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Want to add a safety layer in your chatbot, image analyzer or any another LLM-based system? I would strongly suggest you try OpenAI’s moderation model: omni-moderation-latest, this can help your system identify if the input is potentially harmful or not, that too free of cost. We’ll look into the background of the model, how to […]
OpenAI Omni Moderation: How to Filter Text & Images for Free Read More »
There are some obvious signs that can instantly differentiate between regular and advanced AI users. One, for instance, is the use of voice AI for daily tasks. While majority users still toil away on their keyboard for the perfect prompt, a person proficient in the use of AI now simply speaks to it. A well-put
OpenAI’s New API Voice Models Will Change the Way You Use AI Read More »
Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions. Large Language Models change this by helping machines understand language, extract meaning, and generate
Feature Engineering with LLMs: Techniques & Python Examples Read More »
Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and images in a single
Gemini API File Search: The Easy Way to Build RAG Read More »
Most ML projects do not fail because of model choice. They fail in the messy middle: finding the right dataset, checking usability, writing training code, fixing errors, reading logs, debugging weak results, evaluating outputs, and packaging the model for others. This is where ML Intern fits. It is not just AutoML for model selection and
ML Intern in Practice: From Prompt to a Shipped Hugging Face Model Read More »
Projects are the bridge between understanding AI and actually building with it. While the last couple of years were dominated by generative models, the shift now is toward systems that can think in steps, use tools, and act with a clear objective. This guide brings together over 15 solved agentic AI projects designed to help
15+ Solved Agentic AI Projects with Github Links Read More »
There’s a lot of noise right now making it seem like you have to pick a side between MCP and Agent Skills. It’s being framed like a high-stakes rivalry, but that’s a total misunderstanding of the tech. Skills and MCP is fundamentally different things. Skills are just a prompt loaded on demand, while MCP is
April has been a busy month in the world of AI. Two major AI models, hailing from the biggest AI companies of today, saw their debuts simultaneously. Anthropic was the first to drop Opus 4.7, and close to follow on its heels was OpenAI, which came out with its GPT-5.5. Though the leading models from
GPT 5.5 vs Opus 4.7: Which is the Best AI Model Today? Read More »
Agentic AI refers to autonomous AI systems that can accomplish complex tasks with minimal human supervision. Unlike traditional AI, which reacts to prompts, agentic AI can plan, adapt, and execute actions toward a goal, making decisions throughout the process. These systems are made up of AI agents, each handling a specific part of the task,
Coding assistants have moved beyond autocomplete into full agents that can read projects, run commands, edit files, and iterate toward outcomes. Tools like Claude Code and Codex both operate in this space, but take different approaches. Claude Code centers on a unified agent loop across environments, while Codex spreads capabilities across CLI, IDE extensions, cloud
Claude Code vs Codex: A Detailed Terminal Agent Comparison Read More »
