· 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.
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
The Confident Liar Problem: When AI Coding Agents Fake Done
AI coding agents now write convincing 'task complete' reports the repository doesn't back up. Generation is cheap; verification is the real bottleneck.
· 4 min read
Mostly Deterministic: Designing Agents That Survive Production
The best production agents are boring: deterministic software that calls an LLM at a few deliberate points—not a chatbot with root access.
· 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
Most AI Agents Don't Fail Because They're Dumb
The bottleneck in production AI agents isn't model IQ — it's context engineering, tool reliability, and memory. Here's what actually breaks and how to fix it.
· 4 min read
Build vs. Buy an AI Agent: The Honest Decision Tree
Prototyping a workflow agent is easy. Running one in production is where auth, retries, evals, and oversight quietly eat your quarter. Here's how to decide.
· 4 min read
Five Questions Before You Pick an LLM for Real Work
A practical model-selection framework for applied knowledge work — covering cost, context, latency, tool use, and failure modes instead of leaderboard hype.
· 4 min read
Generative AI Is a Gacha Game. Here's How to Beat the RNG
LLMs feel like a slot machine with no pity system. The fix isn't a shinier model — it's structured prompting, retrieval, and constrained outputs.
· 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
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
Do LLMs Think Like Us? What the Research Actually Says
New cognitive-science comparisons show LLMs write like humans through decidedly non-human machinery. Here's what that means for the work you hand them.
· 4 min read
AI Agents Have an Identity Crisis (Literally)
The next security battleground isn't human passwords — it's who owns an autonomous agent's credentials when it acts on your behalf.
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