Showing 12 of 122 articles on AI systems, retrieval, backend engineering and DevOps.
Design clean, scalable REST APIs — resource naming conventions, versioning strategies and pagination patterns, with real-world examples.
Build production REST APIs with Node.js 22+ — folder structure, error handling, validation, logging, and performance tips for Express and Fastify.
For B2B and regulated domains — sensitive data handling, compliance, latency trade-offs, and when a local model beats a cloud LLM API.
Evaluate and monitor fine-tuned LLMs and ML models in production — the metrics that matter, drift detection, A/B testing, and dashboard setup.
Designing AI agents that hold up — single-step vs multi-agent workflows, tool-calling patterns, memory architecture, and the reliability pitfalls.
Install Ollama, pull and manage open-source LLMs, run terminal chats, and wire local models into Node.js and Python — no tokens leave your machine.
Build a production chatbot with LangChain and OpenAI or Ollama — document ingestion, vector stores, a RAG pipeline, and a deployable web interface.
How Graph-RAG combines knowledge graphs with LLMs to improve multi-hop reasoning and accuracy — with code examples and enterprise use cases.
LangChain vs LlamaIndex compared on architecture, use cases and developer experience — with code examples to help you pick an LLM framework.
Fine-tune open-source LLMs like Llama and Mistral with PyTorch and Hugging Face — dataset prep, LoRA and QLoRA, training, evaluation, deployment.
A beginner's tour of deep learning architectures — CNNs, RNNs and Transformers — with practical PyTorch examples and the trade-offs between them.
Build a production ML pipeline end to end — data collection, preprocessing, training, evaluation, and deployment behind a REST API or function.