[SYSTEM_IDENTITY]
NAME = Ilhan Mansij Jidda
CALLSIGN = Cheese-Cheese
ROLE = Software Engineer // AI & Distributed Systems
[CORE_DIRECTIVES]
> Full-Stack & Systems Architecture: Architecting production backends with FastAPI, Flask, and Next.js.
> Real-Time Voice AI: Engineering multi-agent spoken pipelines using LiveKit Agents, Deepgram Nova-3, and Murf Falcon TTS.
> Computer Vision & Multimodal: Building U-Net tissue segmentation, MobileNetV2 classifiers, and Gemini/LLaMa/GPT-OSS multimodal reasoning engines.
> Hackathon-Tested: Engineered clinical diagnostics (MIT Hackathon) and bilingual voice agents (Voice for Bharat).
> Engineering Standard: Finite State Machine (FSM) control flow, strict output validation, and zero hallucinations.LANGUAGES |
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AI / LLM & CV |
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BACKEND & WEB |
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DATABASES |
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DEVOPS & TOOLS |
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Smart Platform for Adversarial Readiness & Technical Assessment. Compares resumes against job descriptions using Groq-hosted LLMs, PyMuPDF extraction, and NVIDIA Nemotron OCR fallback for scanned pages. Key Architecture: Real-time voice interview engine (Deepgram STT) with Finite State Machine (FSM) orchestration for predictable dataflow, repository coverage reporting, and offline test suites. [SOURCE_REPO]
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[LIVE_DEMO]
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Zero-runtime-dependency GitHub Action dynamically generating customizable, animated CRT television SVGs with cycling channels for live system stats, language breakdowns, repository spotlight, and activity radar. Features a live interactive studio for real-time theme customization. Key Architecture: TypeScript/Node.js rendering engine bundling pure SVG output with embedded VT323 pixel typography, CSS @keyframes phosphor scanlines/noise textures, and GitHub GraphQL API integration (GitHub Camo compatible). [SOURCE_REPO]
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[LIVE_PREVIEW]
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[MARKETPLACE]
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Engineered a multi-agent spoken English tutor across Murf Falcon streaming TTS, Deepgram Nova-3 STT, and Gemini 2.5 on LiveKit Agents, backed by Next.js and outbound SIP telephony. Key Architecture: Integrated live dictionary & grammar REST APIs as LLM tools with spoken fail-soft fallbacks, human teacher escalation dashboards, and a 22-test pytest suite. [SOURCE_REPO]
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[TECH_ARTICLE]
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Dual-model computer vision pipeline combining U-Net semantic segmentation and MobileNetV2 tissue classification with reference-patch calibration converting pixel measurements to real wound surface area (cm²). Key Architecture: Flask REST API backend with Streamlit multi-role clinical dashboard (Doctor/Patient portals) and automated PDF diagnostic reporting. [SOURCE_REPO]
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> TELEMETRY_BROADCAST: Like this CRT telemetry monitor? Customize & deploy your unit •
[LIVE_STUDIO] • [DEPLOY_CATHODERAYSTATS]
