From 7f6baa0c8d5d51a20bfec7867a88f381bf99ab6a Mon Sep 17 00:00:00 2001
From: mayx <mayx@outlook.com>
Date: Wed, 7 Oct 2026 06:16:54 +0000
Subject: Update 2 files

- /_tools/serv00_post-receive
- /_posts/2026-10-01-quine.md
---
 _posts/2026-10-01-quine.md | 562 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 562 insertions(+)

(limited to '_posts')

diff --git a/_posts/2026-10-01-quine.md b/_posts/2026-10-01-quine.md
index 237eed8..bef3437 100644
--- a/_posts/2026-10-01-quine.md
+++ b/_posts/2026-10-01-quine.md
@@ -190,6 +190,568 @@ class BlogQuine:
 
   于是我第一时间就把原来的TGZ压缩命令换掉，换成了AI给我写的[blogquine.py](https://github.com/Mabbs/mabbs.github.io/blob/master/_tools/blogquine.py)，现在就可以通过[这里](https://mayx.eu.org/MayxBlog.7z)下载到“完整”包含我博客所有内容的压缩包了。   
   不过唯一的问题就是这样做出来的压缩包并没有压缩😂，相当于给做成了普通的归档了。当然我的博客本身倒是不大，没压缩也多不了多少空间，但相比于能做出“完整”的效果来说，这点浪费的空间也是小问题了。   
+## 基于博客压缩包的离线模式（2026.10.03更新）
+  既然现在的AI什么都能做了，那上次我因为技术不足仅仅只是用[Service Worker做了个代理](/2025/08/01/sw-proxy.html)，现在也可以升级成下载全站之后让全站离线的功能吧？不过之前做的压缩包并没有压缩，导致整个压缩包有接近20MiB的大小，后来我仔细想了一下我的需求，我只是想让下载全站压缩包的按钮不断链，并没有说解压后的文件一定就必须是它本身，所以我只要用相同的文件名再压缩一次应该就可以了，这样既能满足我的想法，又可以让其他人下载的时候不会花费太多的时间。   
+  在我直接压缩之后，整个压缩包占用大概9MiB的空间，其实我已经很满意了，不过压之前是18MiB，也就是说压缩率是50%……虽然也不是不能接受，但我在想这是不是合理的结果呢？我让AI分析了一下50%的压缩率正不正常，它看了一眼之后告诉我文本部分的10MiB压缩完之后有1MiB，图片因为压不动所以直接占用了8MiB😅，这时候我才意识到原来我一直存着一堆占着大量空间的垃圾，而且还是用的PNG/JPG这种压缩率很低的格式……考虑到兼容性和压缩效果，我把占用比较大的图片转换为了WebP格式，最终整个压缩包就只占用了4MiB的空间，一口气又节省了50%的空间。   
+  最后做完对压缩包的处理之后，我直接让AI给我基于那个压缩包制作离线模式，它轻轻松松就[做出来](https://mayx.eu.org/offline.html)了，最终使用的是[libarchive库](https://github.com/nika-begiashvili/libarchivejs)，在启用离线模式之后整个网站的速度简直就是0延迟，点击就能瞬间跳转，效果算是相当不错了。   
+## 受大佬启发制作了能压缩的7z Quine（2026.10.05更新）
+  做完博客压缩包之后我突发奇想，虽然东西是AI做的，但也是在我的手中做出了世界上第一个LZMA2 Quine，那么如果我向全世界第一个制作ZIP Quine的人展示一下我的作品会怎么样呢？目前可以知道，世界上第一个制作ZIP Quine的人是[Erling Ellingsen](https://infosec.exchange/@steike)，所以我向他发了个帖子[展示了一下](https://infosec.exchange/@mayx/117385801954308033)。大佬不愧是大佬，一眼就能看出理论上能压缩的7z Quine是可以做出来的，对我来说，我是完全不知道7-Zip居然还支持链式编解码，能可以通过“double lzma2”的方式做到既可以自包含、又能真正被压缩的压缩包。既然大佬说理论上可以，那我自然是相信的，于是就让AI给我使劲折腾想办法把它做出来。在折腾了一晚上之后AI终于做出来了，真是强得可怕：   
+```python
+# ============================================================
+# 双层 LZMA2 的 7z quine（"double lzma2"）
+#
+# F = S32 ‖ P ‖ H，folder 的 coder 链 = [LZMA2(外层), LZMA2(内层)]，
+# 解码管线：P --外层--> I --内层--> U。
+#
+#   I = Cseg ‖ LBseg ‖ Mseg ‖ 0x00
+#     Cseg : 真实 LZMA2 对 content 的压缩流（内容真正的压缩数据 C）
+#     LBseg: 内层 store 链，载荷 = seed（seed 内含一份 C 的参照副本）
+#     Mseg : 内层 match 链，输出 quine 区（= F 的逐字节副本）
+#   U = content ‖ seed ‖ F
+#
+# 外层把 I 再压一遍：Cseg 用 store 链原样承载；seed 里的 C 参照副本与 Cseg
+# 逐字节相同，被外层的 rep0 回溯匹配消掉，于是成品里只剩一份压缩数据。
+# quine 区回引 P 的字节时，源全在 seed 里（C 副本 / 头样本表 / 机械串）。
+# ============================================================
+def lzma_chunk(tokens, out_pos):
+    """编一个 LZMA chunk（0xC0: state+props reset，无 dict reset，无 end marker）。
+    match token 的编码字节只取决于 (dist,len,pos)，与历史内容无关（不维护输出历史）。
+    解压长度用 21 位（控制字节低 5 位存高 5 位），故单 chunk 输出上限 2 MiB。"""
+    enc = LzmaEncoder()
+    enc.pos = out_pos
+    total = 0
+    for t in tokens:
+        if t[0] == "m":
+            enc.match(t[1], t[2])
+            total += t[2]
+        else:
+            enc.rep_match(t[1])
+            total += t[1]
+    data = enc.finish()
+    u = total - 1
+    c = len(data) - 1
+    assert 0 <= u < (1 << 21) and 0 <= c < 65536, (total, len(data))
+    hdr = bytes([0xC0 | ((u >> 16) & 0x1F)]) + (u & 0xFFFF).to_bytes(2, "big") + \
+          (c & 0xFFFF).to_bytes(2, "big") + bytes([PROPS_BYTE])
+    return hdr + data, total
+
+LZ2_MAXU = 1 << 21                       # LZMA2 单个 LZMA chunk 的解压上限
+
+
+def lzma2_raw(data, dict_size, preset=6):
+    """FORMAT_RAW + LZMA2 压缩；去掉结尾 0x00 便于后面继续追加 chunk。"""
+    c = lzma.LZMACompressor(
+        format=lzma.FORMAT_RAW,
+        filters=[{"id": lzma.FILTER_LZMA2, "dict_size": dict_size, "preset": preset}])
+    out = c.compress(data) + c.flush()
+    assert out and out[-1] == 0x00, "LZMA2 流应以 0x00 结尾"
+    return out[:-1]
+
+
+def split_len(n, cap):
+    """按 cap 上限切分 n 字节。末片若为 1 字节则从前一片借 1（match 最短为 2）。"""
+    Ls, rem = [], n
+    while rem > cap:
+        Ls.append(cap)
+        rem -= cap
+    if rem == 1 and Ls:
+        Ls[-1] -= 1
+        rem = 2
+    Ls.append(rem)
+    return Ls
+
+
+class DoubleQuine(BlogQuine):
+    """双层 LZMA2 版本：folder = [LZMA2(外层), LZMA2(内层)]。"""
+
+    def entries(self):
+        """U 的顺序是 内容文件… ‖ seed ‖ quine，条目顺序必须与之对应。"""
+        ent = [{"name": r, "dir": isd} for r, isd in self.walk_entries]
+        ent.append({"name": self.seed_name, "dir": False})
+        ent.append({"name": self.quine_name, "dir": False})
+        return ent
+
+    # ---------- 头部：双 coder + BindPair ----------
+    def build_header(self, n, total, d, entries, sub_sizes, size_I):
+        n_sub = len(sub_sizes)
+        N = len(entries)
+        h = bytearray()
+        h += b"\x01\x04"                                   # kHeader, kMainStreamsInfo
+        h += b"\x06" + varint(0) + varint(1) + b"\x09" + varint(n) + b"\x00"
+        h += b"\x07"                                       # kUnpackInfo
+        h += b"\x0b" + varint(1) + b"\x00"                 # kFolder: 1, local
+        h += varint(2)                                     # NumCoders = 2
+        h += b"\x21\x21" + varint(1) + bytes([self.dict_prop_out])   # coder0 = 外层
+        h += b"\x21\x21" + varint(1) + bytes([self.dict_prop_in])    # coder1 = 内层
+        h += varint(1) + varint(0)             # BindPair = (InIndex=1, OutIndex=0)
+        h += b"\x0c" + varint(size_I) + varint(d + total)  # 两个 coder 的输出大小
+        h += b"\x00"
+        h += b"\x08"                                       # SubStreamsInfo
+        h += b"\x0d" + varint(n_sub)
+        h += b"\x09" + b"".join(varint(s) for s in sub_sizes[:-1])
+        h += b"\x0a" + b"\x01"
+        crc_base = len(h)
+        h += b"\x00" * (4 * n_sub)
+        h += b"\x00\x00"
+        h += b"\x05" + varint(N)                           # FilesInfo
+        bits = bytearray((N + 7) // 8)
+        for i, e in enumerate(entries):
+            if e["dir"]:
+                bits[i // 8] |= 1 << (7 - i % 8)
+        h += b"\x0e" + varint(len(bits)) + bytes(bits)
+        mt = b"\x01\x00" + struct.pack("<Q", self.mtime_ft) * N
+        h += b"\x14" + varint(len(mt)) + mt
+        names = bytearray(b"\x00")
+        for e in entries:
+            names += e["name"].encode("utf-16-le") + b"\x00\x00"
+        h += b"\x11" + varint(len(names)) + bytes(names)
+        attrs = b"".join(struct.pack("<I", 0x10 if e["dir"] else 0x20)
+                         for e in entries)
+        h += b"\x15" + varint(2 + len(attrs)) + b"\x01\x00" + attrs
+        h += b"\x00\x00"
+        return bytes(h), crc_base
+
+    # ---------- 布局：三层坐标的不动点迭代 ----------
+    def layout(self):
+        content = self.content
+        dc = len(content)
+        dict_c = max(1 << 20, 1 << max(20, dc.bit_length()))
+        C = lzma2_raw(content, dict_c)
+        self._C = C                         # 内容压缩只做一次，finalize 复用
+        lc = len(C)
+        # 迭代变量：Y = plant 长度, H = 头部长度, M = Mseg 字节数
+        Y, H, M = 96, 256, 256
+        seen = set()
+        for _ in range(200):
+            ds = lc + 32 + H + Y            # seed = C副本‖S32‖H‖plant
+            d = dc + ds
+            Ls_LB = split_len(ds, CHUNK)
+            lLB = 3 * len(Ls_LB) + ds
+            off_in = lc + lLB
+            M1 = M + 1                      # Mseg ‖ 内层流终止符 0x00
+            Ls_M = split_len(M1, CHUNK)
+            Li = off_in + M1
+            self.dict_prop_out, _ = dict_prop_for(max(Li, 1 << 20))
+            self.dict_prop_in, _ = dict_prop_for(d + 32 + H + 8192)
+            st = self._build_streams(C, lc, ds, d, Ls_LB, lLB, off_in, Ls_M,
+                                     M1, H)
+            if st is None:
+                Y += 8
+                continue
+            mseg = self._build_mseg(st, d, lc, ds, H, M)
+            if mseg is None:
+                Y += 8
+                continue
+            M_new = len(mseg)
+            Y_new = len(st["hdr_bytes"]) + len(st["mech"]) + M_new + 2
+            total = st["n"] + 32 + H
+            file_sizes = [len(x) for _, x in self.files]
+            sub_sizes = file_sizes + [ds] + [total]
+            entries = self.entries()
+            hdr, crc_base = self.build_header(st["n"], total, d, entries,
+                                              sub_sizes, Li)
+            H_new = len(hdr)
+            key = (Y_new, H_new, M_new)
+            if key in seen:                 # 再次见到同一组值 = 已收敛
+                Y, H, M = Y_new, H_new, M_new
+                break
+            seen.add(key)
+            Y, H, M = Y_new, H_new, M_new
+        else:
+            raise RuntimeError("double layout 不收敛")
+        return {"Y": Y, "H": H, "M": M}
+
+    # ---------- 定稿：用收敛后的 (Y, H, M) 重算全部结构 ----------
+    def finalize(self, Y, H, M):
+        content = self.content
+        dc = len(content)
+        C = self._C                         # layout 里已压缩过
+        lc = len(C)
+        ds = lc + 32 + H + Y
+        d = dc + ds
+        Ls_LB = split_len(ds, CHUNK)
+        lLB = 3 * len(Ls_LB) + ds
+        off_in = lc + lLB
+        M1 = M + 1
+        Ls_M = split_len(M1, CHUNK)
+        Li = off_in + M1
+        self.dict_prop_out, ds_out = dict_prop_for(max(Li, 1 << 20))
+        self.dict_prop_in, ds_in = dict_prop_for(d + 32 + H + 8192)
+        st = self._build_streams(C, lc, ds, d, Ls_LB, lLB, off_in, Ls_M, M1, H)
+        mseg = self._build_mseg(st, d, lc, ds, H, M)
+        assert len(mseg) == M, "Mseg %d != %d" % (len(mseg), M)
+        total = st["n"] + 32 + H
+        file_sizes = [len(x) for _, x in self.files]
+        sub_sizes = file_sizes + [ds] + [total]
+        entries = self.entries()
+        hdr, crc_base = self.build_header(st["n"], total, d, entries,
+                                          sub_sizes, Li)
+        assert len(hdr) == H, "header %d != %d" % (len(hdr), H)
+        plant = st["hdr_bytes"] + bytes(st["mech"]) + mseg + b"\x00\x00"
+        assert len(plant) == Y, "plant %d != %d" % (len(plant), Y)
+        return {"C": C, "lc": lc, "dc": dc, "ds": ds, "d": d, "Li": Li,
+                "ochunks": st["ochunks"], "hdr_tab": st["hdr_tab"],
+                "mseg": mseg, "M": M, "H": H, "n": st["n"], "total": total,
+                "header": hdr, "crc_base": crc_base, "plant": plant,
+                "dict_size_in": ds_in, "dict_size_out": ds_out,
+                "n_sub": len(sub_sizes)}
+
+    # ---------- 外层流 P 的构造 ----------
+    def _build_streams(self, C, lc, ds, d, Ls_LB, lLB, off_in, Ls_M, M1, H):
+        hdr_tab, hdr_idx = [], {}
+
+        def hidx(b):
+            assert len(b) == 3 and b[0] in (0x01, 0x02), b.hex()
+            if b not in hdr_idx:
+                hdr_idx[b] = len(hdr_tab)
+                hdr_tab.append(b)
+            return hdr_idx[b]
+
+        ochunks = []
+        f, io = 32, 0
+        mech = bytearray()
+
+        def put_lzma(tokens, ioff):
+            blk, t = lzma_chunk(tokens, ioff)
+            ochunks.append({"kind": "lzma", "foff": f, "size": len(blk),
+                            "ioff": ioff, "olen": t, "bytes": blk,
+                            "mech": len(mech)})
+            mech.extend(blk)
+            return len(blk), t
+
+        def put_store(ioff, olen, hi, src):
+            ochunks.append({"kind": "store", "foff": f, "size": 3 + olen,
+                            "ioff": ioff, "olen": olen, "hdr": hi, "src": src})
+            return 3 + olen
+
+        # 段 1：store 链承载 Cseg（I[0:lc] = C 的字节；源 = seed 的 C 参照副本）
+        Ls_C = split_len(lc, CHUNK)
+        c_off = 0
+        for L in Ls_C:
+            f += put_store(io, L, hidx(store_hdr(L, first=(c_off == 0))),
+                           ("seed", c_off))
+            io += L
+            c_off += L
+        assert io == lc
+
+        # 段 2：LBseg（I[lc : lc+lLB]）—— 内层 store 链，载荷 = seed
+        lb_off = lc
+        seed_off = 0
+        for j, L in enumerate(Ls_LB):
+            lb_hdr = store_hdr(L, first=False)
+            if j == 0 or L != CHUNK:
+                # 没有可回引的相同样本，用外层 store chunk 原样携带这 3 字节
+                f += put_store(lb_off, 3, hidx(store_hdr(3, first=False)),
+                               ("tab", hidx(lb_hdr)))
+                io += 3
+            else:
+                # 与 chunk0 的头逐字节相同，直接从 I[lc:lc+3] 复制
+                n, t = put_lzma([("m", lb_off - lc, 3)], lb_off)
+                assert t == 3
+                f += n
+                io += 3
+            # 载荷：C 参照副本区走 rep0 回溯匹配（去重发生处），其余走 store
+            pay = lb_off + 3
+            i = 0
+            while i < L:
+                if seed_off + i < lc:
+                    a = min(L - i, lc - (seed_off + i), LZ2_MAXU)
+                    dist = (pay + i) - (seed_off + i)
+                    n, t = put_lzma(matches_for(dist, a), pay + i)
+                    assert t == a
+                    f += n
+                    io += a
+                    i += a
+                else:
+                    a = min(L - i, LZ2_MAXU)
+                    f += put_store(pay + i, a, hidx(store_hdr(a, first=False)),
+                                   ("seed", seed_off + i))
+                    io += a
+                    i += a
+            lb_off += 3 + L
+            seed_off += L
+        assert lb_off == lc + lLB and io == lc + lLB
+
+        # 段 3：store 链承载 Mseg ‖ 0x00（0x00 是内层流的终止符）
+        m_off = 0
+        for L in Ls_M:
+            f += put_store(off_in + m_off, L,
+                           hidx(store_hdr(L, first=False)), ("mseg", m_off))
+            io += L
+            m_off += L
+        # 外层流自己的终止符 0x00
+        ochunks.append({"kind": "term", "foff": f, "size": 1, "ioff": io,
+                        "olen": 0, "bytes": b"\x00"})
+        f += 1
+        return {"ochunks": ochunks, "mech": mech, "hdr_tab": hdr_tab,
+                "hdr_bytes": b"".join(hdr_tab), "n": f - 32}
+
+    # ---------- 内层 Mseg：把 quine 区的每个 F 区段映射成 match 链 ----------
+    def _build_mseg(self, st, d, lc, ds, H, M):
+        dc = d - ds
+        s_S32 = dc + lc                                  # seed 里 S32 副本
+        s_H = dc + lc + 32                               # seed 里 H 副本
+        s_tab = dc + lc + 32 + H                         # seed 里头样本表
+        s_mech = s_tab + len(st["hdr_bytes"])            # seed 里机械串
+        s_mseg = s_mech + len(st["mech"])                # seed 里 Mseg 副本
+        total = st["n"] + 32 + H
+        hoff = 32 + st["n"]
+        segs = [(0, 32, s_S32)]                          # F[0:32) 签名头
+        for c in st["ochunks"]:
+            if c["kind"] == "store":
+                segs.append((c["foff"], 3, s_tab + 3 * c["hdr"]))
+                src = c["src"]
+                if src[0] == "seed":
+                    u = dc + src[1]
+                elif src[0] == "tab":
+                    u = s_tab + 3 * src[1]
+                else:                                    # ("mseg", off)
+                    u = s_mseg + src[1]
+                segs.append((c["foff"] + 3, c["olen"], u))
+            elif c["kind"] == "lzma":
+                segs.append((c["foff"], c["size"], s_mech + c["mech"]))
+            else:                                        # term：并入前一个载荷段
+                pf, pl, pu = segs[-1]
+                segs[-1] = (pf, pl + 1, pu)
+        segs.append((hoff, H, s_H))                      # F[hoff:] 头部
+        pos = 0
+        for (fo, ln, u) in segs:
+            assert fo == pos, "段不连续: %d != %d" % (fo, pos)
+            assert ln >= 2, "段太短 %d @%d" % (ln, fo)
+            pos += ln
+        assert pos == total, "段总长 %d != total %d" % (pos, total)
+        # 生成 match 链，按 2 MiB 上限切成多个 0xC0 chunk
+        mseg = bytearray()
+        out_pos = d
+        cur, cur_len = [], 0
+        chunks = []
+        for (fo, ln, u) in segs:
+            dist = (d + fo) - u
+            assert 0 < dist, "dist=%d @%d" % (dist, fo)
+            toks = matches_for(dist, ln)
+            if cur_len + ln > LZ2_MAXU - MAX_MATCH:
+                chunks.append((cur, out_pos, cur_len))
+                out_pos += cur_len
+                cur, cur_len = [], 0
+            cur.extend(toks)
+            cur_len += ln
+        if cur:
+            chunks.append((cur, out_pos, cur_len))
+            out_pos += cur_len
+        assert out_pos == d + total, "Mseg 输出 %d != %d" % (out_pos, d + total)
+        for (toks, op, cl) in chunks:
+            blk, t = lzma_chunk(toks, op)
+            assert t == cl
+            mseg.extend(blk)
+        return bytes(mseg)
+
+    # ---------- F 中承载 seed[soff] 的那个字节的偏移 ----------
+    def _f_off_of_seed(self, lay, soff):
+        """返回 (F 偏移, 该 store 载荷内从 soff 起还剩多少字节)。
+        seed_head 超过 64 KiB 时会被切成多片，所以调用方要循环推进。"""
+        for c in lay["ochunks"]:
+            if c["kind"] == "store" and c["src"][0] == "seed":
+                s0 = c["src"][1]
+                if s0 <= soff < s0 + c["olen"]:
+                    return c["foff"] + 3 + (soff - s0), s0 + c["olen"] - soff
+        raise RuntimeError("seed 偏移 %d 未被 store 载荷覆盖" % soff)
+
+    def _f_pos_list(self, lay, soff, length):
+        """seed[soff:soff+length] 在 F 中对应的字节位置列表（可能跨多片）。"""
+        res = []
+        while length > 0:
+            p, avail = self._f_off_of_seed(lay, soff)
+            a = min(length, avail)
+            res.extend(range(p, p + a))
+            soff += a
+            length -= a
+        return res
+
+    # ---------- 装配 ----------
+    def assemble(self, lay):
+        total, H = lay["total"], lay["H"]
+        hoff = total - H
+        C, plant = lay["C"], lay["plant"]
+        F = bytearray(total)
+        # pass A: 签名头 + 所有非 store 载荷字节
+        F[0:6] = SIG
+        F[6:8] = VER
+        struct.pack_into("<Q", F, 12, lay["n"])
+        struct.pack_into("<Q", F, 20, H)
+        for c in lay["ochunks"]:
+            if c["kind"] == "store":
+                F[c["foff"]:c["foff"] + 3] = lay["hdr_tab"][c["hdr"]]
+            else:
+                F[c["foff"]:c["foff"] + c["size"]] = c["bytes"]
+        F[hoff:hoff + H] = lay["header"]
+        # pass B: seed = C副本 ‖ S32副本 ‖ H副本 ‖ plant
+        seed = bytearray()
+        seed += C
+        seed += bytes(F[0:32])
+        seed += bytes(F[hoff:hoff + H])
+        seed += plant
+        assert len(seed) == lay["ds"], "seed %d != %d" % (len(seed), lay["ds"])
+        # pass C: 各 store 的载荷
+        msegz = lay["mseg"] + b"\x00"
+        for c in lay["ochunks"]:
+            if c["kind"] != "store":
+                continue
+            p = c["foff"] + 3
+            src, L = c["src"], c["olen"]
+            if src[0] == "seed":
+                F[p:p + L] = seed[src[1]:src[1] + L]
+            elif src[0] == "tab":
+                F[p:p + 3] = lay["hdr_tab"][src[1]]
+            else:
+                F[p:p + L] = msegz[src[1]:src[1] + L]
+        return F, bytes(seed)
+
+    # ---------- CRC 定点（128x128 GF(2)：seed/quine/Next/Start 四个 CRC） ----------
+    def solve_crc(self, F, lay, C, plant):
+        H, hoff = lay["H"], lay["total"] - lay["H"]
+        n_sub, crc_base = lay["n_sub"], lay["crc_base"]
+        lc = lay["lc"]
+        # seed 里 S32 / H 副本在 F 中的镜像（可能跨多片，逐字节列出）
+        scopy0 = self._f_pos_list(lay, lc, 32)
+        Fv = memoryview(F)
+
+        def seed_crc():
+            v = crc32(C)
+            v = crc32(Fv[0:32], v)                    # S32 副本
+            v = crc32(Fv[hoff:hoff + H], v)           # H 副本
+            return crc32(plant, v)
+
+        def targets():
+            return (seed_crc(), crc32(Fv), crc32(Fv[hoff:hoff + H]),
+                    crc32(Fv[12:32]))
+
+        # 已知：各内容文件的 CRC，写本体 + seed 里 H 副本的镜像两处
+        known = [crc32(data) for _, data in self.files]
+        for i, e in enumerate(known):
+            slot = crc_base + 4 * i
+            b = struct.pack("<I", e)
+            for k in range(4):
+                F[hoff + slot + k] = b[k]
+            mir = self._f_pos_list(lay, lc + 32 + slot, 4)
+            assert len(mir) == 4
+            for k in range(4):
+                F[mir[k]] = b[k]
+        # 未知量：D_seed(seed 的 CRC)、D(quine 的 CRC)、N、S
+        # 每个未知量 4 字节，每字节有"本体 + seed 镜像"两个落点
+        qs = crc_base + 4 * len(known)       # seed 子流的 CRC 槽
+        q = crc_base + 4 * (n_sub - 1)       # quine 子流的 CRC 槽
+        groups = []
+        for slot in (qs, q):
+            groups.append([list(range(hoff + slot, hoff + slot + 4)),
+                           self._f_pos_list(lay, lc + 32 + slot, 4)])
+        groups.append([list(range(28, 32)), scopy0[28:32]])   # NextHeaderCRC
+        groups.append([list(range(8, 12)), scopy0[8:12]])     # StartHeaderCRC
+        nb = 32 * len(groups)
+        base = targets()
+        basevec = 0
+        for i, v in enumerate(base):
+            basevec |= v << (32 * i)
+        cols = []
+        for g in range(len(groups)):
+            own, mir = groups[g]
+            for kb in range(32):
+                bi, bb = kb // 8, kb % 8
+                poss = [own[bi], mir[bi]]
+                for p in poss:
+                    F[p] ^= (1 << bb)
+                t = targets()
+                for p in poss:
+                    F[p] ^= (1 << bb)
+                delta = 0
+                for i, v in enumerate(t):
+                    delta |= (v ^ base[i]) << (32 * i)
+                cols.append(delta)
+        eqs = []
+        for i in range(nb):
+            coeff = 0
+            for j in range(nb):
+                if (cols[j] >> i) & 1:
+                    coeff |= 1 << j
+            coeff ^= 1 << i
+            eqs.append([coeff, (basevec >> i) & 1])
+        for j in range(nb):
+            p = next((i for i in range(j, nb) if (eqs[i][0] >> j) & 1), None)
+            if p is None:
+                raise RuntimeError("GF(2) 奇异 @bit%d" % j)
+            eqs[j], eqs[p] = eqs[p], eqs[j]
+            for i in range(nb):
+                if i != j and ((eqs[i][0] >> j) & 1):
+                    eqs[i][0] ^= eqs[j][0]
+                    eqs[i][1] ^= eqs[j][1]
+        x = 0
+        for i in range(nb):
+            assert eqs[i][0] == (1 << i)
+            if eqs[i][1]:
+                x |= 1 << i
+        vals = [(x >> (32 * i)) & 0xFFFFFFFF for i in range(len(groups))]
+        for g in range(len(groups)):
+            own, mir = groups[g]
+            b = struct.pack("<I", vals[g])
+            for k in range(4):
+                F[own[k]] = b[k]
+                F[mir[k]] = b[k]
+        t = targets()
+        assert t == tuple(vals), "CRC 定点失败: %s != %s" % (t, vals)
+        return vals
+
+    def build(self):
+        l0 = self.layout()
+        lay = self.finalize(l0["Y"], l0["H"], l0["M"])
+        F, seed = self.assemble(lay)
+        crcs = self.solve_crc(F, lay, lay["C"], lay["plant"])
+        # CRC 定点会改写 H / S32，而 seed 是它们的镜像，定点后需重建
+        hoff = lay["total"] - lay["H"]
+        seed = lay["C"] + bytes(F[0:32]) + bytes(F[hoff:hoff + lay["H"]]) \
+            + lay["plant"]
+        return bytes(F), lay, seed, crcs
+
+
+def verify_double(F, lay, files, seed, content):
+    """串联两个原始 LZMA2 解码器还原 U，逐段校验。"""
+    d, total = lay["d"], lay["total"]
+    dec_o = lzma.LZMADecompressor(
+        format=lzma.FORMAT_RAW,
+        filters=[{"id": lzma.FILTER_LZMA2, "dict_size": lay["dict_size_out"]}])
+    I = dec_o.decompress(F[32:32 + lay["n"]])
+    ok = True
+    if len(I) != lay["Li"]:
+        print("[verify] 外层输出 %d != I %d" % (len(I), lay["Li"]))
+        return False
+    dec_i = lzma.LZMADecompressor(
+        format=lzma.FORMAT_RAW,
+        filters=[{"id": lzma.FILTER_LZMA2, "dict_size": lay["dict_size_in"]}])
+    U = dec_i.decompress(I)
+    exp = d + total
+    if len(U) != exp:
+        print("[verify] 内层输出 %d != %d" % (len(U), exp))
+        return False
+    dc, ds = lay["dc"], lay["ds"]
+    if U[:dc] != content:
+        print("[verify] 内容不匹配")
+        ok = False
+    pos = 0
+    for rel, data in files:                   # 内容文件排在 U 的最前面
+        if U[pos:pos + len(data)] != data:
+            print("[verify] 文件不匹配:", rel)
+            ok = False
+        pos += len(data)
+    assert pos == dc
+    if U[dc:dc + ds] != seed:
+        print("[verify] seed 不匹配")
+        ok = False
+    if U[d:] != F:
+        print("[verify] quine 自复制不匹配")
+        ok = False
+    print("[verify] 自解码: |I|=%d, |U|=%d, seed/文件/quine 全部匹配: %s"
+          % (len(I), len(U), ok))
+    return ok
+```
+  仅仅是大佬的一句话，AI就能做出来，实在是太可怕了。最终我试了一下，官方的7-Zip可以正常解压，但是Windows资源管理器不行……Windows资源管理器使用的也是[libarchive](https://github.com/libarchive/libarchive)，似乎是因为libarchive只支持2个解码器，并且[第二个解码器只能是像BCJ这样的过滤器](https://github.com/libarchive/libarchive/blob/8bb3bbdc7b117a1e22086a2260f2087aafa90687/libarchive/archive_read_support_format_7zip.c#L1475)……明明7-Zip官方是可以解压的，不知道如果把这个问题当作BUG报告给libarchive他们会不会解决？但目前存在兼容性问题的话我博客压缩包就先不用这个方案了吧🥲……   
 ## 对TXZ格式的尝试
   在做完7z格式的压缩包之后，我发现了一个问题，虽然7z确实很流行，但是在Linux下解压起来有点麻烦，7-Zip历史上主要面向Windows，Linux上长期以来更多依赖p7zip等第三方移植，因此生态集成度不如tar.xz，想要解压7z文件还得额外安装。   
   不过Linux下也有个支持LZMA2算法的压缩软件，那就是前些年出过[后门](https://tukaani.org/xz-backdoor/)的XZ Utils，配合tar就可以做出TXZ（tar.xz）文件，甚至用我[博客终端](https://mabbs.github.io/linux/)中的BusyBox也能解压。我想了一下反正有AI，干脆一句话让AI帮我把blogquine.py改成tar.xz格式的好了，结果倒也没费多少功夫，AI就这样写出来了：   
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