YATA-NODE Blog

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AI workflow

Articles tagged "AI workflow".

Column

Do You Separate Your Skill Types?

As your Skills pile up, split them by nature — single-task "work Skills" and flow-orchestrating "orchestration Skills" — to keep improvement loops and context light.

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Technical

Open-Weight AI Model Licenses: A Commercial-Use Cheat Sheet for Llama, Gemma, Flux & More

"Open" does not always mean free for commercial use. A model's weights carry a license layer separate from the code — sort them into three buckets (Apache/MIT, conditional community like Llama/Gemma, restricted/non-commercial). A commercial-use cheat sheet for LLM, image, and audio models, plus a use-case checklist and the version/EU/MAU traps.

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Technical

Where to Run Claude in 2026 — Nine Options Across Four Zones, and the Compliance Choice Behind Them

Not sure where to run Claude? Approval-only, full control, autonomous batch, or team rollout each fit a different place. We map nine options onto four zones and narrow to one by use case, budget, and data residency — with the US/EU compliance forces (HIPAA, FedRAMP, sovereign cloud) that drive the choice abroad.

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Security

ChatGPT & Claude Security Settings in Three Tiers (US/EU)

AI service safety splits into three tiers: enforced by settings, per-use discipline, and enterprise contracts. Training opt-outs and retention, the three mechanical layers for CLI and autonomous agents, a pre-input five-question checklist, and contract-only guarantees (ZDR, audit logs) — with the US/EU drivers (FTC, state laws, GDPR, AI Act).

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Technical

Virtualization & Sandboxes — VMs, Containers & AI Agent Isolation (US/EU)

Where to run code safely: a five-layer trade-off map (VMs, microVMs, containers, OS sandboxes, WebAssembly), how to try untrusted code safely, an eight-item AI-agent sandbox checklist (Claude Code / Codex), and the US/EU drivers (NIST SP 800-190, FedRAMP, EU CRA) — for indie developers and practitioners.

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Column

Are You Reusing the Structure of Your Skills?

Apply a Skill's top-of-file summary structure to the context files you feed AI — a two-layer shape that keeps context consumption down.

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One Way to Get Stable Output from AI

For work that needs stable output, narrow AI's role and bracket it with deterministic code — making peace with probabilistic wobble.

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