· 4 min read
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.
Blog
From the Tamaton team
Practical writing on productivity, AI, and building software.
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· 4 min read
Do LLMs Think Like Us? What the Research Actually Says
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.
· 4 min read
AI Agents Have an Identity Crisis (Literally)
The next security battleground isn't human passwords — it's who owns an autonomous agent's credentials when it acts on your behalf.
· 4 min read
Why 'AGI Levels' Charts Won't Help You Pick a Model
AGI-ladder charts are fun philosophy and useless procurement docs. Here's a practical framework for LLM model selection based on capability and cost.
· 4 min read
RAG Isn't Search, and Search Isn't RAG
Retrieval-augmented generation and enterprise AI search get conflated constantly. Here's the actual difference — and when to use RAG instead of plain search.
· 4 min read
How Tamaton Handles the Inbox an Agent Can't Stop Filling
When agents send and reply at machine speed, triage becomes a routing and evaluation problem. Here's how we approach ai email management built for bots, not just humans.
· 5 min read
Your Coding Assistant Skips the Boring Stuff. Here's Why.
LLM coding tools quietly omit rate limits, logging, and error handling — not because they can't, but because nobody named them. Here's how to prompt around it.
· 4 min read
Evaluating Agents: Why 'It Worked in the Demo' Lies
Task-completion scores hide silent failures. Here's a working method for AI agent evaluation using judged rubrics, adversarial cases, and regression suites.
· 5 min read
How to Evaluate an AI Agent That Books Your Meetings
Task-completing agents can't be graded like chatbots. Here's an eval framework built around side effects, reversibility, and the cost of being wrong.
· 4 min read
Multi-Agent Systems Are Coordination Problems, Not Model Problems
The bottleneck for multi-agent setups isn't a smarter model — it's shared state, clean handoffs, and knowing who owns the calendar. Coordination is the real work.
· 5 min read
The Metaphor Trap: Why We Keep Explaining LLMs Wrong
Physics and brain analogies make LLMs feel intuitive — and quietly lead us astray. Here's what to reason about instead.
· 4 min read
Production Bugs Live in the Past: The Case for Context Storage
Coding agents only see the present, so they keep re-solving problems your team already solved. The fix isn't a smarter model — it's durable, searchable context.
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