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    <title>Production AI Insights — Qalab Hassnain Agha</title>
    <link>https://www.qalabagha.com/blog</link>
    <description>Technical articles on production AI engineering, computer vision, LLM pipelines, RAG architecture, and MLOps by Qalab Hassnain Agha, CTO at Quickgen Technologies.</description>
    <language>en-US</language>
    <managingEditor>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</managingEditor>
    <webMaster>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</webMaster>
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      <title>Production AI Insights — Qalab Hassnain Agha</title>
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      <title><![CDATA[YOLOv8 in Production: Building a Multi-Camera CCTV Anomaly Detection System]]></title>
      <link>https://www.qalabagha.com/blog/yolov8-production-multicamera-cctv-anomaly-detection</link>
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      <description><![CDATA[YOLOv8 benchmarks are well documented. What's not documented is what happens when you process 8 simultaneous CCTV feeds in real time, apply zone-based business rules, and deliver WebSocket alerts under 200ms while keeping false positives low enough that security staff actually trust the system.]]></description>
      <pubDate>Thu, 14 Aug 2025 00:00:00 GMT</pubDate>
      <lastBuildDate>Wed, 20 Aug 2025 00:00:00 GMT</lastBuildDate>
      <category><![CDATA[Computer Vision]]></category>
      <category><![CDATA[YOLOv8]]></category>
      <category><![CDATA[Object Detection]]></category>
      <category><![CDATA[Real-Time Systems]]></category>
      <category><![CDATA[Production AI]]></category>
      <author>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</author>
      <dc:creator><![CDATA[Qalab Hassnain Agha]]></dc:creator>
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      <title><![CDATA[Monolith to Microservices: How We Achieved 3x Throughput on a Live Production System]]></title>
      <link>https://www.qalabagha.com/blog/monolith-to-microservices-3x-throughput</link>
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      <description><![CDATA[Most microservices migrations are driven by architectural fashion rather than specific engineering pain. Ours was driven by a measurable scaling problem. This is the story of migrating a live platform without downtime, what broke in ways we didn't anticipate, and what 3x throughput actually looks like.]]></description>
      <pubDate>Tue, 08 Jul 2025 00:00:00 GMT</pubDate>
      <lastBuildDate>Tue, 15 Jul 2025 00:00:00 GMT</lastBuildDate>
      <category><![CDATA[System Architecture]]></category>
      <category><![CDATA[Microservices]]></category>
      <category><![CDATA[Backend Engineering]]></category>
      <category><![CDATA[Production Migration]]></category>
      <author>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</author>
      <dc:creator><![CDATA[Qalab Hassnain Agha]]></dc:creator>
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      <title><![CDATA[Model Quantization for Production: How I Cut Inference Cost by 60% Without Touching Accuracy]]></title>
      <link>https://www.qalabagha.com/blog/model-quantization-production-60-percent-cost-reduction</link>
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      <description><![CDATA[Your production AI model is probably 4x bigger than it needs to be. I reduced inference time from 340ms to 91ms and cut monthly cloud costs by 60% using INT8 quantization — without changing a single model layer. Here's the full pipeline.]]></description>
      <pubDate>Fri, 20 Jun 2025 00:00:00 GMT</pubDate>
      <lastBuildDate>Tue, 01 Jul 2025 00:00:00 GMT</lastBuildDate>
      <category><![CDATA[Model Optimization]]></category>
      <category><![CDATA[ONNX]]></category>
      <category><![CDATA[INT8]]></category>
      <category><![CDATA[MLOps]]></category>
      <category><![CDATA[Cost Optimization]]></category>
      <author>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</author>
      <dc:creator><![CDATA[Qalab Hassnain Agha]]></dc:creator>
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      <title><![CDATA[RAG Architecture in Production: Building a Research Intelligence System with ChromaDB and BM25]]></title>
      <link>https://www.qalabagha.com/blog/rag-architecture-production-chromadb-bm25</link>
      <guid isPermaLink="true">https://www.qalabagha.com/blog/rag-architecture-production-chromadb-bm25</guid>
      <description><![CDATA[Production RAG fails in specific ways the tutorials skip. I built PaperIntel — a research intelligence system with citation-level accuracy — using hybrid retrieval, cross-encoder reranking, and systematic evaluation. This is what the full architecture actually looks like.]]></description>
      <pubDate>Tue, 03 Jun 2025 00:00:00 GMT</pubDate>
      <lastBuildDate>Tue, 10 Jun 2025 00:00:00 GMT</lastBuildDate>
      <category><![CDATA[RAG]]></category>
      <category><![CDATA[LLMs]]></category>
      <category><![CDATA[ChromaDB]]></category>
      <category><![CDATA[BM25]]></category>
      <category><![CDATA[Hybrid Retrieval]]></category>
      <category><![CDATA[Production AI]]></category>
      <author>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</author>
      <dc:creator><![CDATA[Qalab Hassnain Agha]]></dc:creator>
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    <item>
      <title><![CDATA[My Production Deployment Checklist for AI Systems: What I Check Before Every Launch]]></title>
      <link>https://www.qalabagha.com/blog/production-ai-deployment-checklist</link>
      <guid isPermaLink="true">https://www.qalabagha.com/blog/production-ai-deployment-checklist</guid>
      <description><![CDATA[Every item on this checklist exists because I once shipped without it. Seven layers — crash reporting, analytics, UX feedback, bug tracking, infrastructure monitoring, device fingerprinting, and CDN — that I now run before any AI system goes live.]]></description>
      <pubDate>Sat, 10 May 2025 00:00:00 GMT</pubDate>
      <lastBuildDate>Sun, 01 Jun 2025 00:00:00 GMT</lastBuildDate>
      <category><![CDATA[Production AI]]></category>
      <category><![CDATA[DevOps]]></category>
      <category><![CDATA[MLOps]]></category>
      <category><![CDATA[Monitoring]]></category>
      <category><![CDATA[IoT]]></category>
      <author>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</author>
      <dc:creator><![CDATA[Qalab Hassnain Agha]]></dc:creator>
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      <title><![CDATA[LLM-Powered Real-Time Audio Pipelines: How We Built AI Transcription at Scale]]></title>
      <link>https://www.qalabagha.com/blog/llm-realtime-audio-pipeline-at-scale</link>
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      <description><![CDATA[Most developers think the hard part of voice AI is the speech-to-text model. It isn't. The hard part is everything around it — the audio ingestion pipeline, the LLM classification layer, the WebSocket architecture, and the operational infrastructure that keeps it all running under production load.]]></description>
      <pubDate>Tue, 22 Apr 2025 00:00:00 GMT</pubDate>
      <lastBuildDate>Sun, 01 Jun 2025 00:00:00 GMT</lastBuildDate>
      <category><![CDATA[Real-Time Audio]]></category>
      <category><![CDATA[LLMs]]></category>
      <category><![CDATA[Speech-to-Text]]></category>
      <category><![CDATA[WebSockets]]></category>
      <category><![CDATA[Production AI]]></category>
      <author>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</author>
      <dc:creator><![CDATA[Qalab Hassnain Agha]]></dc:creator>
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      <title><![CDATA[Building Real-Time IoT Systems with BLE and WebSockets: Lessons from 200Hz+ Sensor Streaming]]></title>
      <link>https://www.qalabagha.com/blog/realtime-iot-ble-websockets-200hz</link>
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      <description><![CDATA[The hardest part of building wearable tech isn't the AI. It's the 200 milliseconds between the sensor and the screen. Four years of lessons from production IoT systems — BLE reconnection, protocol selection, edge preprocessing, and monitoring.]]></description>
      <pubDate>Mon, 10 Mar 2025 00:00:00 GMT</pubDate>
      <lastBuildDate>Sun, 01 Jun 2025 00:00:00 GMT</lastBuildDate>
      <category><![CDATA[IoT]]></category>
      <category><![CDATA[BLE]]></category>
      <category><![CDATA[WebSockets]]></category>
      <category><![CDATA[Real-Time Systems]]></category>
      <category><![CDATA[Wearable Tech]]></category>
      <category><![CDATA[Edge AI]]></category>
      <author>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</author>
      <dc:creator><![CDATA[Qalab Hassnain Agha]]></dc:creator>
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    <item>
      <title><![CDATA[How to Deploy a Computer Vision Model to Production]]></title>
      <link>https://www.qalabagha.com/blog/deploy-computer-vision-model-production</link>
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      <description><![CDATA[Most CV tutorials end at model training. This guide covers every layer I put in place before any vision model goes live — API design, containerisation, versioning, monitoring, and cost optimisation.]]></description>
      <pubDate>Wed, 15 Jan 2025 00:00:00 GMT</pubDate>
      <lastBuildDate>Sun, 01 Jun 2025 00:00:00 GMT</lastBuildDate>
      <category><![CDATA[Computer Vision]]></category>
      <category><![CDATA[FastAPI]]></category>
      <category><![CDATA[ONNX]]></category>
      <category><![CDATA[Docker]]></category>
      <category><![CDATA[MLOps]]></category>
      <category><![CDATA[Production AI]]></category>
      <author>aghaqalabhassnain@gmail.com (Qalab Hassnain Agha)</author>
      <dc:creator><![CDATA[Qalab Hassnain Agha]]></dc:creator>
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