Part of the value of push-based systems is that each node only needs to keep track of its own dependencies and dependents, which makes analysing each node locally easy, but analysing the system as a whole hard. In the extreme case, you might dynamically create and destroy nodes in the tree depending on previous values — this doesn’t make sense for our spreadsheet analogy, but is essentially what’s happening with RxJS’s switchMap operator. Essentially, the more dynamism we want in our system, the harder it is to achieve efficient updates, and the more we want efficient updates, the more we need to specify our dependency graphs up-front.
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。业内人士推荐新收录的资料作为进阶阅读
Look at how we can use artifacts like logs, traces etc. to build up a body of evidence for a specific failure root cause. Data wrangle it into submission so an LLM can use it.
36氪从多位与奔驰有业务往来的知情人士处获悉，奔驰已经启动了一个代号为“凤凰”的新平台，支撑全球的入门级电动车型，正是这个全新平台，奔驰初步计划将融合吉利GEEA架构。。新收录的资料是该领域的重要参考
Don't feel down if you didn't manage to guess it this time. There will be new sports Connections for you to stretch your brain with tomorrow, and we'll be back again to guide you with more helpful hints.
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.，更多细节参见新收录的资料