Project Info
Inspiration
CreateFlow was inspired by the pressure content creators face managing ideas, drafting, and posting across platforms, leading to burnout. I wanted to create a tool that helps creators focus on their craft while automating repetitive tasks using AI-driven agents and LLMs to streamline content creation and scheduling. What We Learned As a team, we explored LLMs for generating human-like text and AI agents for automating scheduling and trend analysis. We also learned to design a user-friendly dashboard for manual review due to challenges with social media API access. What It Does CreateFlow automates content generation based on user preferences (interest, type, keywords, frequency). It creates a content schedule, generates posts, and suggests future topics, all displayed on a dashboard for review and manual posting. How We Built It We used Python for the backend, Next.js for the dashboard, and the Gemini API to generate content. Fetch AI agents handle scheduling and topic suggestions, while the dashboard offers full control to users. Challenges We Ran Into API limitations for social media posting led us to build a manual dashboard. Ensuring AI-generated content was natural and engaging required fine-tuning language models. Accomplishments We successfully integrated AI to reduce mental load for creators, automating content generation while giving users control through the dashboard. What's Next We plan to integrate more platforms, enhance AI trend detection, and expand dashboard features, including analytics and automated posting as API access improves.
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Analysis
View
Metric
- 2
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- HTMLIn code
- JavaScriptIn code
- PythonIn code
- ReactIn code
- FlaskClaimed
- Google GeminiClaimed
- Node.jsClaimed
5 of 8 appear in the indexed code. 3 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
60 KB
Source files
36
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
dhrumilankola/Calhacks11_CreateFlow
48 files · 823 KB · @ 96d1986
Structure
Interface
20 files · 42%Screens, components and styles rendered to the user.
Application logic
13 files · 27%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Python36%
- JavaScript29%
- CSS27%
- Markdown6%
- HTML3%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
frontend/package.json
npm · 10- @testing-library/jest-dom
- @testing-library/react
- @testing-library/user-event
- axios
- bootstrap
- cra-template
- react
- react-dom
- react-scripts
- web-vitals
package.json
npm · 1- react-markdown
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
This project’s features have not been analysed yet.
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