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How Machine Learning Models Are Trained and Used
A plain explanation of how machine learning models learn from datasets, what parameters are, and how training differs from inference when you build a project.
Tech Horizons
AI for Makers
Practical concepts for builders who want to use machine learning in projects.
Choosing Hardware for AI Inference
A concept-level comparison of CPUs, graphics cards and dedicated accelerators for running trained models, and the factors that should drive your choice.
Embeddings and Semantic Search Explained
How text is turned into vectors that capture meaning, how similarity search works, and what to watch for when building search into a maker project.
How Computer Vision Detects Objects
How image models find and label objects using boxes, scores and learned features, plus the lighting, data and hardware factors that cause detection errors.
Judging Whether an AI Feature Is Worth Adding
A practical framework for weighing benefit, cost, failure modes and long-term upkeep before adding a machine learning or language model feature to a project.
Privacy Questions When Sending Data to Cloud AI
A checklist of privacy questions to ask before sending personal or project data to an online AI service, from retention and training use to access and exposure.
Prompt Design Basics for Reliable Outputs
How to give a language model clear context, constraints and examples, and how to check its answers so your project gets more consistent, usable results.
Running Language Models on Local Hardware
The memory, speed and privacy trade-offs of running a language model at home compared with calling an online service, with sizing ideas and common pitfalls.
Tiny Machine Learning on Microcontrollers
How small neural network models run on low-power microcontrollers, what tasks suit them, how models are shrunk to fit, and where the limits are.
Training Data Quality and Bias
How data collection, labelling and sampling choices shape model behaviour, with common sources of bias and practical ways for makers to check their own datasets.
Why Language Models Make Things Up
Why fluent language model output can be wrong, how next-word prediction produces confident errors, and practical habits for verifying what a model tells you.
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