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c0d3x.org · proprietary · v2.14.4

status privacy changelogBUILT WITH NEXT 16 · REACT 19 · TAILWIND 4// stay curious

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#1000 #100daysofsolana #100daysofsolona #2026 #a11y #accountability #achievement #activator #actors #adaptive

#awesome#collections

GHMay 4, 2026 · 1h ago

vinta/awesome-python — An opinionated list of Python frameworks, libraries, tools, and resources

An opinionated list of Python frameworks, libraries, tools, and resources

#awesome#collections#python

#agenticstandards

#aiassisteddevelopment

#aidiagnostictrust

#angular-roadmap

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#tseasınıfısızmatesti#devsecopsuygulamaları

DEVMay 4, 2026 · 2h ago

Git tabanlı altyapı yönetiminde güvenlik stratejileri ve uygulama esasları

Modern yazılım geliştirme ekosisteminde altyapının kod olarak yönetilmesi hız ve ölçeklenebilirlik açısından devrim yaratırken GitOps yaklaşımı bu süreci merkezi bir doğruluk kaynağına bağlamaktadır. Ancak tüm yapılandırma detaylarının tek bir platformda toplanması kritik siber güvenlik risklerini de beraberinde getirmektedir. Nesil Teknoloji olarak TSE A Sınıfı sızma testi yetkimizle endüstriyel

#tseasınıfısızmatesti#devsecopsuygulamaları#endüstriyelsistemgüvenliği#gittabanlıaltyapı

#aitools#codequality

DEVMay 4, 2026 · 14h ago

200 Lines in CLAUDE.md Dropped My Code Quality to 79% — Splitting into 3 Files Got It to 96.9%

More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively

#aitools#codequality#devtools#aidevos

#productivity#culture

DEVMay 3, 2026 · 1d ago

Why High-Context Culture Makes AI Coding Harder — A Japanese Developer's Perspective

I'm a software engineer in Japan. I've been using AI coding assistants — Claude Code, Cursor, Copilot — for about one years now. At some point I started keeping informal notes on how many prompt revisions it took to get production-quality output. After a few months, a pattern was hard to ignore. For tasks I described in Japanese: 4–6 revisions on average. 1–3. Same AI. Same model. Roughly similar

#productivity#culture#aitools#aidevos