· 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.
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
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.
· 4 min read
AI Agents Are Exposing the Cracks in Your Observability
The tools you built for deterministic microservices can't debug an agent that reasons, retries, and improvises. Here's what your monitoring stack actually needs.
· 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.
· 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.
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