This one is for all the United fans and @MarcusRashford lovers. Created a dashboard showing our star boy’s stat so far in the 22/23 campaign. 🔥🔥🔥. Let me know your thoughts and which United player I should do next!! Design was done by @daniel____aj

Got bribed at work today. Can’t say I hate it

I bought a yoga mat (pure impulse buy) Never beating the allegations

I worked on Data Engineering, Data Analytics, ML Engineering, MLOps, Agentic AI, and Frontend in the last 2 months. Here’s what I learned in each area: 1. Data Engineering: - Most important and evergreen role as data is the new crude oil. - It’s more about designing orchestration flows than tools. - Understand OLAP vs OLTP: it simplifies everything. - Cover edge cases before optimizing. - Data pipelines are hardest to debug, failures can take hours to surface. - Batching and sharding are core principles. - Vibe-coding works for syntax but you need deep pipeline knowledge. 2. Data Analytics: - Use Polars instead of Pandas. - Check nulls, skewness, outliers, value counts, basic stats. - Segment data to show business behavior across groups. - Use AI heavily to write code and create plots. - Feed plots and stats to AI to generate reports. - Automation becomes very easy with AI. 3. Machine Learning: - Feature engineering is the most important part. - Build models from a business perspective, not just ML metrics (which can be improved later). - Start with simple models; if performance is decent, move to production. - Monitor training closely. - Automate inference logic and FastAPI endpoints with AI. 4. MLOps: - More about system design and business/UI needs than tools. - Docker, FastAPI, MLflow, and Redis are mandatory. - AI writes modular code well but can miss loop logic and focus on edge cases like in data engineering. - Kubernetes and AWS take real learning; vibe-coding confuses debugging. - Terraform is your friend for shipping entire ML systems to any cloud, learn it now. 5. Agentic AI: - Prefer orchestration tools like LangGraph and CrewAI. - Use LangChain only for sub-modules. - One vector DB, one LLM, and one embedding model are enough for any prototype. - System design is critical you can’t build good agents without understanding UI and technical flow. - Observability is essential to evaluate agent outputs. - Coding is easy with AI. 6. Frontend: - Just use AI. It’s already dead otherwise. I’m planning my next big project on distributed LLMs. Stay tuned! You’ll love it.

Wish you could see your entire ChatGPT history in one place? I built a small tool for a problem I kept hitting with ChatGPT. I brain-dump ideas there, outline projects, and do research. Then months later I want to find that one conversation… and search does not help. So I made ChatGPT Data Viewer. What it does: ✅ Loads your ChatGPT data export ✅ Builds a local index ✅ Lets you browse history via a GitHub-like contribution calendar ✅ Fast full-text search across all conversations Everything runs locally. No uploading your chats anywhere. Preview video 👇 I wrote about the tool here: alexeyondata.substack.com/p/…