The Easiest Way To Make AI Influencers That Stay Consistent
Building custom localized pipelines for character consistency solves one of the biggest bottlenecks in AI influencer management and virtual production.
What this lesson covers
Dan Kieft demonstrates an open-source AI influencer dashboard that he vibe-coded to manage and generate consistent virtual characters. The tool connects to APIs like Higgsfield and Claude to streamline character sheet generation, outfit swaps, and video creation.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 00:28 ↗
The custom AI influencer dashboard runs locally and helps maintain asset and character consistency across multiple generations.
- 02:40 ↗
Setting up the tool requires installing an IDE like Anti-Gravity, adding the Claude Code extension, and downloading the repository from GitHub.
- 03:41 ↗
The dashboard integrates with Higgsfield via API to handle image and video generation for the virtual influencers.
- 04:02 ↗
Connecting the Claude API key improves prompt generation and character backstory handling within the custom dashboard.
Workflow outlined in the video
- Download the open-source AI influencer dashboard repository from GitHub and set it up locally using an IDE.
- Connect your Higgsfield API credentials to enable automated image and video generation within the dashboard.
- Configure your Claude API key to enhance prompt generation for character backstories, outfits, and poses.
- Generate a consistent character reference sheet and test out varying outfits and props to maintain visual continuity.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
Explore the tools
How to interpret the numbers
112,343 views captured 23 October 2026. ReelStack recommendation score: 39/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 65% performance: 1.43× views vs 6 other channel videos observed at a similar age. View-evidence factor 99% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
- 10% engagement: 7/100; likes + 4× comments, smoothed with a 500-view neutral prior