Tag
Applied AI
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The real trade-off between an API model and a local one
An API model costs nothing until the bill arrives, and a local model costs everything up front, before it answers a single question that matters.
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Fluency is not understanding
A model that sounds confident is not the same as a model that is right, and nothing in its fluent tone tells you which one you are actually getting from it.
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Blackwell quantisation on my own card
Q4_K_M more than doubled throughput and cut VRAM by two thirds on my RTX 5070 Ti, staying 98% similar to full precision. The interesting part was where that 2% lived.
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What the EU AI Act actually requires of a masking layer
The Act never says masking. Three articles, on data, logging and human oversight, each describe a property a masking layer either has or does not.
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Detecting PII before the model sees it
Two registered experiments, one frozen dataset, two detectors that each miss a different category. The entity-level numbers say which, and by how much.
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Permissions that travel with the person
An AI assistant should own no permissions. It borrows the rights of the person using it, and that single design choice bounds every attack path.
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What an internal MCP server actually solves
An internal MCP server does not make the model smarter. It gives authentication, permissions and audit a place to live between assistants and data.
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The masking layer matters more than the model
A masking layer decides what sensitive data ever reaches a language model. Why that design choice outweighs model selection in production AI.
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AI automation inside a cross-border logistics operator
Designing and running the AI integrations across a logistics company's processes: support triage, financial reconciliation, and grounded access to internal data.