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Manufacturing meets modern web — built this from scratch. 🏭⚙️
Vortex Engineering Works isn't a real company (yet) — it's a dev portfolio project I built to show what a serious B2B industrial site should look like. Think pressure vessels, heat exchangers, and boiler components... presented like the Fortune 500 clients they're chasing actually expect.
What's under the hood:
⚡ Full multi-page site — Home, About, Products & Services, Projects, Contact
⚡ Smooth dynamic image carousels showcasing products
⚡ Clean services/expertise grid + client showcase section
⚡ Stats & certifications section built for instant trust (ASME, IBR, QMS 9001)
⚡ Built with Next.js — fast, optimized, production-grade image delivery
No cookie-cutter templates. No generic "industrial business" clichés. Just a site designed to make a heavy-engineering company look as sharp as the machines they build.
Screen recording below 👇 — full walkthrough of the build.
🔗 Live site: https://vortex-engineering-works.vercel.app/
This is a strong example of translating a traditionally rigid industrial category into a confident digital system. The service grid, certification cues, and product carousel make the B2B story feel both credible and surprisingly approachable.
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My hands are still slightly shaking from posting my first project yesterday, but here is build #2 before I overthink it and talk myself out of it. 😅
Meet StudyAI—an AI study companion I put together.
(Please ignore that my logged-in username is "king"—giving myself royal titles during late-night debugging sessions is my only coping mechanism as a CSE fresher).
What’s inside the update:
• AI flashcards with 3D flip animations
• PDF extraction & instant quiz generation via Gemini API
• Firebase database (which gave me multiple mini heart attacks to set up)
Month 1 of engineering has been a wild learning curve, and sharing my messy beginner progress publicly is pretty terrifying, but I'm having fun building.
Let me know what you think or what I should improve next!
Also, promise I'll change my username from "king" to my actual name... right after I figure out how to survive my first round of college exams. 💀
[Yeh I forget these, and i am sorry]---Live demo: https://flashcard-study-app-d.vercel.app/
GitHub:
flashcard-study-app-d.vercel.app
StudyAI — AI-Powered Flashcards & Quizzes
Master any subject faster. Generate intelligent flashcards and interactive quizzes from your study notes and PDFs.
AI agents are most useful when they reduce uncertainty—not when they silently replace judgment.
The Next.js team just used a research agent to review a backlog of 2,244 GitHub issues. In roughly three weeks, 1,462 issues were closed and only 3 were reopened.
What made the workflow credible:
• every investigation ran in an isolated sandbox
• the agent checked code, versions, PRs, commits, releases, and docs
• it actively searched for evidence against its first conclusion
• risky actions stayed read-only until humans reviewed the evidence
• the clearest cases required a second-agent challenge before automation could close them
That is the pattern I trust for production AI automation: evidence first, structured outputs, bounded permissions, and reversible actions.
Where could an evidence-driven agent remove the most operational drag in your workflow?
Where it removed the most drag for me was real-estate contract deadlines - a chain of dates where one addendum moves a date and nothing throws an error. The agent is not there to judge, it is there to show that something moved.
The second-agent challenge is the bullet I would...
Nobody warns you that your first engineer doesn't make the team faster right away.
Ramp-up and review overhead eat about a quarter of throughput before the hire compounds. Plan for the dip.
