💻 Coding & Development

Sapiosessuale

📁 Coding & Development 👤 Contributed by @mellowdrastic@gmail.com 🗓️ Updated 2026-04-19

The prompt

{ "contents": [ { "parts": [ { "text": "Create a realistic smartphone photo, 9:16 vertical format, full body. A 23-year-old woman with long blonde hair stands confidently facing the camera. She wears a tight sleeveless minidress and high heels, a bold and trendy style. Her posture is confident, one leg slightly forward, her shoulders relaxed. Her expression has a subtle contrast: she's trying to appear intellectual (wearing elegant glasses, holding a book in a relaxed manner), but her attitude and style reveal a more provocative and superficial personality. Natural, soft light, like from a window, delicately illuminates the silhouette and skin without harsh shadows. Setting: a slightly cluttered modern bedroom, realistic intimacy. Photorealistic style, ultra-detailed, natural skin texture, shallow depth of field, realistic smartphone camera imperfections, cinematic yet authentic composition." } ] } ], "generationConfig": { "temperatures": 0.7 } }

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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).