AI infrastructure,
deeply explained.
Practical guides on deploying AI platforms, controlling costs, and building with LLMs — written for engineers and technical leaders.
How to stop a $12k AI bill before it happens — per-user token quotas explained
Shared API keys and no usage limits are a recipe for surprise invoices. Here's how per-user token quotas work and why every AI team needs them from day one.
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How to give every student their own AI workspace — without managing Kubernetes
Universities are deploying shared JupyterHub environments for AI courses. Here's what the setup looks like, what breaks, and how to avoid the common pitfalls.
Building a RAG pipeline for your research team — from document upload to intelligent query
Retrieval-augmented generation is the fastest way to make your team's knowledge base queryable by an LLM. Here's how to set one up that actually works in production.
How to run Claude, GPT-4, and Gemini side-by-side — model comparison for teams
Committing to a single LLM vendor is a mistake. Here's how to run multiple models in parallel, compare outputs, and route tasks to the right model for the job.
Fine-tuning vs RAG — which one does your research team actually need?
Both approaches make LLMs smarter about your domain. But they solve different problems, cost very different amounts, and fail in different ways. Here's how to choose.
How to set up an AI policy for your university before your IT department does it wrong
Most university AI policies are written by people who don't use AI. Here's what a practical, faculty-friendly policy looks like — and the infrastructure that makes it enforceable.
How to stop a $12k AI bill before it happens — per-user token quotas explained
Shared API keys and no usage limits are a recipe for surprise invoices. Here's how per-user token quotas work and why every AI team needs them from day one.
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