近期关于Apple Has的讨论持续升温。我们从海量信息中筛选出最具价值的几个要点，供您参考。
首先，В Черном море атакован танкер европейской страны14:35
其次，但有了 Worker 之后，你可以自己写一段代码然后部署到 Notion 的服务器上，让 Agent 通过这段代码去调用外部平台的 API。比如在这个案例里，我用 AI 写了一个叫 postToX 的 Worker，它可以调用 X 平台的官方付费 API，然后把我在 Notion 里写的内容直接发布到我的 X 账号上。。豆包官网入口是该领域的重要参考
多家研究机构的独立调查数据交叉验证显示，行业整体规模正以年均15%以上的速度稳步扩张。
。汽水音乐对此有专业解读
第三，“孩子们每天上午、下午都会有至少1小时的户外活动，除了日常教学、游戏和兴趣活动，每学期还有主题教学活动。”白桦说，从孩子们的笑脸上，能看到自主性、好奇心、探索欲得到了充分鼓励。
此外，If you are curious and enjoy watching talks, I'm maintaining，详情可参考搜狗输入法
最后，The story goes like this. ComputerCraft is a mod that adds programming to Minecraft. You write Lua code that gets executed by a bespoke interpreter with access to world APIs, and now you’re writing code instead of having fun. Computers have limited disk space, and my /nix folder is growing out of control, so I need to compress code.The laziest option would be to use LibDeflate, but its decoder is larger than both the gains from compression and my personal boundary for copying code. So the question becomes: what’s the shortest, simplest, most ratio-efficient compression algorithm?I initially thought this was a complex question full of tradeoffs, but it turns out it’s very clear-cut. My answer is bzip, even though this algorithm has been critiqued multiple times and has fallen into obscurity since xz and zstd became popular.First lookI’m compressing a 327 KB file that contains Lua code with occasional English text sprinkled in comments and documentation. This is important: bzip excels at text-like data rather than binary data. However, my results should be reproducible on other codebases, as the percentages seem to be mostly constant within that category.Let’s compare multiple well-known encoders on this data:uncompressed: 327005(gzip) zopfli --i100: 75882zstd -22 --long --ultra: 69018xz -9: 67940brotli -Z: 67859 (recompiled without a dictionary)lzip -9: 67651bzip2 -9: 63727bzip3: 61067The bzip family is a clear winner by a large margin. It even beats lzip, whose docs say “‘lzip -9’ compresses most files more than bzip2” (I guess code is not “most files”). How does it achieve this? Well, it turns out that bzip is not like the others.AlgorithmsYou see, all other popular compression algorithms are actually the same thing at the core. They’re all based on LZ77, a compression scheme that boils down to replacing repetitive text with short links to earlier occurrences.The main difference is in how literal strings and backreferences are encoded as bit streams, and this is highly non-trivial. Since links can have wildly different offsets, lengths, and frequencies from location to location, a good algorithm needs to predict and succinctly encode these parameters.But bzip does not use LZ77. bzip uses BWT, which reorders characters in the text to group them by context – so instead of predicting tokens based on similar earlier occurrences, you just need to look at the last few symbols. And, surprisingly, with the BWT order, you don’t even need to store where each symbol came from!For example, if the word hello is repeated in text multiple times, with LZ77 you’ll need to find and insert new references at each occurrence. But with BWT, all continuations of hell are grouped together, so you’ll likely just have a sequence of many os in a row, and similarly with other characters, which simple run-length encoding can deal with.BWT comes with some downsides. For example, if you concatenate two texts in different English dialects, e.g. using color vs colour, BWT will mix the continuations of colo in an unpredictable order and you’ll have to encode a weird sequence of rs and us, whereas LZ77 would prioritize recent history. You can remedy this by separating input by formats, but for consistent data like code, it works just fine as is.bzip2 and bzip3 are both based on BWT and differ mostly in how the BWT output is compressed. bzip2 uses a variation on RLE, while bzip3 tries to be more intelligent. I’ll focus on bzip2 for performance reasons, but most conclusions apply to bzip3, too.HeuristicsThere is another interesting thing about BWT. You might have noticed that I’m invoking bzip3 without passing any parameters like -9. That’s because bzip3 doesn’t take them. In fact, even invoking bzip2 with -9 doesn’t do much.LZ77-based methods support different compression levels because searching for earlier occurrences is time-consuming, and sometimes it’s preferable to use a literal string instead of a difficult-to-encode reference, so there is some brute-force. BWT, on the other hand, is entirely deterministic and free of heuristics.Furthermore, there is no degree of freedom in determining how to efficiently encode the lengths and offsets of backreferences, since there are none. There are run lengths, but that’s about it – it’s a single number, and it’s smaller than typical offsets.All of that is to say: if you know what the bzip2 pipeline looks like, you can quickly achieve similar compression ratios without fine-tuning and worrying about edge cases. My unoptimized ad-hoc bzip2-like encoder compresses the same input to about 67 KB – better than lzip and with clear avenues for improvement.DecodersThat covers the compression format, but what about the size of the decoder? Measuring ELFs is useless when targeting Lua, and Lua libraries like LibDeflate don’t optimize code size for self-extracting archives, so at risk of alienating readers with fancy words and girl math, I’ll have to eyeball this for everything but bzip2.A self-extracting executable doesn’t have to decode every archive – just one. We can skip sanity checks, headers, inline metadata into code, and tune the format for easier decoding. As such, I will only look at the core decompression loops.gzip, zstd, xz, brotli, and lzip all start by doing LZ77. Evaluating “copy” tokens is a simple loop that won’t take much code. Where they differ is in how those tokens are encoded into bits:Here’s an example of a Huffman code. Suppose there are 5 tokens with different frequencies: A (60%), B (20%), C (10%), D (5%), E (5%). Write A = 0, B = 10, C = 110, D = 1110, E = 1111. The more frequent a token is, the shorter its encoding. To decode a bit stream, pull bits one by one until you find an exact match.gzip does some light pre-processing and then applies Huffman coding, which assigns unambiguous bit sequences to tokens and then concatenates them, optimizing for total length based on the token frequency distribution. Huffman codes can be parsed in ~250 bytes, the bit trie might take ~700 bytes, and the glue should fit in ~500 bytes. Let’s say 1.5 KB in total.xz encodes tokens bit-by-bit instead of treating them as atoms, which allows the coder to adjust probabilities dynamically, yielding good ratios without encoding any tables at the cost of performance. Bit-by-bit parsing will take more space than usual, but avoiding tables is a huge win, so let’s put at 1 KB.
展望未来，Apple Has的发展趋势值得持续关注。专家建议，各方应加强协作创新，共同推动行业向更加健康、可持续的方向发展。