· 4 min read
RAG for Your Inbox: Retrieval Over an Email Corpus
Email is the hardest RAG target there is — threads, quotes, and recency all conspire against you. Here's how to design inbox retrieval that's actually accurate.
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
Context Windows Are a Trap: Why RAG Still Beats 1M Tokens
Frontier models keep bragging about million-token context windows. For real knowledge work, disciplined retrieval still wins on accuracy, cost, and latency.
· 4 min read
Shell Injection in AI Coding Agents: A Defense Playbook
Autonomous coding agents run commands you didn't write. Here's how to sandbox them like the untrusted executors they are — instead of trusting model alignment.
· 4 min read
The Calendar Is a Reasoning Problem, Not a UI Problem
Most scheduling agents fail because they treat the calendar as slots to fill instead of constraints to reason about. Here's what real calendar reasoning requires.
· 4 min read
Does Your AI Tool Leak IP? A Data-Flow Audit Guide
A practical framework for tracing exactly what leaves your environment when AI touches your documents, code, and email — before it becomes someone else's training data.
· 4 min read
Real-Time Guardrails Beat Better Models for Code Quality
The Sonnet 4.6 guardrail results suggest output validation, not raw model choice, is the highest-leverage lever for reliable AI code.
· 4 min read
LLM Quantization: What You Actually Lose at 4-Bit
A measured look at where 4-bit quantization degrades reasoning and retrieval — so you pick precision by task instead of chasing the smallest model.
· 4 min read
How Tamaton Selects Models Per Task, Not Per Vendor
A routing layer picks the right model for each request — summarization, extraction, or reasoning — so quality and cost stay flexible instead of locked to one provider.
· 4 min read
Evaluating AI Output When 'Looks Right' Isn't Good Enough
Fluent isn't the same as correct. Here's how to build task-level evals, groundedness checks, and regression testing into everyday AI features.
· 4 min read
From Chat to Workflows: What Makes a Stack Agent-Ready
Agent-readiness isn't about bolting on chatbots. It's about exposing structured tools, clean state, and observable actions across email, docs, and search.
· 4 min read
Model Selection for Agents: Match the Model to the Task
One model can't optimally draft, extract, schedule, and search. Here's a practical routing framework for choosing and switching models per task.
· 4 min read
Agentic AI vs AI Agents: Why the Distinction Matters
A single-task AI agent and a goal-directed agentic system are not the same thing. Here's the capability boundary, and what each is actually good for.
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