Engineering the
command center
Deep dives into how we build Operon — the architecture, the decisions, and the problems we're solving for AI-assisted development.
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How much does Claude Code actually cost? I tracked 30 days of sessions.
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.
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The trace model that unifies 5 AI tools
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.
Tracking context window usage in real time
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.
How we extract decisions from AI conversations
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.
Budget intelligence: knowing what AI costs you
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.
Local-first architecture: why local mode never uploads your code
Operon captures every prompt, response, and decision from your AI coding sessions. That data is sensitive. Here's why we chose SQLite, local-first storage, and opt-in cloud sync — not the other way around.
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