{"slug": "codeniva-ai-quiet-queue-a-privacy-first-ai-assistant-that-sorts-tasks-so-you-don", "title": "CODENIVA AI Quiet Queue: A Privacy-First AI Assistant That Sorts Tasks So You Don't Have To", "summary": "A developer built CODENIVA AI — Quiet Queue, an open-source, privacy-first task assistant that sorts messages into deadlines, replies, reviews and lower-priority items and exports a prioritized daily brief as Markdown or JSON. The prototype uses a locally trained scikit-learn TF-IDF and logistic regression classifier for short English and Arabic messages, plus cosine similarity for duplicate detection, and runs entirely in Python without paid AI APIs or cloud inference. All six automated tests passed on synthetic sample data, though the developer notes the small training set limits accuracy and real-world time savings have not been independently measured.", "body_md": "This is a submission for the \"Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady\" ([https://dev.to/challenges/hacktoberfest-week3-2026-10-19](https://dev.to/challenges/hacktoberfest-week3-2026-10-19)).\n\nWhat I Built\n\nI built CODENIVA AI — Quiet Queue, a privacy-first, open-source AI productivity assistant that transforms a messy collection of messages and tasks into a clear, prioritized daily action brief.\n\nThe goal is simple: spend less time sorting information and more time getting things done.\n\nQuiet Queue classifies tasks into deadlines, replies, reviews, and lower-priority items. It highlights urgent deadlines, detects likely duplicates, and generates a structured summary.\n\nCreated during Hacktoberfest Week 3 in October 2026, this is a standalone experimental prototype under the broader CODENIVA AI vision.\n\nDemo\n\n\"Watch the Quiet Queue demonstration\" ([https://github.com/CODENIVA-AI/quiet-queue/blob/main/assets/quiet_queue_demo.mp4](https://github.com/CODENIVA-AI/quiet-queue/blob/main/assets/quiet_queue_demo.mp4))\n\nThe repository includes six synthetic sample records, example outputs, and a short offline demonstration animation.\n\nThe optional Gradio interface runs locally rather than on a publicly deployed server.\n\nCode\n\nGitHub Repository:\n\n[https://github.com/CODENIVA-AI/quiet-queue](https://github.com/CODENIVA-AI/quiet-queue)\n\nThe public repository contains the source code, documentation, sample data, and automated tests.\n\nHow I Built It\n\nThe project uses scikit-learn, an open-source machine learning framework.\n\nIts locally trained TF-IDF and logistic regression classifier categorizes short English and Arabic messages. A separate cosine-similarity process identifies potential duplicate items.\n\nExplicit dates are prioritized using deterministic rules, and the results are exported as Markdown or JSON.\n\nThe software runs locally in Python without requiring paid AI APIs, commercial hosting, or cloud inference.\n\nThe prototype was developed with ChatGPT assistance. Its small synthetic training dataset limits accuracy, so human review remains essential.\n\nWhy Does Open Innovation Matter?\n\nOpen-source AI allows developers and users to inspect, understand, customize, and improve the system.\n\nUnlike a closed cloud API workflow, Quiet Queue can process information locally without uploading private messages to an external AI service.\n\nIts classification logic can be retrained, its examples expanded, and its components replaced by more advanced open models in future versions.\n\nThis approach supports privacy, transparency, low operating costs, and accessibility.\n\nThe Lazy Payoff\n\nIn the included synthetic demonstration, Quiet Queue processes six records, prioritizes an approaching deadline, and detects a duplicate pair.\n\nAll six automated tests passed during prototype validation.\n\nThe result is a repeatable task-organizing workflow. Real-world time savings have not yet been independently measured.\n\nQuiet Queue does not automatically send emails, delete records, or make irreversible decisions.\n\nAbout the Project\n\nCODENIVA AI is a broader long-term vision for adaptable AI capabilities and intelligent automation. Quiet Queue demonstrates one small, practical component of that vision.\n\nThis prototype is released under the MIT License and does not include proprietary CODENIVA AI platform code.\n\nBY DIRECTOR RASHED ALMAYYAS\n\nPOWERED BY CODENIVA AI", "url": "https://wpnews.pro/news/codeniva-ai-quiet-queue-a-privacy-first-ai-assistant-that-sorts-tasks-so-you-don", "canonical_source": "https://dev.to/dirrashed_almayyas_64592/codeniva-ai-quiet-queue-a-privacy-first-ai-assistant-that-sorts-tasks-so-you-dont-have-to-48o8", "published_at": "2026-10-24 21:58:22+00:00", "updated_at": "2026-10-24 22:11:20.513925+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-tools", "natural-language-processing", "ai-products"], "entities": ["CODENIVA AI", "Quiet Queue", "Rashed Almayyas", "GitHub", "scikit-learn", "Gradio", "ChatGPT"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/codeniva-ai-quiet-queue-a-privacy-first-ai-assistant-that-sorts-tasks-so-you-don", "markdown": "https://wpnews.pro/news/codeniva-ai-quiet-queue-a-privacy-first-ai-assistant-that-sorts-tasks-so-you-don.md", "text": "https://wpnews.pro/news/codeniva-ai-quiet-queue-a-privacy-first-ai-assistant-that-sorts-tasks-so-you-don.txt", "jsonld": "https://wpnews.pro/news/codeniva-ai-quiet-queue-a-privacy-first-ai-assistant-that-sorts-tasks-so-you-don.jsonld"}}