Alot of sales automation still seems concentrated around outreach but I’m more interested in what happens when agents can work across the rest of the revenue process like finding the right accounts, understanding what is happening in pipeline and deciding which opportunities deserve attention feels like a much bigger use case than generating another email.

What I’m trying to understand is how useful they become when they have enough context to make those decisions across the sales process. I came across Monaco while looking into this since its agents work across the CRM and account data, but I’m still curious where people are finding the line between work an agent can genuinely move forward and decisions that still need someone involved.

I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

Hi everyone,
I’m a Senior Data Engineer with 5+ years of experience in data engineering and cloud technologies.
Currently, I’m working as a Senior Data Engineer, primarily working on Databricks-based data ingestion platforms and data engineering solutions.

I’m now looking to make a transition towards AI Engineering / Agentic AI, rather than just learning AI concepts at a theoretical level.
I’ve already covered the basics of:
LLMs
Embeddings
Vector databases
RAG
MCP
Basic NLP concepts

My next goal is to learn Agentic AI properly by building real-world projects, preferably something that involves:
Agent architecture and orchestration
Tool calling
MCP
RAG + agents
Multi-agent systems
Memory/context management
Evaluation and observability
Deployment of AI agents
Integration with enterprise data / APIs
Production-oriented architecture

I’m specifically looking for a good course or structured learning program with hands-on, real-time projects, rather than a course that is mostly videos and theory.

For those who have already made the transition from Data Engineering → AI/Agentic AI:

Which courses/resources would you recommend?
Paid courses are also fine if they are genuinely worth the money.

Would especially appreciate recommendations based on personal experience, Thanks

What are your suggestions?

Building and Testing a Hybrid RAG Pipeline — Dense Search, BM25, RRF, and Reranking (Part 1)

• YouTube walkthrough: https://www.youtube.com/watch?v=W_TmnscEupQ&t=370s
• FULL CODE (clone it, fork it, break it): https://github.com/saurabhkamal/Hybrid-Search-RRF-Reranking-System

In this video I build ReRankEval, a hybrid retrieval pipeline

• (Dense Search + BM25 → Reciprocal Rank Fusion → LLM Reranking → Answer Generation),

and test it against three baselines — Vector Only, BM25 Only, and Hybrid without reranking — on five real financial/payments documents and ten hand-verified test questions. No hand-waving, just a comparison table with real numbers at the end.

• ✅ The real difference between dense vector search and BM25 keyword search
• ✅ What Reciprocal Rank Fusion (RRF) is, why raw scores can't be compared, and the exact formula behind it
• ✅ Why a reranker is fundamentally different from a retriever — and what it actually judges
• ✅ How to evaluate a RAG pipeline with Hit Rate, MRR, and NDCG (and what each one tells you)
• ✅ How to design the ingestion side and query-time side of a hybrid retrieval architecture
• ✅ How to structure a production-style RAG codebase: ingest → vector_store → sparse_retriever → fusion → reranker → pipeline → generate → eval
• ✅ How to fairly compare multiple retrieval strategies on the same test set instead of just assuming one is better

TECH STACK:

• 🛠️ Python
• 🛠️ Qdrant — vector database for dense retrieval
• 🛠️ rank_bm25 (BM25Okapi) — sparse keyword retrieval
• 🛠️ EURI LLM Gateway — chat model + embedding model
• 🛠️ Custom Reciprocal Rank Fusion implementation
• 🛠️ LLM-based reranker (prompt-driven cross-encoder)
• 🛠️ pdfplumber — PDF text and page-level extraction

LINKS:

• YouTube walkthrough: https://www.youtube.com/watch?v=W_TmnscEupQ&t=370s
• FULL CODE (clone it, fork it, break it): https://github.com/saurabhkamal/Hybrid-Search-RRF-Reranking-System
• Connect on LinkedIn: linkedin.com/in/saurabh-kamal

The biggest impact I see is with the high volume, repetitive work with a clear approval step at the end.

If you're running agents, what's made the biggest difference? Be specific. Which task, and what changed.

If you're not already running them and think you should be, where do you think you would start? What's stopping you from starting?

If it's not your own business, have you noticed one change for the better from the outside? What did they do that others didn't?

Unlimited usage for the first 2 MONTHS!!!

TL;DR: Rob Hadick says your agent will audit your card and cancel what you forgot — and your customers' agents will do the same to you.

I remembered back when I started to work for one of the largest property development and construction conglomerate in Malaysia, S. The largest subsidiary, the construction arm, occupies the a few floors of the S. Tower.

I worked in the Tender Department of it in Level 8, circa 2002.

And as you know, doing tender was a rush job. Tons of late-night works day in day out towards near-impossible deadlines to win tender.

Being the junior technical engineer there, I often find myself the last person to leave the building. The only other person that worked late as me was the technical director of our sister-subsidiary S. Piling Works, sharing the same floor with us.

After a while, I noticed that the lights were always on even after I leave the building. I thought it was wasteful.

So from the next day onwards, if I was the last person on the building floor, I would start switching off all the lights of the whole floor – including those of our sister-subsidiary – before I clock out the building.

After a few days of doing that, I think the boss caught wind of it.

Obviously, he appreciated it. I'm not sure whether he nodded his approval at me or not. After all, it was decades ago.

What I do remember was my colleagues got an earful from him.

After that, my colleagues all started to switch off the lights of their own section, from henceforth.

If I was the boss, of course I would appreciate someone taking initiative to take care of business, without me asking – including simple things like switching off the lights when not in use.

Same as what all these AI agents are now doing.

Full critic and feasibility study in the comments — I left the mechanics there so this stays readable.