
Context Windows Are Lying to You: The Lost-in-the-Middle Tax
Bigger context windows don't guarantee better recall. Here's where models actually lose information — and how to structure prompts so they don't.

Bigger context windows don't guarantee better recall. Here's where models actually lose information — and how to structure prompts so they don't.

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

Why keyword search fails in modern workspaces, and how semantic search, metadata, and permission-aware retrieval combine to make file search actually usable.

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

Skip the 'long context killed RAG' debate. Here's a practical decision framework based on cost, latency, recall, and freshness.

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.

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

A step-by-step walkthrough for creating specialized agents in Tamaton's agent framework, focused on document analysis and structured data extraction.

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