ITDogtics - Blog

Tired of "What Should We Do?" Introducing the Hangout App! Ever find yourself staring blankly when someone asks "Where should we go?" or getting caught off guard by a sudden downpour? The Hangout App is your new best friend for spontaneous plans! It uses your real-time "vibe" and the current weather to instantly generate personalized hangout ideas, making planning a breeze. Key Features: Location-Based Suggestions: Get ideas tailored to your current location. Customized for You: We consider the weather, your energy levels, and your preferences to suggest the perfect hangout. Map & Transit Help: See your suggested spots on a map and get public transit tutorials to help you get there. AI-Powered Plans: Our suggestions are generated by Gemini for smart, unique ideas. Get Started Today! Ready to simplify your spontaneous adventures? You can access the app at: https://hangout.itdogtics.com/ For the best experience, we recommend adding the app to y...

OpenAI just launched Codex, a smart tool for writing code. Lately, people are talking a lot about "vibe coding" and "agentic coding." This blog will explain what those things mean by looking at Codex. This is for you if: You're curious about Vibe Coding or Agentic Coding You know a little bit about Codex Easy Explanations: Vibe Coding and Agentic Coding Vibe Coding: Letting the Computer Mostly Write the Code Imagine telling a computer what kind of code you need, and it writes it for you. That's basically vibe coding. You don't worry too much about if the code is perfect or has mistakes. You just trust that the computer will give you something that works okay. Agentic Coding: The Computer Helps You Code Smarter Agentic coding is like letting the computer read your existing code files. It understands what's already there and then writes new code or changes old code based on that understanding. This makes some parts of coding automatic, like the comp...

Mastering the Art of Prompting: Why One Size Doesn't Fit All in the World of AI Agents It's a common mistake to use the exact same instructions (called "prompts") for different big language models (LLMs). Just like different people learn in different ways, different AI models are trained in their own special ways. This means the best way to talk to one might not be the best way to talk to another. This blog post will help you understand the different ways to give instructions to AI, whether you're just starting out or you already have some experience. Who This Blog Post Is For: People who are new to giving instructions to AI and want to learn the basics. People who want to share ideas and learn more about how to give the best instructions. Understanding the Basics of Giving Instructions (Prompting) Basically, prompting is how you tell an AI model what you want it to do. There are generally two main ways to do this: Zero-Shot Prompting: This is like asking a ...

Building AI Code Generation Tools from Scratch in Python 3: A Journey from Zero to Everything (Stage Report) After diving deep in our first three parts, this post is a bit different. It's not a typical progress update, but a moment to pause, reflect on the journey so far, and tackle some big questions that have come up – especially about the limits of AI in software development. Where We Left Off: The Roadblocks In our last discussion, we identified some key challenges our AI tool faced: Missing Design Assets: It couldn't create necessary visual elements (like image folders). Backend Blindspots: Setting up databases or connecting to external services was beyond its scope. The User Acceptance Puzzle: What the AI generated often didn't quite match what a user really wanted or expected. Ignoring Runtime Reality: The process couldn't detect if the generated code would actually run without errors. Honestly, finding straightforward ways to solve these purely with ou...

Whoa! AI is Going Wild!  Seriously, it feels like every single day there's a brand new AI tool or model popping up for, well, just about everything! I remember, not that long ago, as a software engineer, it was crazy seeing Claude drop their code thing and then Google's Gemini Code land almost at the same time – like, in the same week! It's non-stop, and honestly, it feels like this AI train has left the station and isn't slowing down. So, I'm writing this for anyone who's curious about AI or maybe feeling a bit lost in all the hype. Who's this for? Anyone who thinks AI is kinda cool. Anyone who's like, "What the heck is going on with AI?!" You might think the AI explosion started when ChatGPT showed up in November 2022, but actually, things were brewing way before that. A Quick Look Back at the AI Story Back in the day (around 2015!), the smart folks in universities were already writing tons of papers trying to solve all sorts of problems w...

Building AI Code Generation Tools from Scratch in Python 3: A Journey from Zero to Everything (Part 3) Welcome back! If you missed the previous parts, you can find them here: Part 1 : Covered the basic workflow, fundamental code generation, directory creation, file generation, and folder automation. Part 2 : Addressed the limitations of the prompts and Large Language Models (LLMs) used in Part 1. In this installment, we'll focus on the compilation pipeline. Disclaimer: I won't be sharing the complete project code directly. My goal is to encourage discussion and collaboration. For Beginners: Use this as a guide to build your own project and join the conversation. I'm happy to help with any learning challenges. For Experienced Developers: Please share your insights and discuss potential improvements. Recap In Part 1, we established the foundation for the compilation phase. This phase is crucial for identifying syntax errors, warnings, and coding inconsistencies. Our...

Building AI Code Generation Tools from Scratch in Python 3: A Journey from Zero to Everything (Part 2) If you missed Part 1, you can find it here: link . In our previous discussion, we covered the basic workflow, fundamental code generation, directory creation, file generation, and folder automation. Disclaimer: I won't be sharing the complete project code directly. Instead, I want to foster discussion and collaboration. For Beginners: Use this as a guide to build your own project and join the conversation. I'm happy to help with any learning challenges you encounter. For Experienced Developers: Please share your insights and discuss potential improvements. Our journey doesn't end here. In this part, we'll delve into some of the challenges we've faced in previous part. Challenges: Token exhaustion: Large files exceeding token limits. Code skipping: The LLM omitting parts of the implementation. Inconsistent code generation: Function naming inconsistencies acr...