Yesterday, a new type of LLM (diffusion-based LLM) was released. I ran a few tests to understand how it performs. This could be the next breakthrough 🚀 🔸 What is a diffusion-based LLM, and how is it different from other LLMs like ChatGPT? The LLMs we use today work by

The simplest way to understand how an LLMs like ChatGPT works (non-tech explanation) with an example 💡 An LLM has only one job: predict the next word (or "token," to be precise). That's it. Not a sentence, not a story, just the next word! Example: (Look at the diagram) 👉 If

I built an agentic software development team to see how agents collaborate to complete a task 🚀 using a task manager. I added three agents to the team - - Project Manager - Developer - QA. Also, all of them have access to a common tool - a Task Manager! 👉 I then told the

Why do LLMs fail to count letters in a word? Here's an easy way to understand. But let's NOT do the famous "strawberry" test. 🍓 Because, most LLMs now answer that correctly (memorised it probably?). Instead, use a slightly misspelled word - "strawberi" or "strayberi," etc. 👉

Do LLMs "understand" what they are saying? Yes, they do. They are not just simple next-token predictors! If you want a true next-token predictor, look at how a Markov chain works. Its behavior is predictable. But LLMs are not. Why? 👉 Because LLMs do not process words, tokens,

I built a simple MCP server to turn my Claude Desktop into an analytics tool! 🚀 I created a MySQL MCP server and added it to my Claude Desktop. Now, it's not just an LLM that generates answers - it can also read my database, fetch the right data by writing accurate queries,

Why can't LLMs do math? Why does it work when they are allowed to 'think'? A simple explanation. For simple math problems, like 2 + 2 = ?, the model answers correctly because it has seen that exact question countless times during training. In fact, it might have memorized "2 + 2

This comic is completely generated by AI. Yes, the new image generator update in GPT-4o is insane! 🚀 The most impressive thing about this update is that the text looks much better than in previous image models. I even tried generating posters, infographics, and even food

My AI refuses to believe it's an AI 😅. I fine-tuned a base language model just enough so it could answer questions - but I gave it zero instructions about its identity. (which they usually do - "You are ChatGPT") Instead, I just taught to answer questions. And I started asking

Hey everyone! 👋 Been off the grid for a few months -what's new?

I built an MCP server that finds up-to-date documentation for any npm package you add to your project. This makes sure your AI IDE (like Cursor) doesn't generate outdated implementations of the npm packages you use. github.com/meanands/npm-p…

Building a simple MCP server in Javascript - Tutorial video - youtu.be/H4Odxtncxrk

I built an MCP server that turns my SaaS product (Engagespot) into an Agentic experience! If you want to learn how to build AI Agents and MCP servers, checkout -> youtu.be/H4Odxtncxrk

I built an MCP Client in JavaScript from scratch! Yes - without using the MCP library. Why? Because I wanted to truly understand how MCP client-server communication works over the JSON-RPC protocol behind the scenes. Here's what I learned 👇 📍 MCP client–server communication

Google just released Gemini 2.5 Flash Image model. One thing that impressed me is it's character consistency! Photoshop is now officially cooked! 😅 I tried the model with my own photos, and the results are impressive compared to any existing image models out there. Look at the

I built (vibe coded) a Chrome extension that lets you replace Myntra's models with your own photo 😉 With this virtual try-on tool, we can upload our photo and see yourself wearing any outfit directly on the Myntra product page. The extension uses Google's newly released Gemini

OpenAI might have just killed many startups with the AgentKit launched yesterday. They made it super easy to build AI agents without using complex frameworks. All using a simple drag-and-drop workflow editor to design logic, add agents, run evals, do vector search, and even

Fine-tuning isn't the final answer for improving agentic performance - at least that's what a new research paper from Stanford says. This new paper from Stanford, SambaNova, UC Berkeley introduces a new strategy - Agentic Context Engineering (ACE). It talks about a better way

If you’re a software engineer worried vibe coding will take your job, you have no idea what your job is.

People calling AI a bubble- If you want to know what's hype vs what's real, ask: Can you live without it? Now try that with ChatGPT. Can we go back to the pre-ChatGPT era?