Articles for people who
ship the stack
Original rewrites on React, Node.js, TypeScript and AI — practical notes from the same engineering practice behind our operator software. Article bodies are in English.
Tagged: ai
Self-adaptive JavaScript frontends for AI-driven interfaces
How streaming, vector retrieval, reactive state, dynamic components, and observability reshape client architecture for AI-native UIs.
1364 wordsRead articleOrchestrate researcher and coder agents with LangGraph
Scaffold a LangGraph multi-agent app with soul prompts, Ollama models, Tavily search, handoff tools, and Postgres checkpoints for orchestrator-led workflows.
2200 wordsRead articlePrompt Engineering: Steer LLMs Without Fine-Tuning
Use roles, few-shot examples, chain-of-thought, and format constraints to guide models — and know when prompting alone hits a ceiling versus fine-tuning.
748 wordsRead articleTools vs Skills vs MCP: Three Layers of an AI Agent
Tools expose actions, skills encode workflows, and MCP standardizes external connections. A clear mental model for designing agent architectures without mixing the layers.
1495 wordsRead articleYour First Tool-Using AI Agent with LangChain
Build a small LangChain math agent in Python: initialize a chat model, register tools, let the LLM choose which function to call, and see multi-step tool use.
1062 wordsRead articleHow Multimodal Models Relate Text, Images and Audio in One Request
Understand what a modality is, how multimodal models map text, images and audio into a shared representation, where that helps, and where visual answers still go wrong.
1115 wordsRead article30 Practical Claude Prompting Techniques from Real Daily Use
A field-tested breakdown of 30 Claude prompting techniques, organized by what they actually accomplish, from clear instructions to full prompt systems.
3022 wordsRead articleReact Development with AI: Real Strengths, Real Limits
Explains where AI coding assistants genuinely speed up React work, where they fail, and a practical workflow for using them without losing code quality.
1053 wordsRead articleVector Databases Explained: The Engine Behind RAG and AI Search
Learn how vector databases turn text into embeddings, power semantic search and RAG pipelines, and drive real-world AI applications like recommendations.
1671 wordsRead article
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