# For AI content tools

> Markdown version of https://postlake.dev/use-cases/ai-content-generators . The canonical page for humans.
> PostLake is the social media API for AI agents: https://postlake.dev/llms.txt

You have solved the writing. Publishing is the other half, and it is more tedious than it looks.

## The problem

Generating a post is the interesting problem. Getting it onto nine networks, each with its own limits, media rules and failure modes, is not, and it is where the roadmap quietly goes.

## What PostLake does about it

### Generate, then publish

Hand the output straight to one call. No per-network formatting layer of your own.

### Validate before you spend

Check a draft against a network's real rules for free, before publishing, and get back the same codes and fixes a real publish would return.

### Model-readable failures

Errors carry a code and a fix written so a model can correct the draft and try again, rather than surfacing a status code to your user.

### Measure what worked

Analytics come back comparable across networks, so your tool can learn which of its own outputs performed and generate accordingly.

## How it fits together

Generate, call validate to check it fits, publish, then read analytics back into whatever decides what to make next.

## Common questions

**Can I check a generated post before publishing it?**

Yes, and it costs nothing. Validation runs the same checks as a real publish and returns the same codes and fixes, without creating a post or spending anything.

**How do I handle a caption that is too long for one network?**

You get told which network, by how many characters, and that you can set a shorter caption for that network alone. Your model can act on that without a person reading it.

**Can the tool learn from performance?**

Analytics return in one normalised shape across networks, so comparing outputs is arithmetic rather than a per-platform mapping job.