Field Notes
Build logs, strategy, and industry breakdowns. Specific over conceptual — written to be useful, not to fill a calendar.
Over six releases my assistant went from a good talker to something that does the work: it refreshes its own model menu, writes checklists with correct numbers, survives a first-run crash, lets you pick how much to trust it, and recovers when a model or a network goes quiet.
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Most AI tools leave you juggling API keys and dashboards while every model behaves differently. I rebuilt Seepient around one control room where every model is visible with its price, sign-in takes seconds, and every doorway behaves the same.
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My AI assistant used to depend on a single model provider, so every outage stopped my work. I rebuilt it around a multi-provider engine, and now it keeps working no matter which AI company has a bad day.
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Why most business AI projects turn into expensive maintenance headaches, and how a one-brain approach gives you total control, lower costs, and zero vendor lock-in.
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How I rebuilt Seepient's permission UX around a single, complete approval screen, and made every granted permission survive a crash, so your AI assistant's work is never silently undone.
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AI development tools speed up development, but unguided code generation introduces hidden operational risks. Here's how I enforce strict governance over AI-built code.
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Why simple yes/no approval prompts fail when automating business tasks, and how my 4-stage governance pipeline makes autonomous AI execution safe and fully auditable.
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How a disciplined, six-layer modular architecture protects Seepient from technical debt, isolates system failures, and guarantees zero vendor lock-in.
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Why single-prompt chatbots fail for busy teams, and how Seepient works as a persistent, secure digital colleague across desktop and cloud environments.
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