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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 »
AI chatbots are the new norm. What earlier was “ask Google” has now largely become “ask Claude”. And that is not just a change of platforms. The new form of conversational guidance goes a whole lot deeper than trying to find the best car for you or looking for an upskilling course. It now spills
How People are Figuring Out Life With Claude Read More »
Modern AI systems struggle with memory. They often forget past interactions or rely on Retrieval-Augmented Generation (RAG), which depends on constant access to external data. This becomes a limitation when building assistants that need both historical context and a deeper understanding of users. MemPalace offers a different approach, enabling structured, persistent memory with higher precision
MemPalace Explained: Building Long-Term Memory for AI Agents Beyond RAG Read More »
Voice assistants that engage in back-and-forth communication are something you’ve likely experienced. But a voice assistant that provides rational, uninterrupted exchanges via spoken dialogue? That’s what xAI delivered with their Grok Voice Think Fast 1.0 in April 2026 and instantly, it became the top model on the τ-voice Bench leaderboard. This is not simply another
Grok Voice Think Fast 1.0: Build Voice AI Agents That Actually Think 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
MCP vs Agent Skills: Different Altogether Read More »
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,
What is Agentic AI? Read More »
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 »
Google just changed how developers do research. On April 21, 2026, they launched Deep Research Max. It runs on Gemini 3.1 Pro and is not just another chatbot upgrade. This is an autonomous AI research agent. It plans, searches, reads, reasons, and writes, all from a single API call. By the end, you get a
Google Deep Research Max: Build Autonomous AI Research Agents in Minutes Read More »
