Product updates
Features, use cases, and product updates
How to Choose an AI Prompt Generator and Multi-Model Workspace
A useful evaluation compares input support, model transparency, versioning, privacy boundaries, quota clarity, and workflow exits instead of counting model logos.
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Where Public Prompts End and Private Source Material Begins
Public pages contain intentionally published or editorially selected material; private workspace prompts and uploaded sources do not become Plaza entries merely because they were processed.
How Lingmo Estimates, Freezes, Charges, and Releases Quota
Estimation, temporary reservation, final charging, and release of unused quota explain the visible balance changes around a generation; policy refusals are not charged.
Using Lingmo for Writing, Programming, SEO, and Marketing
Lingmo turns writing, programming, SEO, and marketing requests into testable prompts, then supports comparison and revision before users execute or publish elsewhere.
From Scenario Templates and the Prompt Plaza to a Private Workspace
Scenario templates provide structure, the Prompt Plaza contains intentionally published work, and the private workspace holds personal material under a different visibility boundary.
How to Version, Save, Restore, and Reuse Prompts
Version history, saved items, restoration, and copy-based reuse make prompt iteration traceable without overwriting a known-good result.
How Multi-Model Comparison and Blind Review Reduce Brand Bias
Running the same prompt against multiple enabled models and hiding their identities during review keeps attention on evidence, fit, and instruction-following.
Natural Language, Image Reverse Engineering, and Video Keyframes in Lingmo
Lingmo can turn natural-language tasks, reference images, and video keyframes into editable prompts, while leaving downstream image and video production to dedicated tools.
What Is Lingmo AI? A Multi-Model Prompt Workspace
Lingmo brings prompt drafting, generation, comparison, version management, and reuse into one workspace organized around the task rather than a model brand.