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If you already know your job well, AI raises a new question: which parts of your work could you do better, faster, or at a larger scale with an assistant that reads, writes, analyzes, searches, and sometimes acts? This episode maps what's available from OpenAI, Anthropic, and Google beyond basic c…
When you type a question into an AI assistant and watch the answer appear word by word, what's happening underneath is inference: the process of running a trained model to produce that answer. This episode follows one request through the full inference path, from tokenization to the KV cache to pr…
When an AI model answers slowly, the problem usually isn't its intelligence — it's how the serving system moves data through a GPU. This episode goes one level below the basic inference loop covered in an earlier episode, into GPU memory, the key-value cache, attention kernels, and the scheduling …
When a large language model generates text one token at a time, even a fast GPU hits a sequential bottleneck. This episode explains speculative decoding, an inference technique that prepares several tokens at once and checks them together, and when the extra computation pays off. Ordinary genera…
Rechtliche Hinweise: Der auf dieser Seite eingebettete Podcast und das Bildmaterial stammen von Dan W, das Eigentum seines Eigentümers ist und nicht mit Listen Notes, Inc. verbunden ist oder von diesen unterstützt wird.