Replay started with initial input: {
Spark or trauma?
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第十四条 纳税人按照简易计税方法计算缴纳增值税的，因销售折让、中止或者退回而退还给购买方的销售额，应当从当期销售额中扣减。扣减当期销售额后仍有多缴税款的，可以从以后的应纳税额中扣减或者按规定申请退还。
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.
But after years of building on Web streams – implementing them in both Node.js and Cloudflare Workers, debugging production issues for customers and runtimes, and helping developers work through far too many common pitfalls – I've come to believe that the standard API has fundamental usability and performance issues that cannot be fixed easily with incremental improvements alone. The problems aren't bugs; they're consequences of design decisions that may have made sense a decade ago, but don't align with how JavaScript developers write code today.