This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
Every week, I help create social media content for local businesses. Before anything gets published, thereās a familiar list of decisions: what to talk about, which photos to use, what to write, and how to spread it across the week.
Sloth Social does the first draft of that work.
Enter your business details, audience, tone and weekly focus. Choose how many posts and stories you want, and the app generates a plan across Monday to Sunday.
You can then:
- Edit titles, captions and story text directly on the cards.
- Copy individual captions or download the complete week.
- Return to your latest plan, with edits saved in your browser.
- Add photos and ask an open-weight vision model to describe them.
- Correct and approve those descriptions before using them in a new plan.
Itās built for small business owners and the people managing their contentāespecially anyone who has ever opened a social media scheduler and immediately decided to make a coffee instead.
Demo
š¦„ Try Sloth Social
For a quick walkthrough:
- Enter a business profile and weekly focus.
- Choose two posts and seven stories.
- Generate your week.
- Edit a caption, copy it, and download the plan.
To try the photo workflow, select a product image, click Analyse photo, review the description and tick the box to include it in your next generation.
The app creates drafts for review. It doesnāt automatically publish them.
Code
How I Built It
The frontend uses React and Vite, with a Node.js and Express backend. The website is deployed on Netlify, and the server runs on Render.
Two open-weight models sit at the centre of the workflow, accessed through OpenRouter:
- Qwen3 32B generates the content plans.
- Qwen2.5 VL 72B Instruct describes uploaded photos.
One useful lesson came from scheduling. Initially, I asked the model to write the content and assign its days. Sometimes it placed two posts on the same day.
I moved the scheduling into JavaScript. The model writes the drafts; the server checks the requested quantities and distributes each content type across the week.
Photo testing revealed another issue: a model could recognise a product but add unsupported claims about its quality or ingredients. I tightened the instructions and added an editable review step. Users choose which descriptions enter the planning brief.
The backend also validates generated data, applies request timeouts and limits requests to the paid AI endpoints. API credentials stay on the server.
Why Does Open Innovation Matter?
The open-weight models do the core work: interpreting photos and turning a business brief into usable content drafts.
I chose hosted inference because my development Mac has 8 GB of memory. This version sends requests to hosted providers; it doesnāt run offline or keep uploaded photos entirely on the userās device.
The benefit of open weights here is flexibility
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