Artificial Intelligence
Explore the world of AI, machine learning, and data science.
AI Is a Tool - Your Expertise Makes It Valuable
Artificial intelligence is transforming how we work, create, and solve problems. But AI is only as useful as the person guiding it - it amplifies your existing knowledge and accelerates research, but it does not replace understanding. Whether you are exploring local AI models, building intelligent applications, or simply trying to understand the landscape, these guides will help you make informed decisions.
Wizard Tech Services offers AI & Automation services including local AI installation, model management, and training. Read our AI philosophy to understand our approach to responsible AI adoption.
Machine learning enables systems to learn patterns from data without explicit programming. Explore supervised, unsupervised, and reinforcement learning, plus feature engineering and evaluation metrics like accuracy, precision, and recall.
LLMs like GPT-5, Claude 5, Gemini, and LLaMA power conversational AI; Stable Diffusion, FLUX, and DALL-E generate images from text. Compare cloud APIs vs. local inference, parameter counts, quantization, and fine-tuning for your domain.
AI workloads are GPU-intensive - VRAM is the bottleneck. NVIDIA RTX 5090 (32 GB), H100, and B200 lead; 12+ GB cards comfortably handle 7B-13B models locally. Also covers llama.cpp CPU inference, Apple Silicon (M4 Max/Ultra), and cloud GPU rental.
Data science bridges raw data and actionable insights using statistics and programming. Python with pandas, NumPy, scikit-learn, and Jupyter notebooks is the standard toolkit. Covers cleaning, analysis, and visualization with Matplotlib and Plotly.
PyTorch and TensorFlow dominate deep learning; LangChain and LlamaIndex simplify RAG-based LLM apps; Hugging Face hosts thousands of models. Covers prompt engineering, vector databases, and deploying AI as APIs.
Computer vision enables machines to interpret images and video - object detection, facial recognition, medical imaging, and more. OpenCV handles foundational processing; YOLO and ResNet tackle complex recognition tasks.
AI Development
Frameworks for building AI systems, and tools for developing faster with AI as your co-pilot.
Want deeper, interactive content?
The AI Hub - Development section covers SDKs, RAG pipelines, fine-tuning, and building production AI apps with expandable guides and code examples.
Developing WITH AI
Using AI as a tool in your development workflow - code editors, agents, copilots, and APIs that let you build faster, debug smarter, and ship more. This is separate from building the AI itself.
VS Code fork with AI built into the editor - codebase-aware completions, multi-file edits, and an agent that can plan and execute changes across your project.
- Codebase-aware chat and completions
- Agent mode for multi-file refactors
- Inline edits with diff preview
- Works with Claude, GPT, and local models
Microsoft's AI pair programmer integrated into VS Code and other IDEs - inline completions, chat, and multi-file editing powered by OpenAI and Claude models.
- Real-time inline code completions
- Copilot Chat for Q&A and refactoring
- PR summaries and code review assistance
- Integrates with VS Code, JetBrains, Neovim
Anthropic's CLI-based agentic coding tool - understands your entire repo, runs terminal commands, edits files, and executes multi-step tasks from natural language instructions.
- Full repo context awareness
- Runs bash commands, edits files, runs tests
- Agent SDK for building custom AI agents
- Hooks system for automated workflows
Open-source CLI pair programmer - connects to your Git repo, makes targeted edits with proper commit messages, and supports most major LLM providers.
- Git-aware edits with auto-commits
- Works with Claude, GPT, local models
- Map-based repo understanding
- Voice input support
Build AI features directly into your apps - chat completions, function calling, structured outputs, vision, and streaming. The foundation for most AI-powered products.
- Streaming responses for real-time UX
- Tool/function calling for structured actions
- Vision endpoints for image understanding
- Embeddings for semantic search and RAG
AI-native IDE with 'Flows' - a context-aware agent that can read files, run commands, and make changes while understanding your codebase history and intent.
- Cascade agent for multi-step tasks
- Free tier with generous completions
- Codebase indexing for deep context
- Integrated terminal and diff views
Prompt Engineering for Developers
Getting useful output from AI tools is a skill. For code tasks: be specific about the language, framework, and constraints. Provide context (file structure, error messages, existing code). Use chain-of-thought prompts for complex logic. For agents like Claude Code or Aider, describe the goal, not step-by-step instructions - let the agent plan.
Instead of:
"Add a button to the form"
Try:
"In ContactForm.tsx, add a Submit button using the shadcn Button component that calls handleSubmit and is disabled while loading"
AI + Git Workflow Tips
Commit before running an AI agent on your codebase - it gives you a clean restore point if the agent goes off-track. Use branches for larger AI-assisted refactors. Review diffs carefully before accepting: AI tools are fast but not infallible. Tools like Aider write commit messages automatically; Claude Code and Cursor let you review before committing.
Building AI - Frameworks & Tools
Frameworks, model hubs, and deployment infrastructure for building and shipping AI models and LLM-powered applications.
The dominant deep learning framework for research and production - dynamic computation graphs, extensive GPU support, and the largest ecosystem of pre-trained models.
Key Features:
- Dynamic computation graphs (eager execution)
- Extensive CUDA GPU acceleration
- TorchVision, TorchAudio, TorchText extensions
- Dominant in AI research and increasingly in production
Google's ML framework with Keras as its high-level API - strong production tooling with TensorFlow Serving, TFLite for mobile, and TensorBoard for visualization.
Key Features:
- Keras provides simple, high-level model building
- TFLite for mobile and edge deployment
- TensorBoard for training visualization
- TensorFlow Serving for production inference
Frameworks for building LLM-powered applications - chains, agents, RAG pipelines, and tool use. LangChain for orchestration, LlamaIndex for data indexing.
Key Features:
- RAG (Retrieval-Augmented Generation) pipelines
- Agent frameworks with tool calling
- Vector store integrations (Pinecone, Chroma, pgvector)
- Prompt templates and output parsing
The GitHub of AI - hosts thousands of pre-trained models, datasets, and Spaces for demos. The Transformers library provides unified access to most model architectures.
Key Features:
- 500K+ pre-trained models available
- Transformers library for unified model access
- Datasets library for ML training data
- Spaces for hosting model demos and apps
Run open-source LLMs locally with one command - Ollama provides a CLI and API server, LM Studio adds a GUI. Both handle model downloading and quantization.
Key Features:
- One-command local model deployment
- OpenAI-compatible API endpoints
- Automatic GGUF quantization management
- GPU and CPU inference support
Purpose-built databases for storing and querying embeddings - essential for semantic search, RAG, and recommendation systems in AI applications.
Key Features:
- Pinecone, Chroma, Weaviate, pgvector
- Similarity search via cosine/dot product
- Essential for RAG pipeline retrieval
- Metadata filtering alongside vector search