💻 Coding & Development

Young woman with bikini

📁 Coding & Development 👤 Contributed by @GemoCrypto 🗓️ Updated 2026-04-19

The prompt

{ "image_prompt": { "subject": { "description": "Young woman with shoulder-length blonde hair.", "face": "Neutral expression, looking directly up at the camera." }, "clothing": { "top": "Black string bikini top with gold O-ring hardware.", "bottom": "Matching black string bikini bottoms with gold O-ring hardware.", "accessories": "A small gold pendant necklace and a belly button piercing.", "style": "Two-piece black bikini set with metallic details." }, "pose": { "action": "Sitting upright on the edge of a lounge chair.", "hands": "Arms resting behind her back on the chair.", "angle": "High-angle, full-portrait view." }, "environment": { "location": "Outdoor patio.", "foreground": "Grey mesh lounge chair.", "background": "Textured stone pavers and green bushes." }, "technical_details": { "lighting": "Bright, direct natural sunlight creating sharp shadows.", "medium": "High-resolution photograph.", "style": "Realistic, clear, detailed photo." } } }

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How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).