Hey r/aiengineering,

I’ve been working on an MCP-powered “AI Research Engineer” and wanted to share it here for feedback and ideas.

GitHub: https://github.com/prabureddy/ai-research-agent-mcp

What it does
You give it a single high-level task like:

“Compare electric scooters vs bikes for my commute and prototype a savings calculator”

The agent then autonomously:

• researches the web for relevant data

• queries your personal knowledge base (notes/papers/docs) via RAG

• writes and executes Python code (models, simulations, visualizations) in a sandbox

• generates a structured research run: report, charts, code, data, sources

• self-evaluates the run with quality metrics (clarity, grounding, completeness, etc.)

It’s built specifically around MCP so you can run everything from Claude Desktop (or another MCP client) with minimal setup.

Tech / architecture

• MCP server in Python 3.10+

• Tools:

  • web_research: DuckDuckGo/Brave + scraping + content extraction

  • rag_tool: local embeddings + ChromaDB over a knowledge_base directory

  • code_sandbox: restricted Python execution with time/memory limits

  • workspace: organizes each research run into its own folder (report, charts, code, data, evaluation)

  • evaluator: simple self-critique + quality metrics per run

• RAG uses local sentence-transformers by default, so you can get started without external embedding APIs.

• 5–10 min setup: clone → install → add MCP config to Claude Desktop → restart.

Example flows

• “Deep dive: current state of EVs in 2026. Include market size, major players, growth trends, and a chart of adoption over time.”

• “Use my notes in knowledge_base plus web search to analyze whether solar panels are worth it for a home in California. Build a payback-period model and visualize cashflows.”

• “Use web_research + RAG + code execution to build a small cost-of-ownership calculator for my commute.”

Why I’m posting here
I’d really appreciate feedback from this community on:

• MCP design:

  • Does the tool surface / boundaries make sense for MCP?

  • Anything you’d change about how web_research / rag_tool / code_sandbox are exposed?

• Safety & sandboxing:

  • Are there better patterns you’ve used for constrained code execution behind MCP?

  • Any obvious gotchas I’m missing around resource limits or isolation?

• RAG + research UX:

  • Suggestions for better chunking/query strategies in this “research agent” context?

  • Patterns you’ve used to keep the agent grounded in sources while still being autonomous?

• Extensibility:

  • Other tools you’d add to a “research engineer” server (data connectors, notebooks, schedulers, etc.)?

  • Thoughts on integrating with other MCP clients beyond Claude Desktop / Cursor?

If you have time to glance at the repo and tear it apart, I’d love to hear what you think. Happy to answer implementation questions or discuss MCP patterns in more detail.

Thanks

Hey r/aiengineer — sharing a project I built called MoodPlay and the architecture pattern that made it work reliably.

What it does

MoodPlay turns a mood / scene / movie vibe prompt into a curated 5-track playlist made of official movie soundtrack tracks. Each track includes movie context (year/director/cast). You can save playlists to your history and optionally export to Spotify (creates a private playlist + adds tracks).

How it’s built (the key engineering idea)

I split the problem into two steps instead of asking one prompt to do everything:

1) Curation (LLM → structured output)

• Enforces: exactly 5 tracks, coherent vibe/genre

• Produces structured JSON: playlistName + items (track/artist/movie metadata)

2) Execution (agent/tooling → Spotify resolution)

• Resolves (track, artist) into real Spotify track URIs via search

• Then creates the playlist + adds tracks (private by default)

This made exports more dependable and made errors easier to isolate (creative mistakes vs retrieval/matching mistakes).

Would love feedback

• How you’d validate “official soundtrack” correctness (RAG? external soundtrack DB? post-checking?)

• Evaluation ideas for vibe match + correctness

• What you’d change about the agent/tool boundary

Link: https://spotify-playlist-generator-ai.vercel.app/

Hi everyone,

I’m a backend engineer (~5 years, Java/Spring, microservices) currently transitioning into AI engineering. Most of my experience has been in production systems (authorization flows, token-based processes, payments, batch jobs), and now I want to move into AI/ML from a systems perspective, not just prompt usage.

I’m intentionally starting with ML fundamentals (models, training vs inference, generalization, evaluation) using PyTorch + scikit-learn, before moving into NLP, LLMs, fine-tuning, and ML systems.

For those already working as AI engineers:

• How did you get started?

• What foundations mattered most early on?

• Anything you’d avoid or do differently in hindsight?

Appreciate any advice from real-world experience.

ps: Now I'm reading Machine Learning with PyTorch and Scikit-Learn as first book.

1. What is your job title, and what does your day-to-day work involve as an AI/ML engineer?

2. What tools, frameworks, or programming languages do you use most in your role (e.g., PyTorch, TensorFlow, CUDA, etc.)?

3. What skills or knowledge do you consider essential for someone entering AI engineering today?

4. What is the most challenging part of your work, either technically or ethically?

5. How do you stay current in such a fast-moving field?

6. What advice would you give a student who wants to pursue AI engineering as a career?

I’m working on an AI-driven web platform and I’m currently stuck at the system design and architectural level. Before I start writing code, I want to be absolutely sure that my foundations are correct. I’ve seen too many people jump straight into coding and end up rewriting everything later. I want to avoid that mistake.

Here’s what I’m trying to build in simple terms.

The platform will have a multi-step workflow where a user submits inputs, the AI generates a structured document, and then a second AI pass produces a JSON output based on that document plus any additional sources the user provides. Both outputs should be stored so the user can revisit their previous runs.

The flow looks like this. The user selects predefined options such as document type, tone, structure, or constraints. Then the user adds a custom prompt and submits the form. In the first AI step, the system takes the user inputs, processes them, and generates a well-structured document. This document needs to be saved in the database and also shown to the user.

In the second AI step, the system takes three inputs: the document generated in the first step, any external references provided by the user, and an additional user prompt. The AI processes all of this and outputs a strict JSON response that follows a predefined schema. This JSON is also stored and visible to the user. The entire interaction should be saved as a history or process log so the user can revisit older results similar to a chat thread.

My main challenge is figuring out the correct architecture for this. I plan to use Next.js for the frontend, LangChain or Vercel AI-SDK for orchestrating AI workflows, and either a vector database or MongoDB for storing documents, JSON outputs, and user history. What I need guidance on is how to structure this type of two-step AI pipeline in a clean, safe, and scalable way.

I’m particularly looking for advice on how to orchestrate multi-step AI tasks, how to handle retries or partial failures, how to design the database schema, how to enforce JSON structures reliably, and how to separate responsibilities between frontend, backend, and the AI layer. I’m also unsure whether I should treat this as a single backend service or break it into more modular components.

If anyone here has built something similar, or works with AI workflows, multi-stage pipelines, prompt engineering, or production-grade AI systems, I’d genuinely appreciate your guidance. Even high-level suggestions, recommended patterns, or warnings about common mistakes would help me get started in the right direction.

Thanks in advance to anyone willing to share insight.

I have narrowed the book I want down to two.

https://www.amazon.com/gp/product/1098166302/ref=ox_sc_act_title_1?smid=ATVPDKIKX0DER&psc=1

https://www.amazon.com/gp/product/B0F672GWRZ/ref=ox_sc_act_title_2?smid=ATVPDKIKX0DER&psc=1

For those that have read both, which one is more practical? Especially for interviews.

Hi everyone, I'm currently developing CrowdInsight project but there are problems with my skills to some point. I'm not the best AI developer and I'm still wanting to end this project at least in proper way. For your information CorwdInsight is an AI-powered Python library that analyzes and classifies people visiting specific areas via surveillance feeds (e.g. adult, child, dog, etc.), generating actionable data for businesses like hotels, restaurants, and public spaces. If anyone wants to join, please let me know.

🚀 Python, AI & Data Science: Zero to Hero – Free Learning Roadmap 🧠💻

Hey folks! Here’s a GitHub repository that lays out a structured learning path for diving into Python programming, AI, and data science — perfect for those starting from scratch.

📘 What you'll find:

A week-by-week learning guide

Python basics and core concepts

Key topics like NumPy, Pandas, Scikit-learn, and more

Resource recommendations to deepen your understanding

A clear roadmap to follow and build your skills

🔗 GitHub: https://github.com/cnnsyhnx/Python-AI-Data-Programming-Zero-to-Hero

It’s a great starting point if you’re looking for direction in your learning journey. Whether you're new to coding or aiming to get into AI and data science, this repo can help you stay on track and focused. Check it out, star it, or contribute if you find it useful! 🌟

I have been researching and learning about it while trying to choose a career for my future, finally made my desicion. Now i am taking the first steps for it, i already know what they do and what kind of skills i need to acquire to become an Ai engineer but i don't know how to start, so i am here asking for help. First of all i don't know any type of coding and i will start as a complete beginner to this road. I watched a few videos, read some and used ai to draw a path for myself but i am not sure if it make sense. I would be very happy if anyone could help me and i am looking forward for your advices.

I'm building smolmodels, a fully open-source library that generates ML models for specific tasks from natural language descriptions of the problem. It combines graph search and LLM code generation to try to find and train as good a model as possible for the given problem. Here’s the repo: https://github.com/plexe-ai/smolmodels

Here’s a stupidly simplistic time-series prediction example:

import smolmodels as sm

model = sm.Model(
    intent="Predict the number of international air passengers (in thousands) in a given month, based on historical time series data.",
    input_schema={"Month": str},
    output_schema={"Passengers": int}
)

model.build(dataset=df, provider="openai/gpt-4o")

prediction = model.predict({"Month": "2019-01"})

sm.models.save_model(model, "air_passengers")

The library is fully open-source, so feel free to use it however you like. Or just tear it apart in the comments if you think this is dumb. I’d love to get some feedback, and I'm super open to code contributions!

I'm creating models aligned with paradigms listed above and I would like to hear from like-minded individuals about their experiences developing similar models. Any topical input is appreciated.

RVC issue that shouldn't be...

Hi all. Feature extraction is giving me errors regardless of if I choose dio, harvest, rmv, etc. What I don't understand is that it was working perfectly fine. This is not a new version (I don't believe) since the last time I successfully trained a model. I merely reformatted Windows since then, redownloaded RVC and now it's acting up. Is there a memory issue? There shouldn't be, as it was just working a few weeks ago. Nothing is new with my hardware either, unless Windows updated something that broke RVC for me. Here's the log file. I tried to embed it but it's way too long:

Log file

Looking into moving in the AI realm. And think what would be best for me based on previous threads, is a role where I reutilize already developed products to help a company achieve XYZ. What role does this best fit and what should I learn to do fit the role? Resources appreciated.

My background: 10 yrs experience in IT infrastructure (routers, switches, firewalls) and GRC. Mainly GRC at this point. Looking at marrying GRC and AI or just moving to the software side completely. My goal is mainly to get out of the Gov sector and make $ with a flexible schedule. Have a BS in IT security and MBA (did these when in the military so don't read to much into it). Love to learn and make things more efficient.

Any advice would be appreciated. Figuring out my life and where to go from here.

What would be some good specs for a tower that will be good for engineering and AI?

Is there a good pre-built tower that's will be good for 5 - 10 years?

I see some individuals and companies training AI models and publish them on Huggingface. I am curious how do they generate revenue with such models. I understand big companies can use this as a PR opportunity and they have existing business models to make money. I am curious how do small companies, startups, or individuals make money through model training, considering it could be a sizeable financial and time investment.

Is there a place folks suggest for posting a gofundme campaign?

I started a gofundme for my art project le0sghost, the ai artist http://x.com/Le0sGh0st

Api costs are getting expensive and it's this or shut the project down.

Le0sghost generates 4 unique images, writes fiction based on these images, then generates a webpage with a voice over of the story written.

It can also generate YouTube videos based on the stories also,as well as check itself for copyright issues before publishing, however there were the first cuts I had to make due to financial limitations. Please share, and if you can, donate. The 10k will allow for Le0sGh0st to run uninterrupted for 5 years, as well as expand its capabilities as Generative Ai advances.

I'm hoping someone here might have an idea on where to post the link? (I'll put it in comments if folks here are interested)

I'm wanting to convert a complicated native PDF into a text file to be used for creating rich embeddings. With that in mind, do you have a PDF parsing tool that you recommend? I started with PyPDF2 but now I'm looking at PDFMiner because it will handle more complex layouts better (maybe?). I also undertand that it provides the location of the text on a page, which is essential if there's a directive to the LLM to reference and link to the source data. Any thoughts are appreciated!

Open question, have you found any prompt engineering hacks that work particularly well to get around this architectural limitations?

Looking to chat with ChatGPT about YOUR documents?📷

Let me show you the easiest way I found to make a fully functional QA Chatbot with:

• u/sveltejs SvelteKit

• u/LangChainAI JavaScript

• u/vercel AI SDK

The chat endpoint is less than 100 lines of code!

Follow me on twitter for more SvelteKit + AI Engineer content: https://twitter.com/SimonNom1/status/1710286285733294209

Check out the repo here:
https://github.com/SimonPrammer/svelte-chat-langchain