2025-11-18 06:10:23Sakshi Mahajan:
Added challenges and roadmap
Projects/Sentify.md ..
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> ## Challenges & Learnings
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What was the hardest part of this project? What did your team learn about the AI models, the data, or the problem domain?
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Building Sentify involved solving complex technical problems across AI, backend, frontend, and deployment layers. The hardest challenges and the biggest learnings include:
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#### 1. Understanding AI Models & Prompt Engineering
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We had to learn how AI models interpret text, score sentiment, and classify news with accuracy. Creating prompts that consistently produce reliable results was challenging, but it helped us master effective prompt engineering and understand how small changes in prompts can dramatically affect outputs.
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#### 2. Working With Vector Embeddings
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This was our first time using vector databases (pgvector) and learning how embeddings store semantic meaning. Understanding similarity search, cosine distance, and how to structure news embeddings for fast retrieval was a major learning milestone.
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#### 3. Python Ecosystem & NLP Processing
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We learned Python more deeply — from building ingestion pipelines to integrating NLP models, handling async tasks, and optimizing data processing. This included working with libraries like Pandas, Sentence-Transformers, LangChain, and the Gemini API.
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#### 4. Backend & Infrastructure Challenges
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We faced challenges in:
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- Setting up servers for both Python (FastAPI) and Node.js
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- Managing API scaling and efficient database querying
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- Learning Redis Stack for caching, real-time alerting, and queueing
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- Implementing Sequelize ORM with PostgreSQL
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These tasks strengthened our backend architecture and deployment skills.
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#### 5. Frontend Challenges (Flutter)
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The most difficult part on the frontend was:
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- Integrating real-time graphs with news sentiment
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- Syncing graph data with live market trends
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- Managing API delays, inconsistent data formats, and UI refresh issues
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This helped us better understand state management and dynamic data visualization.
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#### 6. Project & Time Management
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Coordinating three different modules (AI, alerting engine, backend) within a short time was challenging.
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We learned:
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- How to divide responsibilities smartly
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- Use AI tools to speed up development
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- Manage scope, deadlines, and deliverables
> ## Future Roadmap
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## What are the next steps for this project if it were to continue?
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* [ ] Short-term goal (e.g., Improve model accuracy to 95%)
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* [ ] Medium-term goal (e.g., Launch a mobile app)
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* [ ] Long-term goal (e.g., Open-source the project)
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If this project continues, here are the strategic next steps:
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#### Short-Term Goals (0–2 months)
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- Increase sentiment prediction accuracy.
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- Improve noise filtering and source credibility scoring
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- Enhance Flutter UI for smoother live chart rendering
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- Optimize ingestion pipeline for faster turnaround
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- Begin analysis of additional news resources, sources, and feeds to expand coverage
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#### Medium-Term Goals (2–6 months)
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- Launch the Sentify Mobile App (Flutter) publicly
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- Add multi-source comparative analysis (impact ranking from different news outlets)
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- Introduce custom user alert rules (coin-specific, FUD alerts, volume spikes, etc.)
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- Build a web-based analytics dashboard for historical sentiment