黎智英欺詐案上訴得直：定罪及刑罰被撤銷，出獄時間提前
IntegrationsTo expand functionality and make your e-commerce store run more efficiently, Sellfy offers several integrations. Google Analytics and Webhooks, as well as integrations with Patreon and Facebook Live Chat, are just a few of the options available. Sellfy allows you to connect to Zapier, which gives you access to hundreds of third-party apps, including tools like Mailchimp, Trello, Salesforce, and more.
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"Cloning streams in Node.js's fetch() implementation is harder than it looks. When you clone a request or response body, you're calling tee() - which splits a single stream into two branches that both need to be consumed. If one consumer reads faster than the other, data buffers unbounded in memory waiting for the slow branch. If you don't properly consume both branches, the underlying connection leaks. The coordination required between two readers sharing one source makes it easy to accidentally break the original request or exhaust connection pools. It's a simple API call with complex underlying mechanics that are difficult to get right." - Matteo Collina, Ph.D. - Platformatic Co-Founder & CTO, Node.js Technical Steering Committee Chair
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It’s Not AI Psychosis If It Works#Before I wrote my blog post about how I use LLMs, I wrote a tongue-in-cheek blog post titled Can LLMs write better code if you keep asking them to “write better code”? which is exactly as the name suggests. It was an experiment to determine how LLMs interpret the ambiguous command “write better code”: in this case, it was to prioritize making the code more convoluted with more helpful features, but if instead given commands to optimize the code, it did make the code faster successfully albeit at the cost of significant readability. In software engineering, one of the greatest sins is premature optimization, where you sacrifice code readability and thus maintainability to chase performance gains that slow down development time and may not be worth it. Buuuuuuut with agentic coding, we implicitly accept that our interpretation of the code is fuzzy: could agents iteratively applying optimizations for the sole purpose of minimizing benchmark runtime — and therefore faster code in typical use cases if said benchmarks are representative — now actually be a good idea? People complain about how AI-generated code is slow, but if AI can now reliably generate fast code, that changes the debate.