Why We Open-Sourced Our Time Engine but Not Our Safety Layer
Open-core means drawing a line. Here's why computation is open (trust demands it) and safety is commercial (sustainability demands it).
Technical articles on AI scheduling, temporal reasoning, and building calendar infrastructure for the agentic web.
Open-core means drawing a line. Here's why computation is open (trust demands it) and safety is commercial (sustainability demands it).
An SDR team recovered 13+ hours per day by replacing manual scheduling with 4 API calls. Here's the workflow, the math, and the code.
When two AI agents negotiate a meeting, you get distributed systems problems. Here's how protocol negotiation, deadlock avoidance, and fallbacks work.
A decision framework for developers: when to use calendar APIs directly, when to build your own time logic, and when you need scheduling infrastructure.
End-to-end walkthrough: how to build a scheduling agent that coordinates interviews across US, Europe, and Asia using 5 tool calls.
Property-based testing caught DST double-counting, leap year edge cases, and RRULE bugs that unit tests missed. Here's our proptest setup.
MCP connects agents to tools. A2A connects agents to agents. Scheduling operations need both — and a fallback for when the other side has neither.
Payments got Stripe. Messaging got Twilio. Scheduling is still duct-taped together. Here's why it needs dedicated infrastructure.
A 9am weekly standup crosses spring-forward. Here's what goes wrong with naive implementations and how deterministic tools get it right.
Every calendar MCP server today is a CRUD wrapper with no locking. AI agents make this dangerous. Here's why.
Step-by-step tutorial: install the MCP server, connect Google Calendar, and schedule a meeting in under 5 minutes.
TOON reduces calendar payloads by ~40% vs JSON. Here's how it works, when to use it, and why it matters for AI agent context windows.
Two AI agents, one free slot. Without locking, both book it. Here's the race condition no other calendar MCP server prevents.
We ran 5 real-world RRULEs through frontier LLMs and the Truth Engine. The results show why calendar math needs computation, not prediction.
Why we built a Rust-based RRULE expansion library with 9,000+ property-based tests — and why AI agents need it.
LLMs score below 50% on temporal reasoning. Here's why calendar tools need deterministic computation, not more prompting.