Engineering & coding
AI can explain code, propose changes, and help build small programs. A working-looking interface is not enough: check calculations, error handling, data access, and maintenance. Start with a reversible project whose behavior you can test independently.
Practical / technical · 3 min read
Engineering & coding
Local AI runs a model on your own hardware instead of sending every inference request to a hosted service. It can support offline work and greater control over data flow, but performance depends on the model and hardware. Verify that the application is actually using a local model.
Curious / technical · 2 min read
Engineering & coding
Retrieval-augmented generation, or RAG, finds relevant source material and supplies it to a model before the answer is generated. It can make an assistant more useful with current or private documents. It does not guarantee correct retrieval, accurate interpretation, or appropriate access control.
Technical / engineer · 2 min read
Engineering & coding
An AI evaluation compares a system against defined tasks and criteria. Use representative examples, expected outcomes, and meaningful failure cases. Record quality, cost, delay, and review effort together. A public benchmark is useful context, but it cannot replace testing your own workflow.
Technical / engineer · 3 min read
Engineering & coding
GitHub Copilot is an AI coding assistant that can offer code suggestions, answer questions, and support development workflows. Use it with a clear specification and a way to verify changes. It is a separate product from Microsoft’s general Copilot chat experience.
Technical / engineer · 2 min read
Engineering & coding
Ollama provides a way to run and use language models, including local models on supported computers. Start with a model that fits your hardware and a small text task. Confirm whether your chosen model runs locally or uses a cloud service before treating the workflow as private or offline.
Technical / engineer · 2 min read
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