Every explainer we've published, newest first. Filter by topic to narrow it down.
Anthropic now embeds an invisible watermark in Claude's text at the moment it's generated — not metadata, but a pattern baked into word choice itself. Here's how that works, what it means for SEO, and why open-weight models can't be forced to carry the same signal.
Tool calling, orchestration, memory, tracing, guardrails, agentic workflows. Underneath all of it is a loop that fits in about fifteen lines of code, plus everything you need to keep it alive once real users show up.
CUDA isn't a language, it's NVIDIA's whole GPU computing platform. Here's what a kernel launch actually does, how threads and blocks work, and why deep CUDA usage becomes so hard to leave.
I built cache-augmented generation the slow way, no LangChain, no vector database, no hosted API. Just PyTorch, a 0.6B model, and a GPU, to see exactly what KV cache reuse does under the hood.
RAG lets an LLM answer questions about your private company data without retraining it. Here's how retrieval, embeddings, and generation actually fit together under the hood.
Tokens aren't words — they're subword units a tokenizer learns from raw text. Here's how that actually works.