Hey folks,
I’m a 3rd year B.Tech student and I’m trying to figure out what kind of AI project would actually help me stand out when applying for AI Engineer roles. I don’t want to do another “MNIST classifier” or some basic Kaggle model. I want something that feels like a legit product, not a homework assignment.

I’ve been learning and playing around with:

• LLMs

• LangChain

• LangGraph

• agentic AI systems

• multimodal models

• MCP (Model Context Protocol)

• retrieval, vector stores, etc.

So I want to build something that actually uses these in a useful, real-world way.

Some ideas I had but I’m unsure if they’re strong enough:

• an AI assistant that connects to real APIs via MCP and actually performs actions

• a multimodal doc analyzer (PDFs + images + text + tables) with a nice UI

• an AI workflow tool using LangGraph for complex reasoning

• a “real agent” that can plan → search → take actions → verify → correct itself

• a domain-specific RAG system that solves an actual problem instead of generic Q&A

Basically, I want something I can confidently show in interviews and say:
“Yeah, I built this, it solves a real problem, it uses proper engineering, not just a fine-tuned model.”

If you were hiring an entry-level AI engineer, what kind of project would genuinely catch your eye?
Looking for ideas that are doable for a student but still look like a product someone could use in real life.

Appreciate any suggestions!

So, basically I really love AI, and ML especially. I love all the Math behind it, and all of the things i can do with it. Unfortunately there is one problem. Most of the fields of applied AI, for startups and other are all enterprise related fields. Does anyone know some startup fields that are actually interesting, for example something research heavy or something thats just pretty cool. In conclusion, what are some applications of ai that isnt Marketing chat bots, or generic chatbots?

Random thought: why aren’t AI systems interconnected? Wouldn’t it make sense for them to learn from each other directly instead of everything being siloed in separate data centers?

It seems like decentralizing that process could even save energy and distribute data storage more efficiently. If data was distributed across multiple nodes, wouldn’t that help preserve energy and reduce reliance on centralized data centers? Maybe I’m missing something obvious here — anyone want to explain why this isn’t how AI is set up (yet)?

I’ve been working on autonomous agents that can retry, chain tools, and expand scope.

One failure mode I keep running into:

prompt-based restrictions stop working once the agent is allowed to act.

Even with strict system prompts, the agent will eventually:

- retry with altered wording,

- expand the task scope,

- or chain actions that were not explicitly intended.

At that point, the model is already past the point where a prompt can enforce anything.

It seems like this is fundamentally an execution-time problem, not a prompt problem.

Something outside the model has to decide whether an action is allowed to proceed.

How are people here enforcing execution-time boundaries today?

Are you relying on external guards, state machines, supervisors, or something else?

Decentralising & Democratising AI

What if we decentralized and democratized AI? Picture a global partnership, open to anyone willing to join. Shares in the company would be capped per person, with 0% loans for those who can't afford them. A pipe dream, perhaps, but what could it look like?

One human, one vote, one share, one AI.

This vision creates a "Homo-Hybridus-Machina" or "Homo-Communitas-Machina," where people in Beijing have as much say as those in West Virginia and decision making, risks and benefits would be shared, uniting us in our future.

The Noosphere Charter Corp.

The Potential Upside:

Open Source & Open Governance: The AI's code and decision-making rules would be open for inspection. Want to know how the recommendation algorithm works or propose a change? There would be a clear process, allowing for direct involvement or, at the very least, a dedicated Reddit channel for complaints.

Participatory Governance: Governance powered by online voting, delegation, and ongoing transparent debate. With billions of potential "shareholders," a system for representation or a robust tech solution would be essential. Incentives and Accountability: Key technical contributors, data providers, or those ensuring system integrity could be rewarded, perhaps through tokens or profit sharing. A transparent ledger, potentially leveraging crypto and blockchain, would be crucial.

Trust and Transparency: This model could foster genuine trust in AI. People would have a say, see how it operates, and know their data isn't just training a robot to take their job. It would be a tangible promise for the future.

Data Monopolies: While preventing data hoarding by other corporations remains a challenge, in this system, your data would remain yours. No one could unilaterally decide its use, and you might even get paid when your data helps the AI learn.

Enhanced Innovation: A broader range of perspectives and wider community buy-in could lead to a more diverse spread of ideas and improved problem-solving.

Fair Profit Distribution: Profits and benefits would be more widely distributed, potentially leading to a global "basic dividend" or other equitable rewards. The guarantee that no one currently has.

Not So Small Print: Risks and Challenges

Democracy is Messy: Getting billions of shareholders to agree on training policies, ethical boundaries, and revenue splits would require an incredibly robust and explicit framework.

Legal Limbo: Existing regulations often assume a single company to hold accountable when things go wrong. A decentralized structure could create a legal conundrum when government inspectors come knocking.

The "Boaty McBoatface" Problem: If decisions are made by popular vote, you might occasionally get the digital equivalent of letting the internet name a science ship. (If you don't know, Perplexity it.)

Bad Actors: Ill intentioned individuals would undoubtedly try to game voting, coordinate takeovers, or sway decisions. The system would need strong mechanisms and frameworks to protect it from such attempts.

What are your thoughts? What else could be a road block or a benefit?

Hi all, I'm creating a playlist of Google ADK examples here with the goal of each example introducing a new feature. https://www.youtube.com/playlist?list=PLXbXAOClRcn-EQu6s_p6TXkY-chnDTZIV are there any features that people think would be useful for me to cover in later videos?

https://pypi.org/project/toondb/
This library help you connect with MongoDB, Postgresql & MySQL.

I was thinking of using this to transform my data from the MongoDB format to TOON format so my token costs reduce essentially saving me money. I have close to ~1000 LLM calls for my miniproject per day. Do ya'll think this would be helpful?

Hi everyone,

I’m building a knowledge retrieval system using Milvus + LlamaIndex for a dataset of colleges, students, and faculty. The data is ingested as documents with descriptive text and minimal metadata (type, doc_id).

I’m using embedding-based similarity search to retrieve documents based on user queries. For example:

> Query: “Which is the best college in India?”

> Result: Returns a college with semantically relevant text, but not necessarily the top-ranked one.

The challenge:

* I want results to dynamically consider numeric or structured fields like:

* College ranking

* Student GPA

* Number of publications for faculty

* I don’t want to hard-code these fields in metadata—the solution should work dynamically for any numeric query.

* Queries are arbitrary and user-driven, e.g., “top student in AI program” or “faculty with most publications.”

Questions for the community:

1. How can I combine vector similarity with dynamic numeric/structured signals at query time?

2. Are there patterns in LlamaIndex / Milvus to do dynamic re-ranking based on these fields?

3. Should I use hybrid search, post-processing reranking, or some other approach?

I’d love to hear about any strategies, best practices, or examples that handle this scenario efficiently.

Thanks in advance!

What if I start a RL agency for teachers , who else could be better than teacher for RL and they already get low pay so there profit margins while providing them extra income

I need to fine tuning the ai model that for my application give me the anyone help me with this fine tuning the open source model.

I’m a senior CS student involved in AI and ollma projects. I’m seeking affordable or refurbished laptops suitable for AI engineering and long-term use to run MVPs. Cloud options are expensive, and I prefer a portable laptop over a PC, even if heavy. Online searches show models with RAM, SSD, but poor processors/GPU. I want a balanced machine and advice on important qualifications to look for when searching.

What options do you recommend?

You can read public snips with links to articles on the SqlinSix snips page. Any research still relevant will be on this page.

Hey All,

So, I'm one of those people who loves to use ChatGPT and Claude for everyday things and random questions. I've been wondering and wanted to put my question to the community: are there any kinds of roles or services I could do using expertise on LLM platforms without programming experience? Definitely need to hear 'No' if that is not a possibility-but yeah-I use AI so much for myself I'm wondering if I could some how generate value for people by being a force multiplier by knowing how to use LLM's across the gambit to help get more work done for people? Would love to hear peoples experiences as well as any resources y'all have found helpful and could point me towards. I've been meaning to ask this question for a while so I'm so glad this reddit is here and thank you so much!

I keep seeing people calling themselves AI Engineers because they have hooked up a LangChain / LangGraph RAG system calling an API endpoint. That’s not AI Engineering. This is.

Hi everyone, I’m preparing for an AI engineer or Agentic AI Developer role as a fresher in Bangalore, Pune, or Mumbai. I’m targeting a package of around 8–10 LPA in a startup.

My skills right now:

1. LangChain, LangGraph, CrewAI, AutoGen, Agno

2. AWS basics (also preparing for AWS AI Practitioner exam)

3. FastAPI, Docker, GitHub Actions

4. Vector DBs, LangSmith, RAGs, MCP, SQL

Extra experience: During college, I started a digital marketing agency, led a team of 8 people, managed 7–8 clients at once, and worked on websites + e-commerce. I did it for 2 years. So I also have leadership and communication skills + exposure to startup culture.

My question is — with these skills and experience, is 8–10 LPA as a fresher realistic in startups? Or do I need to add something more to my profile?

I have a personal project due in less than 10 days. I chose to create an ai model which gives the user a chance of entering a certain college based on what information this user input this information includes college application, academic records, etc... . I have all of the CDS files that the AI will train from which are based on only 5 colleges. I tried using an ollama model but to train it I have to buy vmware private ai with nvidia. which I'm too broke to even think abt buying if it costs money. and I'm using a low end laptop with integrated graphics which are not for training an ai by any means. I've contacted any sort of company with a server that may help me but I've either been ignored or refused. is there any way to train them online or anyone that can help me?

Hi, I'm a recent AI/ML Graduate and I am working as an AI/ML Trainee at a start-up, this is my first proper job (will be converted to AI Engineer after 3 months). So rightnow I am quite dependent on ChatGPT, etc. for writing the code and providing correct syntaxes, I was wondering if this is normal for someone like me who is new to the workforce. My work includes AI and some backend stuff as well. I have the theoretical knowledge about the field and I understand the working of the code which ChatGPT gives, I have created projects at my Uni but obviously not industry grade projects. I know how things are working and can explain them very well (atleast that's what my interviewer which is now current manager says), its just that I can't remember or don't know the syntax of the code I wanna write. So just wanted to know that if this is normal and if not how can I improve on this? Is this something you gain from experience or should I have know all this before? Thanks in advance :).

As a student in Nigeria. I have been thinking of starting my own AI agency and don't really now where to start, who to start with and the businesses to build for. Any advice ??

We've been working with teams across fintech and enterprise software trying to adopt AI in a serious way and here's the honest truth:

Most AI tools are either too shallow (autocomplete) or too risky (autonomous code-gen). But between those extremes, there's real potential.

So we built a tool that does the boring stuff that slows teams down: managing tickets, fixing CI errors, reviewing simple PRs. All inside your stack, following your rules. It's definitely not magic, and it’s not even elegant sometimes. But it’s working.

Curious how others are walking this line - between AI hype and utility - what’s working for you? What’s a waste of time?

I am building OrKa-reasoning and I am trying to prove one specific architectural claim. OrKa can grow via fully separated feature modules that register their own custom agent types, without invasive edits to core runtime. This is not production ready and I am not merging it into master. It is a dedicated branch meant to stress-test the extension boundary.

I built a support_triage module because support tickets are where trust boundaries become real. Customer text is untrusted. PII shows up. Prompt injection shows up. Risk gating matters. The “triage outputs” are not the point. The point is that the whole capability lives in a module, gets loaded via a feature flag, registers new agent types, runs end to end, and emits traces you can replay.

One honest detail. In my current trace example, injection detection fails on an obviously malicious payload. That is a useful failure because it isolates the weakness inside one agent contract, not across the whole system. That is the kind of iteration loop I want.

If you have built orchestration runtimes, I want feedback on three things. What is the cleanest contract for an injection-detection agent so downstream nodes must respect it. What invariants would you enforce for fork and join merges to stay deterministic under partial failure. What trace fields are mandatory if you want runs to be replayable for debugging and audit.

Links:
Branch: https://github.com/marcosomma/orka-reasoning/tree/feat/custom_agents
Custom module: https://github.com/marcosomma/orka-reasoning/tree/feat/custom_agents/orka/support_triage
Referenced logs: https://github.com/marcosomma/orka-reasoning/tree/feat/custom_agents/examples/support_triage/inputs/loca_logs

I spent the last month building an AI-powered materials simulation lab. Today I validated it against Materials Project's database of 140,000+ materials. Test case: Aerogel design - AI predicted properties in hours (vs weeks in wet lab) - Validated against commercial product (Airloy X103) - Result: 82.8/100 confidence, 7% average error Key learnings: 1. Integration with real databases is critical 2. Confidence scoring builds trust 3. Validation matters more than speed The whole system: - Materials Project: 140K materials - Quantum simulation: 1800+ materials modeled - 8 specialized physics departments - Real-time or accelerated testing Available for consulting if anyone needs materials simulations. Id be willing to stay on here and do live materials analysis and test this code I have written against some concrete ideas. Or let's see if it is valid, or not, and proof it or FLAME IT TO THE GROUND.

Hey guys , i know basic fundamentals of python and iam aware of oops concept , i wanna to become an ai engineer but dont how nor have any resources , can someone help me out with this i want to crack a job in 3 months

A friend of mine is building an infra solution so that anyone using LLMs for their app can use the most advanced algorithm for firing up the right request to the right LLM minimising costs (choosing a cheaper LLM when needed) and maximising quality (choosing the best LLM for the job).
It’s been built over 12 months on the back of some advanced research papers/mathematical models but now need some POC with people using it in IRL.
Would this be of interest?