· 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.
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
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
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
The Agent Reliability Gap: Why 90% Isn't Good Enough
Multi-step agents compound their error rate — a 95%-accurate step becomes a coin flip after 15 actions. Here's the math and how to design around it.
· 4 min read
How to Evaluate an AI Feature When 'It Feels Better'
A practical eval playbook for AI-assisted knowledge work: build golden sets, run LLM-as-judge checks, and catch regressions without a research team.
· 4 min read
One Agent or Many? A Sober Take on Multi-Agent Hype
Multi-agent systems are trending, but most productivity work is better served by one well-scoped agent with good tools. Here's when orchestration actually earns its keep.
· 4 min read
How Tamaton Grounds Agent Actions in Your Real Data
A concrete look at how shared context across inbox, docs, sheets, and calendar gives AI agents verifiable grounding — and fewer confidently wrong actions.
· 4 min read
Project Memory: The Missing Layer in AI Assistants
A bigger context window won't fix a forgetful assistant. Persistent, structured project memory is what turns chat toys into long-running collaborators.
· 4 min read
Automating Inbox Triage With Agents: What Actually Works
A field-tested breakdown of which inbox tasks AI agents handle reliably today, and where human review still isn't optional.
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