u/Proof_Assumption_500

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I've been trying to replace some of my doomscrolling with reading, and Substack seems like a goldmine if you know where to look.

I'm mainly into psychology, philosophy,

human behavior,emotions, habits, self-awareness, decision-making, and those articles that leave you staring at the ceiling for a few minutes afterward. 😭

Not really looking for the super mainstream picks, I want the hidden gems that make you think differently or see people (and yourself) in a new way.

What are your favorite Substacks in this space? And if there's one article that instantly made you subscribe, I'd love to read that too.

Hi everyone,

I recently got assigned a Legal RAG project as part of my internship, and I have just 24 hours to build the first working version.

The application is a chatbot with a ChatGPT-like interface where users can manually upload legal PDFs and ask questions about them. The focus is on building a clean, reliable RAG pipeline rather than training any models.

The tech stack is fixed, so I have to stick with:

Frontend: Vite + Tailwind CSS Backend: Go (Golang) Database: SQLite LLM: Gemini Flash PDF Upload + Chat Interface RAG pipeline for document question answering

The idea is straightforward:

User uploads one or more legal PDFs PDFs are processed and indexed User asks questions in natural language System retrieves relevant chunks from the uploaded documents Gemini Flash generates answers grounded in the retrieved context with citations whenever possible

Since this is my first production-style RAG project (and my first backend in Go), I'd really appreciate advice from people who've built similar systems.

A few questions I have:

What's the simplest yet scalable RAG architecture you'd recommend for this stack? Which embedding model would you use with Gemini Flash? Should I use SQLite only for metadata and keep embeddings in a vector database, or is there a lightweight alternative that works well? Any Go libraries you'd recommend for PDF parsing, embeddings, or vector search? How would you structure the ingestion pipeline (upload → chunking → embedding → retrieval)? What's the biggest mistake beginners make while implementing RAG? If you only had 24 hours, what would you prioritize to make it look production-ready?

I'm not trying to build a perfect legal assistant yet—just something with a clean architecture that demonstrates a solid RAG pipeline and can be extended later.

Any GitHub repositories, blog posts, architecture diagrams, or lessons learned would be incredibly helpful.

Thanks in advance!

Hi everyone,

I'm a complete beginner when it comes to deployment and could really use some help.

I've built a full-stack AI project with:

• Backend: FastAPI
• Frontend: Streamlit
• The project works perfectly on my local machine and is pushed to GitHub.

I'm trying to deploy it, but I'm getting confused about things like configuring the backend/frontend, ports, environment variables, and deployment settings. I've never deployed a project before, so I'm not sure what I'm doing wrong.

If someone has experience deploying FastAPI + Streamlit projects, would you be willing to guide me through it? It's quite urgent as I need to get it deployed soon.

I'd really appreciate any help, whether it's through comments or a quick chat. Thank you!

Hi everyone,

I'm building a legal AI assistant as part of my internship and would really appreciate some advice from people who've worked on production-grade RAG systems.

The chatbot is intended for Indian law. Initially, I'm planning to build the knowledge base using:

• Indian Constitution
• Bharatiya Nyaya Sanhita (BNS)
• Bharatiya Nagarik Suraksha Sanhita (BNSS)
• Bharatiya Sakshya Adhiniyam (BSA)

The idea is that a user describes an incident in natural language (e.g., "Someone broke into my house and stole my phone"), and the system should:

• Identify the likely offense(s)
• Map the incident to the relevant legal sections
• Explain why those sections apply
• Suggest the general legal procedure/course of action
• Cite the exact provisions used (to minimize hallucinations)

My current plan is:

• Build a robust RAG pipeline first
• Add a multi-agent workflow (fact extraction → retrieval → legal reasoning → response validation)

The catch is that I'd like to stay within free tiers as much as possible during development.

Current stack I'm considering:

• LlamaIndex or LangGraph
• Qdrant Cloud (Free)
• Hybrid Search (BM25 + Dense Embeddings)
• BGE embeddings + BGE reranker (run locally)
• FastAPI backend
• Groq (Qwen/Llama models) for inference
• RAGAS for evaluation

I'd love feedback on a few things:

1. Is this stack a good choice, or are there better free alternatives?
2. For legal RAG, would you recommend Qdrant, pgvector, Weaviate, or something else?
3. Is LlamaIndex still the best option for RAG, or should I build most of the pipeline manually?
4. Any recommendations for publicly available Indian legal datasets beyond the Constitution, BNS, BNSS, and BSA?
5. How would you design the multi-agent workflow for a legal assistant?
6. What were the biggest challenges you faced with legal RAG (chunking, retrieval quality, hallucinations, evaluation, etc.)?

I'm aiming to build something that's reliable enough to demonstrate production-style architecture, not just a basic chatbot. Any recommendations, papers, GitHub repositories, or lessons learned would be greatly appreciated.

Thanks in advance!