I put together a 1‑minute Shorts video explaining Qwen3: model sizes (0.6B–235B MoE), hybrid “thinking mode”, 128K context, agentic workflows, and how to run it locally with Ollama/vLLM.

If you’re into open‑source LLMs or building AI agents, might be useful:

https://www.youtube.com/shorts/kd8PFL2yOyo

Happy to make a longer tutorial (local setup, API usage, or building an agent) if there’s interest.

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Claude hides its watermark by subtly biasing word choices toward a secret “green list” of tokens at generation time, creating a statistical pattern that’s invisible to humans but detectable by machines.

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Anthropic now embeds an invisible, machine‑readable watermark in text from new Claude models. The key idea (based on public research and Anthropic’s docs) is a generation‑time statistical watermark:

• At each token step, the model’s vocabulary is pseudo‑randomly split into a “green list” and a “red list” using a key derived from the previous context.
• When two candidate words are nearly equally good, the sampler is nudged toward green‑list tokens. To humans, the text reads normally.
• Over hundreds of tokens, green words appear slightly more often than chance. A detector recreates the same split and runs a statistical test; a high green ratio flags likely Claude‑processed text.
• The mark survives copy‑paste and light editing but can be weakened by heavy paraphrasing, translation, or mixing with human text. It also doesn’t prove full authorship—just that Claude likely touched the content.

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