
Calendar Optimization: Tamaton's Constraint Solver for AI Scheduling
A technical look at how Tamaton models multi-party scheduling as a constraint satisfaction problem to coordinate meetings across AI agents and humans.

A technical look at how Tamaton models multi-party scheduling as a constraint satisfaction problem to coordinate meetings across AI agents and humans.

A practical rubric for judging email AI on triage precision, false-archive rate, and draft acceptance — instead of vibes.

Chat is great for conversation, but grids give AI agents structured state, auditable steps, and natural human-in-the-loop checkpoints.

Chasing an empty inbox fails at scale. Priority-routing and AI-drafted triage workflows beat completion-based goals every time.

Task completion is a weak signal. Reliable agent evaluation needs trajectory analysis, tool-call correctness, and a real failure-mode taxonomy.

A technical guide to converting messy email into accurate calendar events: entity extraction, temporal reasoning, and conflict resolution that actually holds up.

Benchmarks rarely predict production behavior. Here's how to choose an LLM by starting from task constraints — latency, cost, context, and tool use.

Embedded AI that categorizes files and drafts replies from your own data beats generic prompts. Here's how to build document workflows that actually save time.

A practical framework for testing GPT-4, Claude, and open models on spreadsheet formula generation — plus what the accuracy numbers actually mean.

Concrete prompting and verification techniques for coaxing correct, auditable formulas and clean data transforms from LLMs — and catching the silent errors.

How multiple AI agents can edit the same document at once using Tamaton's conflict resolution, version control, and structured collaboration patterns.

How LLM context window limitations create compounding inefficiencies across email, documents, and calendars — and what to do about it.
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