What is Temperature?
Temperature is the sampling dial that sets how random a model's next token feels. What low and high settings do, and where builders set it for factual versus creative work.
1 min read↗
Ideas, collected
Essays.
Essays, guides and observations. Find something worth sitting with.
What is Inference?
Inference turns a trained model and a fresh prompt into an answer, running the weights forward instead of learning. How serving differs from training, and where builders spend their latency and cost budget.
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What is Quantization?
Quantization shrinks a model by storing its weights in fewer bits so it fits smaller GPUs and laptops. How lower precision trades a little accuracy for speed and where builders reach for it.
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What is Fine-tuning?
Fine-tuning keeps training a ready-made model on focused examples so it follows one task, style, or domain more reliably. What changes under the hood, what it costs, and when builders choose it over prompting alone.
1 min read↗
What is Embeddings?
Embeddings turn text, images, or rows into lists of numbers that place similar meanings close together. How the vectors are made and where builders use them for search and grouping.
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What is RAG?
RAG grounds a language model in documents retrieved at query time instead of memory alone, cutting hallucinations on facts the model never learned. How the retrieve-then-write loop works and where builders reach for it.
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What is prompt injection?
Prompt injection hides hostile instructions inside data a language model reads, steering it to disobey its real task. How the trick works and how builders counter it.
1 min read↗
Hello, machines: why we started this blog
Why Machine Made Worlds exists: a manifesto for slow, useful writing about AI and automation in a noisy web.
1 min read↗
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