Fatima TOUHTOUH@FatimaTouhtouh
1
Events attended
1
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Morocco
1 year of experience
About me
I'm a Data Science and AI Engineering student passionate about analyzing data, building predictive models, and developing intelligent systems. I love turning data into meaningful insights and using machine learning to solve real-world problems.
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🤓 Latest Submissions
DevForge — Multi-Agent Dev Pipeline
Software teams increasingly want AI to handle routine engineering work, but fully autonomous pipelines are risky: a model can approve its own broken or insecure code with no one checking. DevForge solves this by making human oversight a first-class stage in an otherwise automated pipeline, not an afterthought. DevForge runs a task through six stages : Plan, Build, Test, Debug, Security, and Fix , using a coordinated set of agents, each with a defined role and a real fallback-free implementation for the stages that matter most: Security and Fix. The Security agent scans the codebase for real vulnerabilities (for example a hardcoded secret key and a missing ownership check on a delete endpoint), and the Fix agent proposes and applies an actual patch, not a scripted demo. Every stage's output is logged to an event trail so nothing the pipeline claims to have done is unverifiable. Critically, before anything ships, DevForge stops and asks a human to approve the architecture and the fix. This human-in-the-loop gate is enforced in the orchestrator itself: the pipeline cannot silently sail past it. Only after approval does the project move to Released , at which point the user gets something tangible: a downloadable zip containing the patched source file and an auto-generated README summarizing what each agent found, what was fixed, and how to run it. Target users are small dev teams, solo developers, and hackathon-style rapid-prototyping teams who want AI acceleration on the repetitive parts of the SDLC (planning, testing, security review) without losing the final say on what actually ships. They interact through a single-page dashboard: start a project with an idea, watch each stage run live, review the security findings, click Approve or Reject, and download the result. What makes DevForge distinct is its honesty about what's automated versus simulated, and its insistence that a human, not a model, makes the release decision. Rather than presenting a black-box "AI wrote your app" experience, it shows its work stage by stage and gives the human reviewer real signal actual test counts, actual vulnerability findings, an actual patch diff to approve or reject. That transparency, combined with a genuine deliverable at the end rather than just a log of activity, is what turns DevForge from a demo into something a team could plausibly adopt.
27 Sep 2026
