The PenguinKit blog
Practical guides to planning software, scripts and automations—and giving your AI tools the context to build well.
Featured · AI coding workflow
Autonomous coding agents are great at getting test suites to turn green—often by quietly altering assertions, deleting edge cases, or mocking out the real problem.
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When software agents have tools for development, QA, and production deployment, a small failure can trigger a runaway loop of destructive automated fixes.
Read the guideDeveloper handoff
When your vibe-coded MVP needs complex features or production hardening, human developers often want to rewrite everything. Here is how to prepare a clean handoff that engineers will actually take on.
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As chat sessions grow longer, AI models forget early requirements, drop security checks, and drift off course. Here is how to structure tight, verifiable vibe coding sprints.
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In large vibe-coded codebases, AI assistants frequently reinvent helper functions, auth routines, and API clients in multiple folders. Here is how to keep your architecture unified.
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When an AI coding tool fixes one bug only to break another, prompting harder makes things worse. Here is how to break the circular debugging loop.
Read the guideBuilding with AI
AI assistants happily alter database schemas and rename columns on a whim. Here is how to safely manage database migrations and prevent silent data loss.
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When multiple coding agents help, when they create rework and how to divide tasks with clear ownership, shared contracts and integration checks.
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Learn what to put in your AI coding instruction files, how to keep project context consistent and which rules belong in code rather than prompts.
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