[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-80024":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":14,"subscribersCount":14,"size":14,"stars1d":14,"stars7d":13,"stars30d":15,"stars90d":14,"forks30d":14,"starsTrendScore":14,"compositeScore":15,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":16,"fork":16,"defaultBranch":17,"hasWiki":16,"hasPages":16,"topics":18,"createdAt":9,"pushedAt":9,"updatedAt":19,"readmeContent":20,"aiSummary":21,"trendingCount":14,"starSnapshotCount":14,"syncStatus":22,"lastSyncTime":23,"discoverSource":24},80024,"graphglyph","Tenobrus\u002Fgraphglyph","Tenobrus","Encode text as reversible unit-distance graph images",null,"Python",71,9,1,0,3,false,"main",[],"2026-06-12 02:03:57","# Graphglyph\n\nEncode text as reversible blue-and-orange unit-distance graph images.\n\nThe visual style is based on the finite illustration from OpenAI's unit-distance\npaper: points of the form\n\n```text\nz = a + b i + c rho + d i rho\nrho = exp(2 pi i \u002F 3)\n```\n\nwith edges drawn between projected points at Euclidean distance `1`. See\n[Planar Point Sets with Many Unit Distances](https:\u002F\u002Fcdn.openai.com\u002Fpdf\u002F74c24085-19b0-4534-9c90-465b8e29ad73\u002Funit-distance-proof.pdf).\n\n## How It Works\n\n- Text is normalized with Unicode NFKC and encoded as UTF-8.\n- Long payloads are zlib-compressed.\n- A packet header stores magic bytes, version, flags, seed, length, and CRC32.\n- Payload nibbles are distributed through seeded graph cells.\n- Each cell encodes four bits by making one edge in each candidate edge pair\n  stronger than the other.\n- The same text seed also varies the coefficient window, point count, basis\n  family, edge weights, and payload placement.\n\nThe SVG and JSON outputs are decodable. PNG outputs are presentation previews.\n\n## Usage\n\n```bash\npython3 graph_cipher.py encode \"you are loved immensely\" \\\n  -o examples\u002Fyou_are_loved_immensely.svg \\\n  --json examples\u002Fyou_are_loved_immensely.json\n\npython3 graph_cipher.py decode examples\u002Fyou_are_loved_immensely.svg\n```\n\nColors are presentation-only and do not affect decoding:\n\n```bash\npython3 graph_cipher.py encode \"goblins\" -o goblins_dark.svg \\\n  --edge-color \"#7c8cff\" \\\n  --node-color \"#ffd166\" \\\n  --node-stroke-color \"#d08700\" \\\n  --background-color \"#0b1020\"\n```\n\nThe default `--mode glyph --variant-strength 0.75` gives visible variation\nbetween texts. Encoding has three public generation modes; decoding is the same\nfor every mode because the recoverable data is stored in weighted graph edges.\n\nGeneration modes:\n\n| Mode | Point set | Typical use |\n| --- | --- | --- |\n| `glyph` | Seeded text-varying coefficient window. | Default visual glyphs with stronger phrase-to-phrase variation. |\n| `norm` | `a,b,c,d in {-N,...,N}` and `|a + bi + c rho + d i rho| \u003C R`. | Matches the first bounded-norm graph variant. |\n| `double-norm` | `|a + bi + c rho + d i rho| \u003C R` and `|a - bi + c rho - d i rho| \u003C R2`. | Matches the later two-embedding graph variant. |\n\n```bash\n# 1. Text-varying glyph mode.\npython3 graph_cipher.py encode \"text\" -o glyph.svg --mode glyph\n\n# 2. Single bounded complex norm:\n# a,b,c,d in {-N,...,N}, z = a + bi + c rho + d i rho, |z| \u003C R.\npython3 graph_cipher.py encode \"text\" -o norm.svg \\\n  --mode norm --unit-range 2 --norm-radius 4\n\n# 3. Two-embedding norm mode:\n# |a + bi + c rho + d i rho| \u003C R and |a - bi + c rho - d i rho| \u003C R2.\n# R2 defaults to R; set --dual-norm-radius for asymmetric bounds.\npython3 graph_cipher.py encode \"text\" -o double_norm.svg \\\n  --mode double-norm --unit-range 4 --norm-radius 4\n```\n\nLegacy `--window` names are still accepted as aliases, but `--mode` is the\nstable interface.\n\n## Examples\n\n### You Are Loved Immensely\n\n#### Glyph\n\n[SVG](examples\u002Fyou_are_loved_immensely.svg) |\n[PNG](examples\u002Fyou_are_loved_immensely.png)\n\n![you are loved immensely](examples\u002Fyou_are_loved_immensely.png)\n\n#### Norm\n\n[SVG](examples\u002Fyou_are_loved_immensely_norm.svg) |\n[PNG](examples\u002Fyou_are_loved_immensely_norm.png)\n\n![you are loved immensely norm](examples\u002Fyou_are_loved_immensely_norm.png)\n\n#### Double Norm\n\n[SVG](examples\u002Fyou_are_loved_immensely_double_norm.svg) |\n[PNG](examples\u002Fyou_are_loved_immensely_double_norm.png)\n\n![you are loved immensely double norm](examples\u002Fyou_are_loved_immensely_double_norm.png)\n\n### Goblins\n\n#### Glyph\n\n[SVG](examples\u002Fgoblins.svg) |\n[PNG](examples\u002Fgoblins.png)\n\n![goblins](examples\u002Fgoblins.png)\n\n#### Norm\n\n[SVG](examples\u002Fgoblins_norm.svg) |\n[PNG](examples\u002Fgoblins_norm.png)\n\n![goblins norm](examples\u002Fgoblins_norm.png)\n\n#### Double Norm\n\n[SVG](examples\u002Fgoblins_double_norm.svg) |\n[PNG](examples\u002Fgoblins_double_norm.png)\n\n![goblins double norm](examples\u002Fgoblins_double_norm.png)\n\n### Meditations On Moloch\n\nEncoded from Scott Alexander's\n[Meditations On Moloch](https:\u002F\u002Fslatestarcodex.com\u002F2014\u002F07\u002F30\u002Fmeditations-on-moloch\u002F).\n\n[PNG](examples\u002Fmeditations_on_moloch.png)\n\n![Meditations On Moloch](examples\u002Fmeditations_on_moloch.png)\n\nThe large `meditations_on_moloch.svg` is intentionally not included.\n\nAll code written by GPT 5.5.\n","Graphglyph 是一个将文本编码为可逆的单位距离图图像的工具。它使用蓝橙两色表示，基于OpenAI单位距离论文中的有限插图技术，通过在欧几里得距离为1的点之间绘制边来实现编码。项目支持长文本压缩、自定义颜色设置及多种生成模式（如glyph、norm和double-norm），每种模式适用于不同的视觉需求与应用场景。输出格式包括SVG、JSON和PNG，其中SVG和JSON可用于解码恢复原始文本，而PNG仅作为预览用途。该工具适合需要以图形方式隐藏或传输信息的场景，如艺术创作、数据可视化或加密通信等。",2,"2026-06-11 03:58:57","CREATED_QUERY"]