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Mohammed Arhaan Hussein@Sunnyhussain
1
Events attended
1
Submissions made
India
2+ years of experience
About me
I am an engineering student (BTCSE) with a specialization in AIML and robotics, with keen interest in AI agents, agentic AI, and generative AI. I am also fond of web dev and cybersecurity. Currently I am exploring competitive programming at codeforces and leetcode.
🤓 Latest Submissions
Multiagent coding assistant
BOB / FORGE is a multi-agent coding assistant designed to make software development safer, faster, and more reliable. Many developers, students, and small teams can generate code quickly, but first drafts often contain hidden bugs, incorrect assumptions, weak input validation, or untested edge cases. Reviewing and testing every generated solution manually is time-consuming, especially for beginners and teams working under tight deadlines. Our solution uses a LangGraph-powered workflow that behaves like an autonomous engineering team. The user enters a natural-language programming request through the web interface. The Builder agent creates an implementation and test contract. The Reviewer agent analyzes the code for logical, structural, and security issues. The Runner executes the tests inside a restricted sandbox, preferably using Docker with network isolation and resource limits. If problems are detected, the Fixer agent applies a targeted correction. The workflow then loops through review and testing until the solution passes or the iteration limit is reached. BOB / FORGE is intended for students, developers, educators, hackathon teams, and anyone who wants an additional layer of confidence before using generated code. Users interact with a simple engineering studio interface that displays the generated artifact, review findings, test output, agent telemetry, iteration count, and final readiness status. What makes the project unique is its “code that argues with itself” approach. Instead of relying on a single code-generation response, the system creates constructive disagreement between specialized agents. It also includes BobRiskNet, a lightweight neural bug-risk model trained on synthetic code-defect patterns. This model provides an additional local risk signal even when no external AI model is configured. By combining generation, adversarial review, sandboxed execution, automated repair, and transparent evidence, BOB / FORGE reduces the chance that users will blindly trust flawed code. It turns code generation from a one-step output into an observable, testable, and repeatable engineering process.
27 Sep 2026
