· 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.
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From the Tamaton team
Practical writing on productivity, AI, and building software.
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· 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
Zero-Trust for AI Agents: Scoping Permissions in Your Stack
Treat every agent action as untrusted by default. Here's how to build permission boundaries around inbox, files, and calendar instead of handing out blanket access.
· 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.
· 5 min read
Most AI Agents Nobody Needs: The Utility Test
A practical framework for deciding when an autonomous agent actually beats a prompt or a plain feature — and when it's just expensive theater.
· 4 min read
How to Choose an LLM in 2026: A Buyer's Map
A practical selection matrix across reasoning, coding, latency, and cost — plus why routing one model per task beats crowning a single winner.
· 4 min read
Fine-Tuning vs Prompting for Knowledge Work in 2026
LoRA and lightweight fine-tuning are cheaper than ever, but for most inbox and document tasks, prompting plus retrieval still wins. Here's the decision tree.
· 4 min read
RAG for Inboxes: Grounding Email Replies in Real Context
Email is a messy, threaded corpus. Good retrieval design — thread chunking, dedup, recency weighting — decides whether AI replies are trustworthy.
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