The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Completed coursework for DataTalksClub's LLM Zoomcamp 2026, covering the core building blocks of production RAG/LLM systems: Agentic RAG (converting OpenAI-based notebooks to Groq API), Vector Search with sentence-transformers and minsearch, Orchestration with Kestra (workflow automation in a containerized environment), and Retrieval/LLM Evaluation using Hit Rate and MRR metrics across text, vector, and hybrid search approaches. Debugged real-world integration issues including API compatibility, deprecated model migrations, and credential rotation.
GitHub :https://github.com/BimbsyStats/llm-zoomcamp-2026
https://github.com/BimbsyStats/llm-zoomcamp-hw5
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Building AI for healthcare leaves zero room for error.
I’m currently collaborating with an incredible team on Raphald AI, a medical detection application. Building the systems for a project with stakes this high is a massive reminder that the underlying backend architecture matters just as much as the machine learning model itself.
When integrating diagnostic AI, your API endpoints cannot drop requests, and your database workflows demand absolute integrity. You aren't just passing JSON payloads; you are handling critical, real-time workflows where stability is non-negotiable.
Engineering these systems continues to shape my approach to building robust Python backends. If you are developing a product that requires reliable AI integration or rock-solid FastAPI infrastructure, check out the newly updated services on my profile. Let's build something that works when it counts.
Messy Data In. Clearer Decisions Out. 📊
Over the past year, I've noticed most businesses don't struggle because they lack data — they struggle because that data never turns into a decision.
That's the gap I work in:
→ Cleaning and analyze data with Python & R
→ Turning findings into investor-ready pitch decks
→ Polishing the final write-up so nothing gets lost in translation
If your team has data sitting in spreadsheets with no story attached, or a pitch deck that needs to actually land — that's exactly where I can help.
Open to projects. Let's talk 👇
I built this face detection app to run machine learning models directly in the browser inside a clean interactive UI.
Users can upload any photo and get an instant scan. The system draws targeting brackets over the face, maps 68 facial points, estimates age and gender, and shows percentage bars for seven basic emotions. If a photo has more than one person, the app counts them and lets you click through each face to update the readings on screen.
Everything runs on the client side using JavaScript and HTML5. The photos stay on the user's computer and never get uploaded to an external server. The model detects the face position, facial points, age, and emotion expression in one pass. If someone uploads a photo without a visible face or any pics of living creatures other than humans, the app handles the error and updates the status message instead of freezing.
The ragebaiting part was to format and structure out the data so it felt clean and fast. Putting the emotion stats beside the face box makes it easy to read the numbers while looking at the person. No distractions, no drama... Showing the total face count in the corner also helps users know if the model missed anyone in the photo.😊
Looks very futuristic💯