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AM
Asib Mahmud
Technical Stack/Honest Inventory

Tools, Systems & Disciplines

I don't believe in fake percentage bars (“95% in Python”) or five-star skill ratings. Below is an honest breakdown of the technologies I ship to production daily, how I think about each layer, and where I add the most value.

DisciplineAM-STACK

Web & WordPress Architecture

Fast frontend interfaces, semantic markup, and reliable WordPress systems without plugin bloat.

“I treat WordPress as a serious publishing engine, not an excuse to install 50 visual builders. For custom web builds, I prioritize clean HTML, sub-second LCP, and dependable component structures.”
Production Technologies
  • ›Next.js / ReactCore
    App router, SSR, server actions, dynamic pages
  • ›TypeScriptCore
    Strict typing, interface design, robust API models
  • ›WordPress Block API & Theme DevCore
    Custom blocks, PHP 8.3, REST API & headless setups
  • ›Tailwind CSS & Modern CSSCore
    Grid, flexbox, custom properties, responsive systems
  • ›Performance & Core Web VitalsCore
    Asset optimization, edge caching, DOM reduction
  • ›PHP & Bedrock
    Modern WordPress directory architectures, Composer
  • ›WooCommerce Customization
    Checkout flows, payment gateways, schema overrides
DisciplineAM-STACK

Software Engineering & Systems

Disciplined software principles, clean REST/GraphQL APIs, and maintainable application logic.

“Graduating in Software Engineering taught me how systems behave at scale: database indexing, state boundaries, error budgets, and how to write code that another human can read six months later.”
Production Technologies
  • ›System ArchitectureCore
    Modular separation of concerns, DRY patterns, microservices
  • ›REST & API DesignCore
    Predictable endpoints, idempotency, structured JSON payloads
  • ›Relational & NoSQL DatabasesCore
    PostgreSQL, MySQL, SQLite, query optimization
  • ›Node.js & Express / NestJS
    Backend services, authentication, task queues
  • ›Git Workflow & CI/CDCore
    Semantic commits, automated linting, GitHub Actions
  • ›Design Patterns & Refactoring
    Repository pattern, adapter pattern, unit test coverage
DisciplineAM-STACK

Data Science & Analytics

Wrangling noisy datasets, statistical analysis, and making numbers tell the real story.

“Most data problems are won or lost in data cleaning. I focus on dependable ingestion, exploratory analysis, and clear visual synthesis rather than treating metrics like a black box.”
Production Technologies
  • ›PythonCore
    Pandas, NumPy, SciPy, custom data extraction pipelines
  • ›Exploratory Data Analysis (EDA)Core
    Statistical distributions, outlier detection, imputations
  • ›Data VisualizationCore
    Matplotlib, Seaborn, interactive web charts
  • ›SQL & Query TuningCore
    Window functions, aggregations, schema partitioning
  • ›Time-Series Data
    Sensor logs, moving averages, trend decomposition
  • ›Feature Engineering
    Encoding, scaling, correlation pruning
DisciplineAM-STACK

AI / ML & Computer Vision

Practical machine learning and computer vision pipelines built for edge devices and real world lighting.

“I don't claim to have rewritten backprop from scratch, but I know how to train, quantize, and deploy models that solve specific industrial, visual, or classification bottlenecks under real hardware constraints.”
Production Technologies
  • ›Computer Vision & OpenCVCore
    Image preprocessing, edge filtering, ROI segmentation
  • ›PyTorch & YOLOv8Core
    Object detection, classification, transfer learning
  • ›Edge Inference & TensorRTCore
    Model quantization (INT8/FP16), Jetson optimization
  • ›Scikit-LearnCore
    Classical ML algorithms, random forests, regression, evaluation
  • ›Data Augmentation
    Albumentations, glare/shadow synthesis, real-world noise simulation
  • ›LLM & Embedding Integrations
    API orchestration, prompt engineering, semantic search
DisciplineAM-STACK

Tools, Infrastructure & Craft

The daily workbench tools that keep deployments predictable and codebases tidy.

“I value tools that get out of the way. I prefer a sharp command line, clean Docker containers, and clear documentation over complicated proprietary toolchains.”
Production Technologies
  • ›Docker & ContainerizationCore
    Multi-stage builds, local dev parity, docker-compose
  • ›Linux & Bash / ZshCore
    Shell automation, log diagnosis, server maintenance
  • ›Vercel & CloudflareCore
    Edge routing, DNS, SSL, zero-downtime deployments
  • ›Figma to CodeCore
    Translating designer intent into responsive, accessible code
  • ›Postman & HTTP Clients
    Endpoint testing, mock servers, automated checks
  • ›VS Code & Neovim
    High-productivity editor setup with linting and keybindings
Engineering Principles

Frameworks come and go. Fundamentals endure.

I prioritize strong software engineering fundamentals: understanding how memory and CPU react to un-indexed SQL queries, respecting the DOM tree, keeping dependencies lean, and designing modular systems that remain pleasant to maintain long after launch day.

Have a specific technology stack in mind? Let's talk→