· 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
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.
· 5 min read
AI Agent Identity: Why Your Bot Needs Its Own Credentials
Borrowing a human's login is a security disaster waiting to happen. Here's why AI agents deserve their own scoped, revocable, auditable identities.
· 4 min read
RAG for Your Inbox: Retrieval Over an Email Corpus
Email is the hardest RAG target there is — threads, quotes, and recency all conspire against you. Here's how to design inbox retrieval that's actually accurate.
· 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
Shell Injection in AI Coding Agents: A Defense Playbook
Autonomous coding agents run commands you didn't write. Here's how to sandbox them like the untrusted executors they are — instead of trusting model alignment.
· 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
Real-Time Guardrails Beat Better Models for Code Quality
The Sonnet 4.6 guardrail results suggest output validation, not raw model choice, is the highest-leverage lever for reliable AI code.
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