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A neuron is just a weighted sum: build a perceptron in 20 lines of NumPy
Modern AI systems can contain billions of parameters, but the core idea behind a neural network starts with a much smaller building block.
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How diffusion models generate images, explained with a runnable toy
Diffusion models are among the most important ideas in modern GenerativeAI and ComputerVision. They power many image-generation systems by learning a simple but powerful concept:
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Choosing a vector database in 2026: pgvector vs the specialists
Retrieval-augmented generation (RAG) systems have moved from prototypes into production software. In 2026, choosing a vector database is no longer just about asking, “Can this store embeddings?”
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Image segmentation with Segment Anything and its successors
For years, image segmentation models were trained for specific tasks. If you wanted to segment roads, tumors, vehicles, or people, you typically needed a dedicated dataset with pixel-level annotations
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Context windows, KV-cache, and why long prompts get expensive
Modern large language models can accept remarkably long context windows. It’s tempting to assume that if a model supports 128K, 200K, or even 1M tokens, you should simply keep appending more informati
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Data leakage: the silent killer of ML projects
Machine learning projects rarely fail because the algorithm is not advanced enough. More often, they fail because the model learned from information it would never have at prediction time.
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Chunking strategies that actually matter for retrieval quality
If you’ve experimented with embedding models, vector databases, rerankers, or prompts, but your Retrieval-Augmented Generation (RAG) system still misses obvious answers, the bottleneck may be much sim
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Tokenization pitfalls: why your LLM miscounts characters and money
If you’ve ever asked an LLM to count letters in a word, estimate API cost, or enforce a strict character limit, you’ve probably seen surprising results.
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How transformer self-attention actually works, with a runnable toy
Transformers power modern large language models, image generators, coding assistants, and multimodal AI systems. The core idea behind them is surprisingly compact: self-attention lets every token deci