In Part 1, we started with a simple experiment. I gave ChatGPT a basic food and activity log and asked: How did I do today? The answer was generic. Why? Because ChatGPT did not know enough about me. It did not know my: - Goal - BMR - Daily calorie target - Protein target It did not know what “good” meant for me. That was the first real lesson: - A generic chat produces generic answers. - The next step was creating better context memory using Projects. Step 1: Create a Dedicated Project Instead of starting a random new chat every day, I created a dedicated ChatGPT Project for nutrition and activity tracking. Why does this matter? Because a regular chat is just a conversation. A Project is more like a focused workspace. It gives ChatGPT a place to keep the work organized around a specific purpose. For this project, the purpose was simple: 🍽️ Track food 🚶 Track activity 🔥 Estimate calorie burn 📊 Summarize the day 🎯 Help me understand whether I am moving toward my goal To create the Project: 1. Click Projects 2. Click New Project 3. Name it Nutrition and Activity Tracker Step 2: Add Basic Project Instructions Next, I gave ChatGPT a few simple Project Instructions. Nothing complicated. Just enough context to make the answers more useful. Something like: You are my nutrition and activity tracking assistant.My goal is to lose body fat while maintaining muscle.My estimated BMR is 1,800 calories per day. When I enter food, estimate calories and macros.When I enter activity, estimate calories burned.When I ask for a summary, show food calories, protein, carbs, fat, activity burn, net calories, and whether the day is trending toward a calorie deficit. Keep summaries concise. That small set of Project Instructions changed the quality of the output because ChatGPT now had context. To add Project Instructions: 1. Open the Project 2. Click the three dots 3. Select Project settings 4. Add your instructions The Same Food Log Became More Useful

Okay, I am going to say something I may be completely alone in thinking. I keep coming back to the same question. Why are we so obsessed with making robots look like us? I understand the argument. The world was built for humans. Doors. Stairs. Tools. Handles. Factories. Warehouses. Kitchens. Vehicles. So the logic makes sense on the surface. Build a robot shaped like a human and it can operate inside the world humans already built. Fair point. But then I look at a humanoid robot pushing a lawn mower (illustrative) through tall grass, and I cannot help but ask: Is that really the best solution? A purpose-built mowing machine seems far more logical than a human-shaped machine using a human-designed tool to perform a machine-friendly job. A humanoid robot mowing the lawn looks futuristic. But the boring machine built specifically to cut grass may actually be the better answer. And that just makes me pause and shake my head. Because this is where humanoid robots start to feel more like form over function. They look impressive. They photograph well. They feel like the future we were promised in movies, cartoons, and science fiction. But impressive is not the same as useful. And familiar is not the same as optimal. Maybe humanoid robots are necessary because we are trying to automate environments that were designed around people. Maybe they are a bridge technology. A way to bring robotics into homes, businesses, factories, and job sites without rebuilding the world around the robot. That is possible. But I still wonder if we are making a deeper mistake. Maybe we are not just designing robots to solve problems. Maybe we are designing robots in our own image because we still struggle to imagine intelligence, labor, and usefulness without putting ourselves at the center. That is the tension. We say we want machines to do the work better. But then we keep making the machine look like the worker. That is a very human thing to do. And maybe that is exactly the problem.

Welcome to the Out of The Box Series, where I test how far curiosity and AI can take you in 30, 60, or 90 minutes using today’s best no-code and low-code tools. No setup. No training. Just pure exploration, right out of the box. 🎬 This Episode: Gemini Image Editing Upgrade Google Gemini just upgraded its image editing model with Nano Banana 2, and I wanted to see how quickly it could solve a real everyday business problem. Turning a casual photo into a polished LinkedIn-style headshot. Not for fun. Not as a demo. For an actual profile upgrade. The result? Wow! In less than a minute, Gemini can take a casual selfie and give you something that looks much closer to a professional corporate headshot. 🧪 The Challenge Can Gemini take a simple, well-lit personal photo and turn it into a LinkedIn-ready headshot without making the person look fake, plastic, or over-edited? That was the test. ⏱️ The 1-Minute Playbook 1. Open Gemini 2. Upload a clear, well-lit selfie or casual photo 3. Copy and paste the prompt below 4. Adjust the outfit, background, or lighting with follow-up prompts 📋 Copy-Paste Prompt Use the default prompt provided by Gemini, or use this prompt: Transform this uploaded photo into a professional LinkedIn-style corporate headshot. Maintain my exact facial features, natural expression, and realistic skin texture without making it look artificial. Dress me in sharp professional business attire, such as a navy blue blazer or charcoal suit. Place me against a softly blurred, modern bright office background with clean, professional studio lighting. 💡 Bits & Pieces Pro Tips Start clean. AI works best when you give it a strong starting point. Use a clear, high-resolution photo with natural lighting, ideally near a window. Use the reset trick. If the first result changes your face too much, start a new chat, re-upload the image, and add: Keep my facial structure 100% true to the original photo. Dial in the details. If you like the face but not the outfit or background, just reply with something simple:

Most people think AI needs to solve massive problems to matter. Sometimes the best AI systems are simply the ones that help you understand yourself better every day. Several months ago, I set out to see whether ChatGPT could replace my nutrition and activity tracking app. At first, the goal was simple: 🍽️ Track what I eat. 🚶 Track what I do. 📈 Understand whether I am making progress toward my health goals. What I did not expect was that the project would become one of the best examples of how to get more value from AI. Today, the system tracks food, activity, workouts, steps, calorie burn, bodyweight, and long-term trends. It generates dashboards, calculates custom metrics, analyzes patterns, helps interpret nutrition labels and food photos, and provides feedback tailored to my goals. But what interests me most is not just the nutrition tracking. It is what the project teaches about AI. Over the next several posts, I’ll use this project as a real-world case study to explain some of the more useful features and nuances of ChatGPT and large language models, including: - 📁 Projects - 🧭 Project Instructions - 🧠 Context accumulation - ✍️ Structured prompting - 📸 Vision and image analysis - 🎨 Image creation - 💾 Memory vs. context - 🧮 AI reasoning versus calculation - 📊 Custom metrics - 🔁 Iterative system design - 🛠️ Building useful AI workflows without writing code - 🤖 How LLMs work, in plain English You do not need to understand every technical layer of a large language model to use one well. But it helps to understand the basics. LLMs predict, associate, reason across context, follow patterns, and generate outputs based on the information available to them. That is why context matters. That is why instructions matter. That is why the same prompt can produce a generic answer in one chat and a highly personalized answer inside a well-structured project. The interesting part is that none of these features are especially impressive on their own.

Let me introduce myself.....apologies if I've already done and this will be a re introduction but here goes I am Shuaib, software engineer with over a decade's experience in the industry. Build many internal Web, Desktop and Mobile Apps - this includes websites, complex web apps, crms, gui, form apps, spreadsheets to android and maui mobile apps using a variety of techniques and languages. Tbh the list is too long but it's tools, skills and experience that will always be with me. Looking back I've also had an amazing opportunity to learn from VERY talented professionals from engineers to designers to managers and jacks of all traders across Health, Gov, Finance, Banking and Private industries. Now it's time to pick up tools in the world of ai.... I am so excited 😊 and can't wait to interact more with ALL of you!