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Running AI Models in the Browser with Transformers.js
Running real AI model inference directly in the browser with Transformers.js, no server or API key required.
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How to Benchmark AI Models for Your Own Use Case
How to benchmark AI models for your own specific use case, and why public benchmarks like MMLU frequently don’t predict real-world task performance.
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Federated Learning: Training AI Without Centralizing Data
Federated learning explained: training a shared AI model across many devices without ever centralizing the raw underlying data.
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Quantization Explained: How AI Models Get Smaller
How quantization shrinks AI models by reducing weight precision, why it usually doesn’t wreck quality, and how to quantize a model yourself with llama.cpp.
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Understanding Open-Weight AI Model Licenses
Understanding open-weight AI model licenses: why “open” doesn’t automatically mean unrestricted, and what to check before commercial commitment.
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Data Labeling: The Unglamorous Work Behind AI Models
Data labeling basics: the unglamorous, foundational work behind AI models, and the quality control techniques that keep flawed labels from silently degrading model quality.
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Why Do AI Models Hallucinate? A Practical Explanation
Why AI models hallucinate: it’s an inherent consequence of next-token prediction, not a simple bug, and why that means mitigations reduce but don’t eliminate it.
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AI Model Training vs Inference: What’s Actually Different
AI model training vs inference explained: the fundamentally different computational profiles, and why they call for genuinely different hardware.
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AI Model Quantization Formats Compared: GGUF, AWQ, and GPTQ
AI model quantization formats compared: GGUF’s CPU-friendly flexibility versus AWQ and GPTQ’s more sophisticated GPU-optimized calibration.
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AI Model Cards: Understanding What You’re Actually Using
AI model cards explained: what genuinely good documentation covers, and why reading a model’s stated limitations matters before deploying it.