Developers often struggle to get actionable results from AI coding assistants. This guide provides 7 detailed prompts, designed at the Principal Engineer level, that walk AI step by step through code review, debugging, testing, architecture, and productionization. Copy, paste, and adapt these prompts to ship scalable, secure, production-ready software with AI support.
Discover how the 2017 paper Attention Is All You Need introduced Transformers, sparking the AI revolution that powers ChatGPT, Claude, Gemini, and today’s LLMs.
Running large language models (LLMs) locally is easier than ever, but which tool should you choose? In this guide, we compare Ollama, vLLM, Transformers, and LM Studio—four popular ways to run AI on your own machine. Whether you want the simplicity of a command line, the flexibility of Python, the performance of GPU-optimized serving, or a sleek GUI, this showdown will help you pick the right workflow for your needs.
Picking an AI coding partner in 2025? GPT-5 often wins fast bug-fixing and tool-heavy workflows, while Claude’s Sonnet-4 earns praise for steady, requirement-faithful edits in big repos. Benchmarks like SWE-bench show both at the top; developer reports vary. The real answer: pilot on your code and choose what ships green tests most reliably.
OpenAI has made a bold move into the open-source community by releasing a new family of models: GPT-OSS. This guide will introduce you to these powerful, open-weight models, their technical specifications, and how you can start running them on your own hardware for agentic workflows and advanced reasoning tasks.
GPT-5 is here and it’s redefining AI. With a huge 400k-token context, advanced reasoning, and powerful multimodal skills, it’s built for more than chat. From coding to research, GPT-5 delivers smarter, faster, safer results—making it the most capable AI agent yet.
Explore how Large Language Models (LLMs) reason step-by-step using CoT, RAG, tools, and more to solve problems accurately and think like humans.
Transformers power AI models like GPT-4 and BERT, enabling machines to understand and generate human-like text, transforming how we interact with technology.
Discover how to create, fine-tune, and deploy powerful LLMs with customized Retrieval-Augmented Generation (RAG) using Ollama. Learn best practices, optimize performance, and integrate RAG for accurate, domain-specific responses.