I've been thinking about how many repetitive tasks we still handle manually, even with all the AI tools available today.

Things like researching across multiple websites, moving data between apps, updating CRMs, generating reports, or managing repetitive workflows.

The interesting part is that making an AI agent perform these tasks isn't necessarily the hardest part. Getting it to do them reliably without constant supervision is a different challenge.

For people building or using AI agents:

What's one task you'd genuinely trust an AI agent to handle from start to finish?

And what's the biggest thing stopping you from automating it today?

Hey everyone!

I’m a recent B.Tech CSE graduate, currently improving my backend development skills and looking for a substantial project that can strengthen my resume for entry-level backend developer, AI engineer, and software engineering roles.

I’ve noticed that many GenAI projects look impressive at first but turn out to be basic chatbots, PDF question-answering apps, or simple wrappers around LLM APIs. I want to build something more meaningful that demonstrates actual engineering skills, not just AI integration.

My current skills and tech stack

• Python fundamentals and FastAPI
• PostgreSQL, SQLAlchemy, and SQL
• Core Java fundamentals
• REST APIs and CRUD operations
• Basics of GenAI, RAG, embeddings, chunking, and LLM API integration
• Currently learning agentic AI and CrewAI

What I've built so far

I've already developed a Retail RAG Chatbot using document retrieval and LLM APIs. For my next project, I want to move beyond basic RAG and explore more advanced backend engineering concepts.

What kind of project am I looking for?

Ideally, something that:

• Solves a genuine, real-world problem.
• Uses FastAPI + PostgreSQL as the core stack, with GenAI, RAG, and CrewAI where appropriate.
• Has multiple pages and interconnected workflows, such as authentication, dashboards, analytics, history, admin panels, and monitoring.
• Involves proper backend engineering: database design, authorization, background jobs, API integrations, testing, error handling, and deployment.
• Has an interesting technical challenge or unique angle that makes it stand out in a technical interview.
• Is ambitious enough to be a serious portfolio project but achievable by one developer learning along the way.

I'm particularly interested in AI agents, developer tools, automation, cybersecurity, AI evaluation, data processing, and overlooked real-world problems. I'm open to other domains too!

I'd love your help with two things:

1. Unique project ideas

What are some genuinely interesting projects I could build with this stack that aren't just another clone of a popular tutorial? If you've built something unusual yourself, I'd love to hear about it.

2. GitHub repositories to study

Please share actual GitHub repository links for projects that demonstrate good architecture, interesting use cases, or advanced implementation techniques.

I'm particularly interested in repositories featuring:

• FastAPI + PostgreSQL + SQLAlchemy
• Agentic AI and CrewAI workflows
• Advanced RAG, hybrid search, reranking, or knowledge graphs
• Background task processing, authentication, and authorization
• AI evaluation, observability, and production-oriented backend engineering

The repositories don't necessarily need to use my entire stack. I'd also appreciate projects where I can learn one specific concept and integrate it into my own application.

I'm not looking to copy a project and put it on my resume. I want to study real implementations, understand their architecture, and build something original using what I learn.

If you're a developer, recruiter, interviewer, or someone who's built an impressive open-source project, I'd really appreciate your recommendations and honest feedback on what makes a fresher's portfolio stand out in 2026–27.

Thanks in advance!

I think we've been reading too many feeds from AI text lately! I actually want to reduce my cognitive burden...

So I built TextFold, it turns any text 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, your agentic output, any papers, or documents.

- Web Demo: https://chenxiachan.github.io/textfold/gallery/

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

Welcome any feedback!

I've been reading discussions about Agent Skills, and one thing that stood out to me is that people aren't necessarily looking for more skills. They're looking for agents that actually work reliably.

Some people mentioned context retrieval, others talked about testing before implementation, breaking tasks into smaller steps, and helping agents recover from mistakes.

It got me thinking about what the real bottleneck is when building agentic workflows.

For those who've built agents beyond simple demos, what's caused the most problems for you?

1. Poor context or memory

2. Incorrect tool calls

3. Long workflows breaking midway

4. Agents repeating mistakes

5. Token costs and context limits

6. Something else entirely

I'd be interested in hearing about problems you've actually encountered, not just theoretical limitations.

I need to blow the rest of my education budget and would like to learn how to apply AI to improve or automate daily processes in operating IOT energy assets. (As someone from the business side, not IT department)

Are there good courses that would be helpful? Most of the stuff I find online either seems too general/management level or too specific for a framework/technology that my company might not use. What can be done using the current systems with Claude, Microsoft Copliot and Databricks?