· 4 min read
Calendar Optimization: Tamaton's Constraint Solver for AI Scheduling
A technical look at how Tamaton models multi-party scheduling as a constraint satisfaction problem to coordinate meetings across AI agents and humans.
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
RAG vs Long Context: When to Retrieve, When to Stuff
Skip the 'long context killed RAG' debate. Here's a practical decision framework based on cost, latency, recall, and freshness.
· 4 min read
Evaluating AI Agents: Beyond 'Did It Finish the Task'
Task completion is a weak signal. Reliable agent evaluation needs trajectory analysis, tool-call correctness, and a real failure-mode taxonomy.
· 5 min read
Building Reliable Email-to-Calendar AI Agents
A technical guide to converting messy email into accurate calendar events: entity extraction, temporal reasoning, and conflict resolution that actually holds up.
· 4 min read
Choosing an LLM: A Decision Framework, Not a Leaderboard
Benchmarks rarely predict production behavior. Here's how to choose an LLM by starting from task constraints — latency, cost, context, and tool use.
· 4 min read
Evaluating LLMs for Spreadsheet Formula Generation
A practical framework for testing GPT-4, Claude, and open models on spreadsheet formula generation — plus what the accuracy numbers actually mean.
· 4 min read
Building Custom AI Agents in Tamaton: A Technical Guide
A step-by-step walkthrough for creating specialized agents in Tamaton's agent framework, focused on document analysis and structured data extraction.
· 5 min read
Tamaton's Document Editor: Real-time AI Collaboration Patterns
How multiple AI agents can edit the same document at once using Tamaton's conflict resolution, version control, and structured collaboration patterns.
· 4 min read
Why Your RAG Pipeline Retrieves the Wrong Chunks
Most RAG failures aren't the LLM's fault. Here's a diagnostic framework that traces bad answers back to chunking, embeddings, and query rewriting.
· 5 min read
The Hidden Cost of Context Windows in AI Productivity
How LLM context window limitations create compounding inefficiencies across email, documents, and calendars — and what to do about it.
· 4 min read
Memory Systems for Long-Running AI Productivity Agents
Architectural patterns that give agents persistent memory across emails, documents, and projects — without exponential token costs.
· 4 min read
The Latency Tax: Why Unified Data Layers Beat App-Switching
Context switching isn't just a human problem. For AI agents, it's a measurable performance tax that only a unified data layer can eliminate.
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