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Topics Everyone Is Talking About No370
Claude Code Sends 33K Tokens Before Reading Your Prompt; OpenCode Sends 7K • Ask HN: Should AI-Generated Articles Be Labeled? • I Love LLMs, but Not the Hype • Irish Data Centers Now Consume 23 of the Nations Electricity • Chromium 148 Makes Math.tanh an OS Fingerprinting Signal…
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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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Topics Everyone Is Talking About No369
New Federal Rule Ties College Aid to Graduate Earnings • Internet Pioneer Vint Cerf Retires After Two Decades at Google • Mindwalk Visualizes AI Coding Sessions in 3D • Researchers Discover That Some Simple Fluids Can Fracture • Goeteia Brings Pure Scheme to Modern WebAssembly…
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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
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Object detection today: from YOLO to open-vocabulary models
Object detection sounds simple:
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The bias-variance tradeoff, finally explained with runnable plots
Machine learning models rarely fail because they are too simple or too complex by accident. More often, they fail because they learn the wrong amount of information from data.