· 5 min read
Designing an Inbox an Agent Can Actually Use
Most productivity APIs were built for humans clicking buttons. Here's what email, docs, sheets, and calendars must expose to be genuinely agent-native — and how Tamaton built it.
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
Confident, Wrong, and in Your Spreadsheet
A hallucinated sentence is awkward. A hallucinated formula is expensive. Here's a taxonomy of where generative AI fails in structured work — and the guardrails that catch each type.
· 5 min read
Package Hallucinations and Other Ways Agents Invent Reality
A field guide to grounding failures — fabricated npm packages, invented spreadsheet columns, phantom calendar events — and the design patterns that catch them before they ship.
· 4 min read
The Spreadsheet Is the Best Eval Harness You Already Own
You don't need an ML platform to measure AI quality. A well-structured sheet turns vibes-based prompting into something you can actually score and improve.
· 5 min read
Why Time-Series LLMs Hallucinate Forecasts (And When Not to Trust Them)
A practitioner's guide to when LLM-based forecasting earns its keep and when it's confidently extending a trend line into thin air.
· 4 min read
Aggregate Scores Lie: What EduFairBench Teaches AI Evaluation
Averaged benchmark scores hide the subgroup failures that break real workflows. Here's how to build disaggregated, reproducible evals for triage, summaries, and spreadsheets.
· 4 min read
When LLMs Forecast: Hallucination in Time-Series Models
New research shows LLMs hallucinate when forecasting time-series data. Here's why that matters before you point one at your spreadsheet.
· 4 min read
AGI Is Coming? Fine. Your Spreadsheet Still Has Errors.
While leaders argue over AGI timelines, the winnable fight is narrow AI that checks its own document and spreadsheet output. Grounding beats grandeur.
· 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
How to Evaluate an Agent That Edits Your Spreadsheet
Spreadsheet edits demand cell-level, deterministic evaluation — accuracy, reversibility, and formula integrity — not vibes-based scoring.
· 4 min read
Spreadsheets as Tool Use: Letting LLMs Compute, Not Guess
Why LLMs should generate and execute formulas instead of hallucinating math — plus concrete patterns for verifiable spreadsheet automation.
· 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.
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