· 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.
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
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
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
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
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.
· 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 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
Can LLMs Predict Human Behavior? What the Research Says
New studies suggest LLMs can forecast social-science and neuroscience results. Here's what that actually means for knowledge work — and what it doesn't.
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