Nauman Mustafa

A field report on building a human-supervised AI workflow that turns starred inbox reading into natural narration, visual slides, and private video.

A personal comparison of GPT-5.5, Claude, Codex, and Claude Code based on coding workflow, context handling, readability, limits, and developer experience.

A 2-month YouTube automation experiment covering news scraping, video generation, auto-uploading, niche choice, costs, and watch time.

AI changed my job from writing every line to directing parallel software systems.

A software engineer journey from vibe coding to compound engineering, and what AI changes about architecture, intent, and code quality.

Field notes from building an iOS app with AI coding tools, covering what worked, what failed, and how to ship faster with quality.

A benchmark of Autify OCR versus Google OCR on rendered mobile UI text, including methodology, accuracy metrics, and failure analysis.

An experiment on compressing multiple LLM tokens into one representation for faster decoding and longer effective context.

Experiments on extending transformer context length, including training observations, tradeoffs, and lessons from long-context tuning.

How Autify for Mobile uses machine learning for visual regression testing, visual self-healing, and AI-powered app exploration.

A case study on automated mobile app navigation using behavior cloning and heatmap prediction for tap-location modeling.

A practical guide to applying modern deep learning and pre-trained NLP models in production software and product workflows.

How to use pre-trained NLP models effectively for text generation and question answering, with practical examples and tradeoffs.

A 2019 snapshot of major AI breakthroughs across text, image, audio, and video generation, with practical startup use cases.

Using GANs to generate unique icon designs, with model behavior examples and creative applications for design workflows.

Step-by-step tutorial for packaging and deploying a machine learning app to Google Cloud Run with Docker and Cloud Shell.

Overview of Google Cloud Run and how serverless containers can simplify deployment, scaling, and cost management for ML applications.

A cost-focused comparison of cloud GPU options for deep learning across AWS, Paperspace, Colab, and Google Cloud preemptible machines.