· 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.
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
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.
· 5 min read
LLMs Beat Experts at Predicting Study Results. Now What?
A study found LLMs can forecast experiment outcomes better than human specialists. We unpack what that really proves — and how to use it without fooling yourself.
· 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
Most AI Agents Are Demos. Here's What Real Ones Do.
A field guide to separating agents that actually finish work from the reasoning-superhero hype — and the boring, bounded jobs they quietly win at.
· 4 min read
Picking the Right Model per Task: A Router's Playbook
Stop chasing one 'best' model. Route each task — drafting, summarizing, spreadsheet reasoning, coding — to whichever model wins on quality-per-dollar.
· 4 min read
Context Windows Are a Trap: Why RAG Still Beats 1M Tokens
Frontier models keep bragging about million-token context windows. For real knowledge work, disciplined retrieval still wins on accuracy, cost, and latency.
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