Deep dives into how we build Operon — the architecture, the decisions, and the problems we're solving for AI-assisted development.
I'd been using Claude Code for two months before I looked at the bill. Here's what 30 days of real usage data actually looks like — average cost, expensive sessions, and where the money goes.
Claude Code, Cursor, Codex, Gemini CLI, Aider — each works differently. But every interaction follows the same pattern: prompt → tool calls → response. We built a universal trace model and three data tracks to capture them all.
The #1 complaint about AI coding tools: the AI 'forgets' things mid-session. We built a context monitor that shows exactly which files are loaded, which dropped, and how many tokens remain — before hallucinations begin.
Every AI coding session generates dozens of architectural decisions buried in chat. We use Claude Haiku to extract, tag, and store them in a searchable knowledge base that persists across sessions and teams.
Most teams have no idea what they spend on AI coding tools until the bill arrives. We built per-developer cost attribution with spike detection and monthly budgets so you're never surprised.
Operon captures every prompt, response, and decision from your AI coding sessions. That data is sensitive. Here's why we chose SQLite, local-first sync, and optional cloud — not the other way around.
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