· 5 min read
The Productivity Tool Myth: Integration Beats Installation
Model quality isn't your bottleneck anymore — context portability is. We map where the time actually goes in AI-assisted work, and what to fix first.
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
· 5 min read
Your Inbox Is the Best RAG Corpus You'll Ever Have
Mailboxes, files, and calendars are structured, timestamped, and permissioned — which is why naive vector search fails on them. Here's a retrieval design that works.
· 5 min read
How Tamaton's AI Reads Your Inbox So You Don't Have To
A concrete walkthrough of Tamaton's inbox triage agent: how 84 unread messages become a ranked queue with searches run, drafts written, and calendar holds already placed.
· 5 min read
When Your Agent Says "I Might Be Wrong": Honest AI Triage
Confident guessing is the most expensive failure mode in AI workflows. Here's how surfaced confidence and cited sources turn inbox triage and search into something you can actually trust.
· 5 min read
Grounding Is the New Prompting: How to Stop Agents Guessing
Clever prompts don't make agents accurate — verified, fresh sources do. Here are the grounding architecture patterns that keep AI agents honest.
· 5 min read
Keeping LLMs Current: Knowledge Editing vs. RAG
Pretraining freezes your model's worldview on a date that has already passed. We compare knowledge editing research with retrieval as strategies for keeping an AI assistant accurate.
· 4 min read
RAG on Your Own Inbox: What Actually Works
Email is the messiest RAG corpus you'll ever ingest. Here are the chunking, dedup, and recency choices that make inbox retrieval trustworthy.
· 4 min read
RAG for Your Own Files: Why Search Is the Hard Part
RAG over your email, docs, and spreadsheets usually fails at retrieval, not generation. Better search and chunking beat a bigger model almost every time.
· 4 min read
How Tamaton Grounds Answers Across Inbox, Docs, and Calendar
A concrete look at how a unified data layer lets AI cite the actual email or spreadsheet cell instead of paraphrasing from memory.
· 4 min read
RAG Isn't Search, and Search Isn't RAG
Retrieval-augmented generation and enterprise AI search get conflated constantly. Here's the actual difference — and when to use RAG instead of plain search.
· 4 min read
Production Bugs Live in the Past: The Case for Context Storage
Coding agents only see the present, so they keep re-solving problems your team already solved. The fix isn't a smarter model — it's durable, searchable context.
· 4 min read
The Circular Trust Problem: When LLMs Verify LLMs
Using one model to fact-check another can turn errors into a mutual admiration society. Here's how the recursion happens and how to break it.
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