Ahmad Shuaib@x_drxzx_x
2
Events attended
1
Submissions made
India
2+ years of experience
About me
I am an Electronics and Communication Engineering student from India actively building my career as a Backend and AI Developer with a strong foundation in Java and Spring Boot. I am gaining hands‑on experience in designing REST APIs, solving data structures and algorithms problems, and working on embedded AI/IoT systems while learning Docker, Kubernetes, and cloud‑native tools. I am consistently improving my problem‑solving skills through platforms like "LeetCode" where i got 400k rank overall and "HackerRank" 30k overall, participating in hackathons, and applying best practices such as layered architectures, containerization, and version control to my projects. I am pursuing an ECE degree with a data science specialization, supported by focused courses in machine learning for embedded systems, Python, and DevOps, positioning myself as a technically strong, fast‑learning engineer ready to contribute to modern backend and AI‑driven solutions.
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🤓 Latest Submissions
Argos Desktop
## The Problem Modern developers don't use one AI assistant — they juggle several at once. IBM Bob lives in its IDE, GitHub Copilot lives in VS Code, and terminal agents (Gemini CLI, Claude Code, Codex) live in shells. Each one is powerful alone, but the work *between* them is entirely manual: - **Onboarding is repeated.** Every agent must be told the project layout, conventions, and current task separately. A new repository can take hours of back-and-forth before any agent is productive. - **Parallel work is copy-pasted.** Splitting a feature into "Bob writes the HTML, Copilot writes the CSS, the terminal agent writes the docs" means a developer manually writes three prompts, watches three windows, and reconciles three answers by hand. - **Status is invisible.** Once a task is handed to an agent, there is no single place to see who is done, who is still thinking, and who silently failed — the developer polls each window. - **Results never come back.** Prompts go out; answers stay trapped in each tool's chat panel. Nothing flows back into one report. - **Release hygiene is forgotten.** API keys and credentials leak into commits because scanning before a push is yet another manual step. The net effect: the developer becomes a human message bus between AI tools — constant context-switching, duplicated instructions, missed failures, and rework.
27 Sep 2026
