· 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.
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
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.
· 4 min read
The Calendar Is a Reasoning Problem, Not a UI Problem
Most scheduling agents fail because they treat the calendar as slots to fill instead of constraints to reason about. Here's what real calendar reasoning requires.
· 4 min read
Does Your AI Tool Leak IP? A Data-Flow Audit Guide
A practical framework for tracing exactly what leaves your environment when AI touches your documents, code, and email — before it becomes someone else's training data.
· 4 min read
How Tamaton Selects Models Per Task, Not Per Vendor
A routing layer picks the right model for each request — summarization, extraction, or reasoning — so quality and cost stay flexible instead of locked to one provider.
· 4 min read
Agentic AI vs AI Agents: Why the Distinction Matters
A single-task AI agent and a goal-directed agentic system are not the same thing. Here's the capability boundary, and what each is actually good for.
· 5 min read
Most AI Agents Nobody Needs: The Utility Test
A practical framework for deciding when an autonomous agent actually beats a prompt or a plain feature — and when it's just expensive theater.
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