· 5 min read
Custom Agents in Tamaton: Give an AI a Standing Order
Move past one-off prompts. Build a persistent Tamaton agent that watches your inbox, files, and calendar, then acts on a schedule with human checkpoints.
Blog
From the Tamaton team
Practical writing on productivity, AI, and building software.
All#ai#ai-agents#ai-productivity#applied-ai#automation#calendar#context-switching#deep-work#documents#email#engineering#engineering-leadership#invisible-ai#knowledge-work#llm-evaluation#product-updates#productivity#prompt-engineering#remote-work#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 4 min read
RAG Isn't Dead: When Retrieval Beats a Bigger Context Window
A practitioner's framework for choosing retrieval-augmented generation over long-context prompting — based on cost, freshness, and grounding, not vibes.
· 5 min read
The Open-Source Tax: Who Pays for the AI Coding Boom
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.
· 4 min read
Small Models, Big Inbox: Where 200M-Param MoE Actually Wins
A recent 200M-parameter hobbyist MoE is a great reminder: email triage, classification, and routing rarely need frontier models. Here's where small wins.
· 4 min read
RAG Is Not Search, and Your Inbox Proves It
Retrieval-augmented generation quietly breaks on email and files because it ranks documents, not conversations. Here's why, and what to do about it.
· 4 min read
The GRASP Problem: Why General LLM Benchmarks Lie to You
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.
· 4 min read
Give Your Calendar an Agent, Not Another Assistant Bot
A scheduling bot books slots. A real AI calendar agent remembers, decides, and negotiates. Here's how to design the second one.
· 4 min read
AI Agents vs. Assistants: What Actually Changes for Your Work
The real difference between AI agents and assistants isn't intelligence — it's who owns the loop from goal to done, and what that demands from your tools.
· 4 min read
Choosing an LLM for Real Work, Not Benchmarks
A practical framework for picking the best AI model for productivity tasks — weighing latency, cost, and reliability over leaderboard scores.
· 5 min read
How We Let an Agent Touch Your Inbox Without Losing Trust
A product deep-dive on the permission, preview, and rollback design that makes an AI email agent safe to actually act — not just suggest.
· 4 min read
Evaluating LLM Output Without Fooling Yourself
Vibes-based testing is why AI features break in production. Here's how to build cheap, repeatable evals for summaries, extraction, and drafts.
· 4 min read
How to Evaluate a RAG System Before You Trust It
A concrete evaluation playbook for retrieval recall, grounding, citation faithfulness, and the failure modes that quietly wreck RAG systems.
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