Abdul Rauf Qasim@abdulraufqasim21
1
Events attended
1
Submissions made
Pakistan
1 year of experience
About me
I’m Abdul Rauf Qasim, a Data Science student passionate about technology, software development, and AI. Alongside my studies, I’m developing my skills in Front-End Development, working with HTML, CSS, JavaScript, and modern web technologies. I enjoy turning ideas into practical projects, exploring AI-powered tools, participating in hackathons, and continuously learning new technologies. My goal is to combine Data Science, AI, and Web Development to build useful and innovative solutions. Currently learning, building, and turning ideas into projects. 🚀
🤝 Top Collaborators
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🤓 Latest Submissions
Driftwatch
Every software project starts with a clean, intentional architecture: clear rules about which layers can talk to which, how services connect, and where boundaries exist. But as teams move fast under deadline pressure, small shortcuts creep in — a UI component quietly calling a database directly, a service bypassing its intended gateway. No one notices, because catching this kind of "architectural drift" requires holding an entire codebase in view at once, something no human reviewer, linter, or single-file AI assistant can realistically do. The cost compounds silently. New developers struggle to understand a system that no longer matches its own documentation. Every change carries a hidden, unknown blast radius, since nobody can see everything it might break. Small violations pile up into large-scale technical debt that takes weeks to unwind. By the time the problem is visible, it is already expensive to fix. Driftwatch is our answer to this problem. It is an AI-powered architecture guardian — not another code generator or line-level reviewer, but a tool that watches the structural health of a codebase over time. Powered by IBM Bob 2.0's full-repository context, Driftwatch builds a ground-truth map of a project's intended architecture, then continuously checks new commits against it. When a rule is broken — say, a UI layer calling a database directly instead of going through an API — Driftwatch flags it immediately with a plain-English explanation of what broke and why it matters. Beyond detection, Driftwatch's "Blast Radius" feature traces the downstream impact of any proposed change across the entire codebase, so a developer knows exactly what else might be affected before merging. Its Architecture Map auto-generates and stays perpetually in sync with the real codebase, replacing static, stale diagrams that no one has time to update by hand. A single Health Score, tracked per commit, gives teams an at-a-glance read on whether their architecture is improving or degrading over time. Our target users are engineering teams and technical leads responsible for maintaining codebase quality at scale — particularly teams onboarding new developers, managing microservice boundaries, or inheriting legacy systems where architectural intent has been lost over time. They interact with Driftwatch through a simple dashboard: connect a repository, run a scan, and immediately see a live architecture graph, a timeline of drift across recent commits, and a prioritized list of violations to address. What makes Driftwatch original is its category. Nearly every AI developer tool on the market today reasons about one file at a time — generating code, reviewing a diff, answering a question about a snippet. Driftwatch instead uses Bob 2.0's whole-repository reasoning to solve a structural, systemic problem that is invisible at the single-file level. It doesn't write code for you; it protects the shape of the code you already have. In a hackathon full of AI code generators and PR-review bots, Driftwatch demonstrates a fundamentally different, and we believe more durable, application of what full-context AI reasoning makes possible.
27 Sep 2026
