anadim (@dimitrispapail)
发布仅两周的 MiniMax M2.5 模型以 4.55 万亿 Token 的调用量位列月度第一；月之暗面的 Kimi K2.5 以 4.02 万亿 Token 排名第二。谷歌 Gemini 3 Flash Preview、DeepSeek V3.2 与 Anthropic Claude Sonnet 4.5 分列其后。
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Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.
Issuer‑agnostic and composable These credentials can be anchored in multiple ways: Government-issued digital IDs, where available; third‑party identity verifiers similar to visa application centers; employers; or the Linux Foundation itself acting as an issuer.