ResearchAlmost AI

Inspiration

What it does

💡 Inspiration

Staying updated with the massive volume of daily arXiv and SSRN papers is overwhelming. Traditional reading methods can't keep pace with modern information consumption habits. We wanted to bridge this gap by transforming complex academic data into an engaging, modern format: micro-learning through faceless, short-form reels. ResearchAlmost AI was born to turn dense scientific literature into bite-sized, automated audio-visual insights tightly integrated with a structured study scheduler.

🛠️ How We Built It (with ChatGPT 5.6 Terra)

This project was co-engineered from concept to deployment alongside ChatGPT (5.6 Terra), acting as an elite technical architect:

• Planning & Architecture: ChatGPT 5.6 Terra helped map the strict database schemas across Users, Papers, Reels, and StudyGoals, ensuring logical relational integrity.
• Prototyping: The AI rapidly accelerated the frontend prototyping, generating our full-screen mobile-first vertical layout, the dynamic CSS canvas background arrays, and kinetic caption styling.
• Base44 Engineering: ChatGPT 5.6 Terra engineered the final, precise system prompt for our Base44 integration. It established the deterministic JSON response schema {"hook_text", "scene_1_speech", "scene_2_speech", "call_to_action"} that fuels our GPT-4o and Text-to-Speech (TTS-1) automated media pipeline.

🧠 What We Learned & Key Formula

We learned how to tightly synchronize AI-generated text-to-speech assets with dynamic client-side animations. Additionally, we implemented a precise scheduling system where daily study quotas are strictly handled via the formula: $$\text{Daily Quota} = \left\lceil \frac{\text{Total Papers}}{\text{Timeline in Weeks} \times 7} \right\rceil$$

🚧 Challenges We Faced

During development, we discovered that executing backend serverless functions required an upgraded plan on our deployment platform. To bypass this barrier without compromising on product functionality, we adapted our strategy: we leveraged the integration SDK to implement the entire LLM + TTS pipeline logic directly inside the frontend. This preserved our automated database persistence while remaining highly cost-efficient and lightweight.

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for ResearchAlmost AI

Built With

• base44
• gpt-4o
• html5
• openai
• react
• tailwindcss
• text-to-speech-api
• typescript