OpenAI just launched ChatGPT Go, a new low-cost plan priced at ₹399/month—India-only for now. You get big bumps over Free (notably ~10× message and image limits, file uploads, and longer memory) while Plus keeps the heavier tools (Deep Research, Sora, Agent Mode). Here’s how Go compares to Free/Plus/Pro, who it’s for, and whether you should switch.
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.
Experiment tracking tools often slow down research with heavy configuration and infrastructure setup. RexF flips the script by offering zero-configuration reproducibility with just a single decorator. Here’s why it’s quickly becoming a favorite among researchers and data scientists.
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.
Step-by-step guide to building a neural network from scratch using Python and NumPy, with forward propagation, backpropagation, and training examples.
Learn TensorFlow from scratch with this beginner-friendly guide. Build, train, and evaluate a neural network using the MNIST dataset with step-by-step examples.
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.
Learn how to integrate AI-driven image generation into your workflow with Ollama, Stable Diffusion, ComfyUI, and DALL·E. This guide covers setup, benefits, and real-world applications of these powerful tools.