
How Tamaton Grounds Answers Across Inbox, Docs, and Calendar
A concrete look at how a unified data layer lets AI cite the actual email or spreadsheet cell instead of paraphrasing from memory.

A concrete look at how a unified data layer lets AI cite the actual email or spreadsheet cell instead of paraphrasing from memory.

New cognitive-science comparisons show LLMs write like humans through decidedly non-human machinery. Here's what that means for the work you hand them.

Retrieval-augmented generation and enterprise AI search get conflated constantly. Here's the actual difference — and when to use RAG instead of plain search.

Physics and brain analogies make LLMs feel intuitive — and quietly lead us astray. Here's what to reason about instead.

Using one model to fact-check another can turn errors into a mutual admiration society. Here's how the recursion happens and how to break it.

Bigger context windows won't fix agents that forget. Persistent, structured project memory is the real unlock for sustained knowledge work.

Email and calendar data breaks most RAG setups because relevance is temporal and social, not just semantic. A bigger embedding model won't save you.

Bolting AI onto disconnected tools recreates the handoff tax it promised to kill. Here's why integration depth beats feature breadth in 2026.

While leaders argue over AGI timelines, the winnable fight is narrow AI that checks its own document and spreadsheet output. Grounding beats grandeur.

A practitioner's framework for choosing retrieval-augmented generation over long-context prompting — based on cost, freshness, and grounding, not vibes.

AI coding assistants generate more PRs and issues than any human maintainer can triage. That review debt is a real cost — and it's landing on volunteers.

Leaderboard scores predict almost nothing about how a model performs on your actual domain tasks. The GRASP paradigm shows why — and what to measure instead.
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