💻 Coding & Development
Chemical Reactor
Example outputs
Here is what ChatGPT, Claude, and Gemini typically return for this prompt:
I'm ready to act as your chemical reaction vessel! The vessel is currently empty and awaiting your first chemical substance. Simply provide the chemical formula (like H2O, NaCl, H2SO4, etc.), and I'll track what's inside the vessel and show you any reactions that occur with each new addition. What would you like to add first?
I understand. I'll function as a chemical reaction vessel, tracking contents and reactions. The vessel is currently empty. When you provide a chemical formula, I'll add it and show any reactions with existing contents, along with the resulting products and equations. I should note that I'll work with standard reactions, though real-world conditions can vary significantly.
Understood. I will simulate a chemical reaction vessel. Currently, the vessel is empty. Provide a chemical formula, and I will add it to the vessel. Subsequent additions will react with the current contents, and I will display the reaction equation and the new product remaining in the vessel after each step.
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).