Practical guides on building with Claude, LLM APIs, agents, MCP servers and modern developer workflows.
How to build a production AI content pipeline - brief intake, Claude generation, quality scoring, and output routing.
AI features have unique rollout risks: unpredictable outputs, cost spikes, latency variance.
How to use AI to personalise app onboarding based on user goals, context, and behaviour - improving activation rates without building complex rule-based branching logic.
Use AI to generate personalized push notification copy, predict optimal send times and lift engagement, with practical Firebase and Cloud Functions examples.
How to build AI features in React apps: streaming responses, state management for LLM outputs, tool use patterns.
Step-by-step guide to building a Model Context Protocol server that extends Claude Code with your own tools, resources, and data sources.
How to integrate the Claude API into iOS and Android apps: model selection, cost control, streaming, tool use.
How to build a production-ready AI backend on Firebase Cloud Functions - secure API key handling, streaming, per-user rate limiting.
A step-by-step GEO audit process - how to assess existing content for AI citability gaps, prioritise pages to retrofit, and measure whether GEO improvements are working.
Definition blocks are the single highest-impact GEO element. How to write machine-readable definitions that AI engines extract, cite.
Technical content has unique GEO advantages - code blocks, version numbers, and specificity are exactly what AI engines prefer.
GEO and SEO optimise for different engines with different ranking signals.
How Perplexity selects and cites sources - crawl signals, content structure, authority indicators.
LLM outputs are not type-safe - until you make them so. How to use Zod schemas, structured outputs, and retry logic to get reliable typed data from any LLM in TypeScript.
Step-by-step guide to building a production RAG (Retrieval-Augmented Generation) pipeline using Supabase, pgvector.
How to add semantic search to your app using pgvector and embedding models - from generating embeddings to writing similarity queries, with practical Supabase examples.
The TypeScript patterns that make LLM-powered applications maintainable at scale - typed clients, middleware chains, retry logic, provider abstractions.
How to run small AI models directly in the browser using WebAssembly and ONNX Runtime Web - no API calls, no latency, no cost per inference.
A/B testing AI features requires different approaches than testing traditional product changes. Learn how to design experiments that produce reliable
LangChain, LangGraph, CrewAI, and AutoGen all aim to make building AI agents easier. Here is how they compare and how to pick the right one for your use case.
AI benchmarks drive model selection decisions, but most developers do not know what they measure or why they may not predict real-world performance.
GitHub Copilot, Cursor, Claude Code, and a dozen alternatives all claim to make developers more productive.
Using AI for content creation at scale requires more than a good prompt. Learn how to build content pipelines that produce high-quality output consistently
LLM applications need layers of content moderation - both for inputs from users and outputs from the model.
LLM API costs can scale faster than your user base. Here are the practical techniques for controlling AI spending while maintaining application quality.
AI ethics is not abstract philosophy - it has concrete engineering implications. Here is how to think about bias, transparency, and responsible deployment
AI can accelerate ASO research, keyword analysis, screenshot copy, and metadata generation. Here is how indie developers can use AI to improve App Store
iOS developers have multiple options for adding AI to their apps in 2026: cloud APIs, on-device Core ML, and Apple's own AI frameworks.
Integrating AI into mobile apps involves trade-offs between on-device and cloud, battery impact, latency, and privacy.
Image generation APIs are mature and easy to integrate. Here is a practical guide to AI image generation for developers
A developer's perspective on where AI stands in mid-2026: what capabilities have matured, what remains unsolved, and what the trajectory looks like for the
AI has transformed app localization - but using it well requires understanding its strengths, its failure modes, and how to build a quality review process
AI applications fail in ways that traditional monitoring does not catch. Learn the observability stack for LLM production
Standard product metrics miss what matters for AI features. Learn the AI-specific metrics - engagement, quality, coverage
Reasoning models like o3 and Claude's extended thinking generate internal reasoning before responding.
AI safety is moving from an abstract research concern to a practical engineering discipline. Here is what application developers need to understand about
AI semantic search and traditional keyword search solve different problems. Learn when each is appropriate, how hybrid search combines them, and the real
Prompt injection attacks manipulate LLM behavior through malicious inputs. Learn how these attacks work, what they can do to your application, and the
Testing AI applications is fundamentally different from testing traditional software. Learn the strategies and tools that make LLM application testing
AI can dramatically accelerate user research - synthesizing interviews, analyzing feedback, and identifying patterns at scale.
AI TTS has become indistinguishable from human voice in many contexts. Learn about the leading APIs, voice cloning techniques, and where TTS creates real
AI can automate complex knowledge work workflows that were previously too unstructured to automate.
Chain-of-thought prompting dramatically improves LLM performance on reasoning tasks by making the model show its work.
Designing a good AI chat experience goes well beyond the prompt. Learn the UX patterns, error states, and trust signals that separate professional AI
Running AI models directly on device - phone, laptop, or embedded hardware - eliminates latency, preserves privacy, and works offline.
Embedding models convert text into numerical vectors for semantic search, RAG, and clustering. Learn how to choose the right model, understand its
Few-shot prompting uses examples to shape LLM behavior more reliably than descriptions alone. Learn how to select examples, structure them, and use them
Fine-tuning adapts a base LLM to your specific task or domain. Learn when it outperforms prompting and RAG, what the process looks like, and the real costs
Fine-tuning and RAG solve different problems. Learn when to use each, how to combine them, and which factors should drive your architectural decision.
Function calling lets LLMs call external APIs, read databases, and trigger actions in your system.
Knowledge graphs represent relationships between entities explicitly, enabling AI to reason about connections that vector search misses.
Caching is one of the most effective tools for reducing LLM API costs and latency. Here is a practical guide to the three main caching strategies and when
The context window is the fundamental constraint of every LLM application. Understand what it is, how token limits affect your design, and practical
Evaluating LLM quality is harder than it looks. Learn the metrics that matter - from automated benchmarks to human evaluation
Serving LLMs at production scale requires specialized inference infrastructure. Learn the key optimization techniques
Temperature, top-p, and top-k control how LLMs sample from probability distributions. Learn what these parameters mean, how they interact, and what values
Understanding how LLM pricing works - per token, input vs output, caching, batch - is essential for building AI products with sustainable unit economics.
Model distillation trains a smaller model to mimic a larger teacher model, achieving similar performance at lower cost.
Multimodal models process images, audio, and text together. Learn how they work, what they enable, and how to use them effectively in your applications.
Open source LLMs have closed much of the gap with proprietary models. Here is what the landscape looks like in 2026, which models to consider, and when
Prompt caching lets providers reuse the processed representation of repeated prompt prefixes. Here is how it works, how to design for it, and how much it
Learn the core principles of prompt engineering that separate reliable AI outputs from inconsistent ones.
Retrieval-Augmented Generation (RAG) lets LLMs answer questions from your own data without fine-tuning.
AI hallucinations - confident false statements - are the reliability challenge for LLM applications.
Getting reliable structured JSON from LLMs is a core challenge for AI applications. Here is how structured output modes, JSON schemas, and validation
Using LLMs to generate training data for other AI tasks is now standard practice. Learn how synthetic data generation works, its limitations, and quality
The system prompt is the most important prompt in any LLM application. Here is how to write one that establishes reliable behavior, handles edge cases, and
Vector databases store and search high-dimensional embeddings for AI applications. Learn how they work, how they compare, and when you actually need one
AI models have no memory between sessions by default. Here's how to give Claude Code and AI agents persistent context
How to build a production AI content pipeline using Claude agents - the architecture, the handoffs, the quality gates, and the parts that still require a
Google AI Overviews cite sources that get 35% more clicks than organic position 1. Here's what Google looks for when selecting sources to cite
SEO in 2026 requires serving two search channels simultaneously - traditional Google and generative AI tools.
A step-by-step guide to building a real SEO agent using Claude - what it should know, what tools it needs, how to separate editorial from technical SEO,
Claude Code is Anthropic's AI coding tool that lives in your terminal and IDE. Here's what it actually does, how it differs from other AI coding tools, and
Claude Code hooks let you run shell commands automatically in response to AI actions - before a tool call, after a response, when a session starts.
Claude and ChatGPT are the two most-used AI coding assistants. Here's an honest comparison of where each one excels, where each one struggles, and which to
Google's E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) matters more in 2026 than ever before.
The exact workflow we use at Stika Studio to go from a content idea to a published, SEO-optimized blog post using Claude agents
Generative Engine Optimization (GEO) is how you get cited in ChatGPT, Gemini, Perplexity, and Google AI Overviews.
System prompts for AI agents are not the same as prompts for chatbots. Here's what makes an agent prompt work
When does one AI agent become two? The principles behind multi-agent pipeline design - when to split, how to design handoffs, and the failure modes to avoid.
skills.md files let you store specialized instructions for Claude Code that activate on specific tasks.
Structured data (JSON-LD schema) helps both Google and AI systems understand your content. Here's which schema types matter most in 2026 and how to
Topical authority - being the definitive source on a focused topic area - is the most durable SEO strategy in 2026.
AI agents explained without hype - what they actually are, how they differ from plain prompts, and when building one is worth the effort.
Model Context Protocol (MCP) lets AI models like Claude connect to external tools, APIs, and data sources in a standardized way.