· 4 min read
Picking a Coding-Adjacent Model in 2026: A Selection Playbook
Model choice is now a routing decision, not a beauty contest. Here's how to match task, latency, and cost across the big providers.
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#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 5 min read
Everyone's Shipping ChatGPT Work. Here's What Breaks.
Bolting AI onto disconnected tools recreates the handoff tax it promised to kill. Here's why integration depth beats feature breadth in 2026.
· 5 min read
The Agent-to-Agent Protocol Problem Nobody's Solving
As AI agents start transacting on our behalf, the bottleneck isn't intelligence — it's the missing trust, identity, and handoff layer between them.
· 4 min read
AGI Is Coming? Fine. Your Spreadsheet Still Has Errors.
While leaders argue over AGI timelines, the winnable fight is narrow AI that checks its own document and spreadsheet output. Grounding beats grandeur.
· 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.
· 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
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.
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