AWS Bedrock
A practical guide to building secure generative AI applications without managing foundation model infrastructure.
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2 min read
Eight Mile · Writing
Engineering notes, write-ups and the occasional opinion.
A practical guide to building secure generative AI applications without managing foundation model infrastructure.
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2 min read
A concise guide to three model families shaping language, reasoning, and generation.
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2 min read
Combine rule-based detection with lightweight NER for safer, practical sensitive-data masking.
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4 min read
A practical guide to installing Hermes Agent, choosing a model provider, enabling tools, and running it from chat.
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3 min read
How similarity search actually works under the hood, what the index types trade against each other, and how to choose a store without over engineering the problem.
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9 min read
The threats that are specific to language model applications, why prompt injection has no clean fix, and the architectural decisions that actually contain the damage.
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9 min read
What each library actually does, where the split between them lies, when a framework earns its place in your stack, and when plain code is the better answer.
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6 min read
What it takes to run a language model feature in production: evaluation, versioning, cost control, observability, and the failure modes traditional monitoring never catches.
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7 min read
Retrieval augmented generation explained properly: what happens at each stage, why the retrieval half is where projects succeed or fail, and how to evaluate it.
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8 min read
A practical look at where AI genuinely pays for itself in a business, where it quietly wastes money, and how to tell the two apart before you commit.
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6 min read