Griffin AI vs Self-Hosted Llama: Real Costs
Self-hosting Llama looks cheap on paper. The real costs — GPUs, operations, engineering — make the comparison less obvious than the list price suggests.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Self-hosting Llama looks cheap on paper. The real costs — GPUs, operations, engineering — make the comparison less obvious than the list price suggests.
AI-for-security metrics that show up on board slides are different from the ones engineers use day-to-day. Designing both sets properly is the work.
Crypto misuse is not about broken algorithms. It is about misused parameters, missing checks, and the gap between "it compiles" and "it is secure."
Claude's Computer Use lets an agent drive a GUI. For security, this is powerful and dangerous in equal measure. The architecture around it matters.
Most security teams are sitting on hundreds of stale findings. Here is how to clear an aged vulnerability backlog with bulk remediation that actually merges.
Synthetic eval benchmarks are controllable. Real-world data is messy. The gap between performance on each is usually large, and vendors prefer one over the other for a reason.
AI coding assistants promise productivity but expand the data leakage surface in specific, mappable ways. The paths, the mitigations, and what enterprise policy actually looks like.
Explainable AI makes model decisions inspectable so security teams can trust, audit, and defend them. Here is what that means in practice.
A senior engineer's take on the confused deputy problem in AI agent tool use, why it keeps reappearing in 2026, and the architectural patterns that actually fix it.
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