Public OpenCAS runtime snapshot and release docs: local-state agent runtime, dashboard controls, memory, scheduling, channels, and provider-routed model access.
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Updated
May 22, 2026 - Python
Public OpenCAS runtime snapshot and release docs: local-state agent runtime, dashboard controls, memory, scheduling, channels, and provider-routed model access.
Windows desktop vision automation runtime: Tauri shell, Python backend, OpenCV/YOLO detection, FSM orchestration, packaged builds.
Offline founder operating system for customer onboarding health, activation risk, SLA risks, and founder attention queues.
Founder-facing retention, renewal, expansion, and customer health operating system.
Payments business KPI engine for monthly merchant reporting, variance diagnosis, SLA alerts, and executive summaries.
Offline-first AI workflow ROI decision system for early-stage founders.
Evidence and Verification Agent for agent-system observability, review gates, and operator-facing verification.
Operations KPI automation for SLA compliance, backlog visibility, breach drivers, and team scorecards.
Generate board packs, investor updates, risks, decisions, charts, and HTML reports from startup metrics CSVs.
Public-safe demo of the Gauges Green AI harness for senior operators
Founder-facing product feedback and roadmap prioritization operating system.
Post-call intelligence system for founder-led sales conversations.
Founder OS revenue engine for finding funnel leaks, prioritizing execution, and drafting investor updates.
Founder-facing hiring and talent pipeline operating system.
AI operating-system style skills and workflows for business operators: research, outreach, reporting, memory, content, and automation.
AI GTM workflow that turns target accounts into ICP scoring, pain hypotheses, emails, follow-ups, and founder-call briefs.
Personal AI pipeline for job hunting. Multi-source scraping + tiered LLM scoring + per-question answer drafting. Built by directing Claude Code; no hand-written code.
Structured self-reports for long-running LLM sessions: operator signals, explicit uncertainty, no claims of model feelings.
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