a claim, not a result. Tests and checks decide.