· 4 min read
Why Your Inbox Is the Best RAG Corpus You Already Own
Email threads encode decisions, commitments, and relationships better than any wiki — making your inbox the highest-signal grounding source for an AI assistant.
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
Structured Output: Getting Reliable Tables From LLMs
Practical patterns for coaxing consistent, spreadsheet-ready data out of LLMs — schema enforcement, validation loops, and the pitfalls that quietly corrupt your tables.
· 4 min read
Evaluating AI Agents: Beyond Pass/Fail Benchmarks
Final-answer accuracy hides how agents actually work. Here's why trajectory, tool-use, and recovery metrics matter — plus a practical scoring rubric.
· 5 min read
How to Evaluate an AI Agent That Touches Your Inbox
Benchmark scores won't tell you if an email agent is safe to trust. Here's a practical eval harness built on task completion and harm metrics.
· 4 min read
Evaluating AI Agents on Multi-Step Office Tasks
Single-turn benchmarks miss what matters. Here's a practical eval harness for agents that move data across email, docs, sheets, and calendar.
· 4 min read
Model Selection in 2025: When Smaller Beats Frontier
A practical framework for routing tasks to small, fast models or frontier reasoning models based on latency, cost, and failure cost.
· 4 min read
The Hidden Cost of Context Windows in Agent Workflows
A quantitative look at how context window limits quietly degrade agent performance on email threading and document synthesis—and what to do about it.
· 4 min read
How to Choose an LLM for Each Task, Not Just the Best One
Stop defaulting to the largest frontier model. Use a cost-latency-quality matrix to route each task to the right LLM and cut spend without hurting output.
· 5 min read
How to Evaluate an AI Inbox Assistant Before You Trust It
A concrete eval rubric for email AI — measuring intent precision, action safety, and hallucinated commitments — plus test cases you can run today.
· 5 min read
The Quiet Shift to Smaller, Specialized Models in 2025
Frontier models aren't always the answer. For inbox and search work, routing to small fine-tuned models is quietly becoming the default architecture.
· 4 min read
Spreadsheets as Agent Workspaces: Beyond the Formula Bar
Why the humble spreadsheet is one of the best environments for AI agents to read, write, and verify data — and why grounded tabular reasoning beats free-text prompting.
· 4 min read
How to Evaluate an AI Agent Before You Trust It With Your Inbox
A concrete eval methodology for action-taking agents: measure task success, failure recovery, and over-action risk before you hand over the keys.
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