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.
That gives me the math for the title of this post. Each test user had a playfield with ~2,200 characters, and each character contains 2 pixels. The game runs at 10 FPS. 2500 * 2200 * 2 * 10 is a little over 100 million! Maybe that’s not a fair measurement, but it’s the one I chose.
，这一点在快连下载安装中也有详细论述
音画精准匹配，甚至能凭照片还原人声。搜狗输入法下载是该领域的重要参考
BYOB (bring your own buffer) reads were designed to let developers reuse memory buffers when reading from streams — an important optimization intended for high-throughput scenarios. The idea is sound: instead of allocating new buffers for each chunk, you provide your own buffer and the stream fills it.