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Pernah minta AI coding assistant bikin satu fitur sederhana, tapi yang keluar malah 400 baris kode yang merusak struktur Clean Architecture di project?
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Penyebabnya sederhana: LLM dirancang untuk langsung menebak token berikutnya tanpa punya rem kontrol. Mereka langsung ngoding sebelum benar-benar paham masalahnya.
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Beberapa waktu terakhir saya membangun Pólya SDLC Coder, sebuah kumpulan 15 modular skills dan kerangka kerja SDLC untuk mendisiplinkan AI coding agent (mendukung Cursor, Claude Code, GitHub Copilot, Google Antigravity, dan Codex).
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Framework ini menggabungkan metode pemecahan masalah matematika dari George Pólya (1945) dengan Clean Architecture (Uncle Bob).
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Apa saja yang berubah saat AI menggunakan workflow ini?
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Tidak ada lagi tebak-tebakan buta: Lewat /to-grill-me, AI menjadi sparring partner yang menguji desain kita dengan pilihan trade-off teknis yang nyata, bukan sekadar mengiyakan semua prompt.
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Ada rem darurat otomatis (🛑 The Pause Rule): AI dilarang keras menulis kode aplikasi sebelum rencana implementasi (tracer bullets) di-review dan disetujui oleh developer.
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Bebas "AI Amnesia" (/to-memory): Saat sesi chat di-reset atau context window penuh, keputusan arsitektur tetap tersimpan rapi antar-sesi.
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Disiplin Clean Code (/to-code & /to-review): Fungsi kecil di bawah 25 baris, pemisahan domain port dan adapter, larangan memakai @ts-ignore atau melewati test, serta audit keamanan & SOLID sebelum rilis.
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Cara pasangnya di project juga sangat praktis, cukup jalankan: npx skills add GulajavaMinistudio/polya-sdlc-coder
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Cerita lengkap di balik pembuatan proyek ini baru saja saya tulis di Dev.to:
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🔗 Artikel Dev.to: https://dev.to/gulajavaministudio/why-an-80-year-old-math-book-fixed-my-ai-coding-agent-517b
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🔗 Repository GitHub: https://github.com/GulajavaMinistudio/polya-sdlc-coder
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🔗 Dokumentasi Interaktif: https://gulajavaministudio.github.io/polya-sdlc-coder/
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Silakan dicoba di codebase masing-masing, masukan dan kontribusi sangat terbuka!
#SoftwareArchitecture #CleanCode #AIAgents #Programming #opensource
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