Proof of Concept
Autonomous Operation of @cutepanel.
Over the past few weeks, we’ve been quietly running the @cutepanel Twitter account entirely through an autonomous agent — powered by Cute-Panel.
While the account had previously been run manually, this marked a shift to full automation — and performance slightly improved compared to manual curation.
This serves as our first real-world proof of concept:
LLM-driven behavior;
media scraping logic;
and automated posting.
All powered by our own agent stack.
How It Works
Cute-Panel runs as a headless Node.js agent, invoked by a CRON job that executes the full posting cycle once per day. No human intervention has been involved since initial setup.
1. Topic Guidance via Prompt Seeding
Even though the tweets are just images, we use a lightweight LLM prompt that helps shape the aesthetic and thematic pull of the selected images.
export const mangaPrompt = `
You are curating a social media feed that features exactly one carefully selected manga panel per day.
Focus exclusively on traditional Japanese manga panels, typically black-and-white artwork with characteristic linework and shading.
Your theme centers on romantic and heartwarming moments between couples: cute interactions, tender affection, shy glances, blushing, and gentle eye contact. The mood should be soft, emotional, and sweet.
Selection criteria:
- Panels must depict couples engaged in affectionate gestures like holding hands, subtle touches, quiet smiles, or intimate moments.
- Exclude any panels with excessive text, speech bubbles, or sound effects; minimal text is acceptable only if it does not detract from the visual.
- Images must be high-resolution, clear, and visually sharp. Avoid blurry, pixelated, or low-quality panels.
- Prioritize lesser-known or underrated manga series to highlight unique content rather than popular mainstream titles.
- Only select panels with traditional manga style: black-and-white or grayscale, no colored art or manhwa/manhua styles.
- Avoid violence, action scenes, explicit content, or emotional distress.
- Source panels from reputable manga archive sites or legal manga databases to ensure safe and proper use.
Return only the direct URL link(s) to single manga panel images that fit these criteria. Provide no captions or additional commentary—only clean, direct image links.
Maintain a consistent visual and thematic standard to create a charming, romantic, and cute daily manga panel feed.
`;Claude is invoked once daily with this system prompt and no user message. It returns 3–5 image URLs for candidate selection.
2. Content Sourcing
Claude’s outputs are passed into a custom imageVerifier module that filters by:
Image dimensions (landscape format preferred)
File size
Duplicate detection
NSFW filtering (via a basic vision API integration)
If multiple candidates pass, one is selected at random.
const filtered = await filterImagesFromClaude(candidates, {
minResolution: [600, 400],
nsfwScan: true,
dupeHashDB: localCache,
})3. Twitter Media Upload
The selected image is uploaded using Twitter’s media endpoint:
await twitterClient.v1.uploadMedia(imageBuffer, { type: 'png' })We intentionally do not add captions — this keeps the account neutral, algorithm-friendly, and focused on mood.
4. Posting Schedule
An external cron service (e.g. cron-job.org) triggers the agent once per 24h. No cloud functions or persistent server is required.
# Shell command run by cron
node dist/postMangaPanel.jsThe agent runs headlessly, posts once, and exits.
Results and Observations:
From May 14 to June 6, 2025, @cutepanel was run completely autonomously for 24 consecutive days. No manual interventions occurred during this window.
This is not a simulation. These are real posts, live content fed to a real audience, and an almost unsupervised agent operating in production.
Twitter Engagement
Despite having no captions, no hashtags, and no manual promotion, the bot generated impressive organic reach simply by posting visually appealing, well-curated manga content once a day.
Total Tweets Posted
45
36
Total Impressions
~2,789,000
~2,300,000
Avg. Impressions per Tweet
~62,000
~53,000
Total Likes
~157,000
~123,000
Avg. Likes per Tweet
~3,489
~2,800
Top Tweet
403K views, 22K likes, 1.7K reposts
337K views, 13K likes, 1.7K reposts
New Followers Gained
+~3.4K (est.)
+~1K (est.)
Engagement Rate
~12.5–13%
~10–11%
Across nearly identical posting volumes, autonomous posts slightly outperformed manually curated ones—likely due to factors such as improved visual consistency, guidance from large language models, and reliable daily scheduling.
However, when considering the time and effort required to manually browse for content, create posts, and optimize posting schedules across different time zones, autonomous posting presents a clear efficiency advantage.
These figures represent organic reach — no paid promotion, hashtags, or algorithm gaming. The highest-performing panel received 403K views and 22K likes, purely based on visual strength.
System Stability
We monitored and logged all post cycles to assess system uptime, Claude reliability, and Twitter media upload behavior.
Prompt Execution (Claude)
98.50%
Media Fetch Success
89.00%
Media Filtering/Approval
96.25%
Twitter Upload Success
100%
Cron Execution (Daily)
100%
No critical failures were observed during the full test period.
2 posts required fallback logic due to Claude returning dead image URLs — caught by the verifier module and recovered via a cached candidate pool.
Notably, the agent maintained higher uptime consistency and a stricter daily cadence than human posting.
Log Snapshot
Each post generates an internal audit entry like:
{
"date": "2025-05-16T07:00:12Z",
"image_url": "https://cdn.mangapanels.net/img/romantic-panel-182.png",
"tweet_id": "1790349821348317186",
"source": "Claude",
"nsfw_scan": "Passed",
"upload_status": "Success",
"log_digest": "7cb39b5"
}Logs are mirrored to a private Discord webhook, allowing quick reviews and potential rollback if needed.
Performance Summary
The agent demonstrated:
Operational reliability under zero-maintenance conditions
High content relevance with only one prompt and no human filtering
Engagement above Twitter average for similar small accounts
Recoverability from LLM or image failures via custom filters
Cute-Panel is already capable of running niche content accounts at scale — autonomously.
Future Enhancements
Based on the success of this proof of concept, future developments may include:
Threaded Posts: Posting multiple panels in a single thread.
Caption Generation: Using AI to generate contextually relevant captions.
User Interaction: Responding to mentions or DMs with curated content.
Blockchain Integration: Implementing token-based engagement rewards on Solana.
This proof of concept demonstrates the viability of using AI-driven agents for autonomous content curation and social media management. The Cute-Panel system showcases how combining prompt engineering, content validation, and API integration can result in a reliable and engaging social media presence without human oversight.
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