· 4 min read
How AI Agents Should Prove Who They Represent
Authentication tells us an agent is real. Delegation tells us who it's acting for — and that's the primitive we're missing. Here's how to build it.
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
· 4 min read
Zero Standing Privilege for AI Agents: A Practical Guide
AI agents are non-human identities that deserve just-in-time, scoped access — not the permanent keys most teams still hand out. Here's how to do it.
· 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.
· 4 min read
HalluSquatting: When Your Coding Agent Trusts a Fake Repo
AI coding agents love to invent package names. Attackers register those names and wait. Here's how the hallusquatting attack works and how to defend against it.
· 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
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.
· 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
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.
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