My frustrations with my AI Buddies

Using AI code generation tools can feel like having a supercharged assistant—they can quickly draft code snippets, suggest solutions, and even build entire components. But as helpful as they are, I’ve found some frustrating limitations that often slow me down rather than speed me up.

One of the biggest issues I run into is that AI tools can’t remember my personal coding style. Every developer has their own way of structuring code, naming variables, and organizing logic. I might prefer concise functions or certain design patterns, but AI generators typically offer generic solutions that don’t align with how I like to write code. This means I often spend extra time rewriting or tweaking the output to match my style, which defeats the purpose of using these tools for efficiency.

Another pain point is that AI tools don’t remember which frameworks or CSS libraries I’m using. If I’ve chosen React with Tailwind CSS for a project, the AI might still suggest solutions using plain CSS or even other frameworks entirely. It’s frustrating to constantly correct the suggestions, reminding the tool that I’ve already made specific technology choices. This disconnect slows down development and forces me to double-check everything for compatibility.

I also like using the Context API in React, particularly putting fetch calls directly in the Provider for better encapsulation and centralized data management. However, AI often suggests creating standalone service files instead, which doesn’t align with my approach. This mismatch forces me to rewrite the AI-generated code to fit my preferred architecture.

Then there’s the issue of file organization. I like to use a feature-based folder structure, keeping related components, hooks, and utilities together. But AI tools have no sense of where files should go. They’ll suggest creating a new file but won’t place it in the right folder or follow my project’s organization pattern. This leads to a messy file structure that I have to clean up manually, making the process more cumbersome.

Overall, while AI code generation has a lot of potential, it lacks the memory and context needed to be truly helpful. Without the ability to learn and adapt to my style, tools, and project structure, it often creates more work than it saves. Until these tools can bridge that gap, they’ll remain more of a novelty than a reliable development partner.

My AI Buddies

Generative AI has completely changed how I approach software development. From writing snippets of code to automating repetitive tasks, these AI tools have become my go-to partners in the coding process. Today, I want to share how three of my favorite AI agents—GitHub CoPilot, Cline, and Bolt—have enhanced my workflow.

My Experience with GitHub CoPilot in VS Code

GitHub CoPilot, developed by GitHub and OpenAI, has been a game-changer for me. Integrated seamlessly into Visual Studio Code, it feels like having a helpful coding buddy suggesting lines of code and even entire functions as I type. Whether I’m working with PHP or React, CoPilot provides intelligent, context-aware recommendations that save me time and help me break through coding blocks. It’s especially handy for automating boilerplate code and exploring new frameworks or libraries without needing to constantly check documentation.

How Cline in VS Code Simplifies My Workflow

Cline is another AI-powered tool that has made coding more intuitive for me. While CoPilot helps by suggesting code, Cline enhances my development workflow by offering smarter interactions directly within VS Code. I can ask Cline to do something in a code file using English, and it can run complex terminal commands, navigate my file system, and automate build processes. This has been a huge help in bridging the gap between coding and command-line tasks, allowing me to stay focused on building instead of getting stuck on syntax-heavy commands.

Why I Love Using Bolt.new and Bolt.diy

Bolt has been a fantastic addition to my toolkit, with its two products: Bolt.new and Bolt.diy. Bolt is all about rapid prototyping. I can have an idea for a web app and turn it into a working prototype in minutes. It generates functional codebases, user interfaces, and basic logic structures, Bolt.diy van even import an existing codebase and I can tell it to add features or make modifications. It strikes the perfect balance between automation and creative control for me.

Bringing It All Together

Each of these AI tools brings something unique to my workflow:

  • CoPilot boosts my productivity by suggesting relevant code in real-time.
  • Cline streamlines command-line tasks, making my development process smoother.
  • Bolt helps me accelerate prototyping and custom development with smart automation.

By integrating these tools into my daily work, I’ve been able to cut down on development time, dive into new technologies more easily, and focus on solving complex problems instead of getting stuck in routine tasks. Whether you’re a seasoned developer or just starting out, these generative AI agents could become your new favorite coding companions.

I truly believe the future of coding is more collaborative, with AI working alongside us to unlock greater creativity and efficiency. It’s an exciting time to be part of the software development world!

Have you tried any of these tools yet? I’d love to hear how they’re working for you!

Impact Of Ai On Software Engineering

How Generative AI is Changing Software Engineering

Generative AI is making big waves in software engineering, changing how we create, manage, and improve code. From tools that handle entire codebases to smarter ways of working, AI is becoming a key player in the field. Let’s dive into three big ways it’s transforming things: AI-managed codebases, AI-driven best practices, and the boost in productivity once developers get the hang of it.

AI-Managed Codebases: Letting AI Handle the Heavy Lifting

I can hardly contain my excitement about the rise of AI-managed codebases! The idea of tools like Bolt.new taking care of entire systems feels like stepping into a future we’ve only dreamed about. These platforms are doing things we once thought impossible, like:

  • Building apps from scratch based on what users need.
  • Keeping code optimized for speed, security, and scalability.
  • Updating old code and fixing issues without needing a person to step in.

This is so much more than just a technical improvement; it’s a complete shift in how we think about development. Imagine having an AI partner that keeps your code clean, identifies and fixes bugs before they cause trouble, and always ensures everything is up-to-date. It’s freeing us from the tedium of maintenance and giving us more time to focus on creative and meaningful challenges. The possibilities here are limitless, and I can’t wait to see how far we can push this technology!

AI Best Practices: Developers Still Call the Shots

As incredible as AI is, it’s important to remember that developers are still the ones in charge. AI tools might be smart, but they need our guidance to make sure they’re doing the right thing. These tools are fantastic at spotting patterns and coming up with solutions, but we’re the ones who ensure:

  • The code matches the project’s goals and user needs.
  • Ethical issues like fairness, privacy, and security are addressed.
  • The solutions work in real-world scenarios.

It’s like having an exceptionally talented assistant who can handle the heavy lifting, but we’re still the directors of the project. Developers bring creativity, vision, and judgment to the table, and that’s something AI can’t replicate. This partnership allows us to achieve more than ever before while staying true to what’s important.

A Productivity Boost: Unlocking New Potential

The speed and efficiency generative AI brings to the table are nothing short of thrilling. For developers who learn how to use these tools, the productivity gains are incredible. AI can:

  • Cut down the time spent fixing bugs and testing.
  • Make onboarding new team members a breeze with automatically generated documentation.
  • Help developers try out new ideas quickly by handling repetitive setup tasks.

I’m amazed every time I see how much faster projects move when AI is involved. Tasks that used to take days can now be done in hours, giving teams the freedom to focus on what really matters: innovation and problem-solving. This is the kind of transformation that changes not just projects but entire industries.

But to fully embrace this potential, developers need to:

  • Learn how to give clear instructions to AI.
  • Carefully review AI-generated code to make sure it fits the project’s needs.
  • Keep up with the latest advancements in AI tools and techniques.

The Future of Software Engineering

Generative AI isn’t just another tool; it’s opening the door to a whole new way of thinking about software engineering. It’s letting us dream bigger, move faster, and achieve more than we ever thought possible. But even with all this power, the human element remains essential. Developers are still the ones steering the ship, setting the goals, and making sure we stay on course.

The future of software engineering is incredibly exciting. We’re moving toward a world where developers spend less time on routine tasks and more time shaping strategies, solving problems, and creating bold new ideas. Generative AI isn’t here to replace us; it’s here to amplify what we can do. I’m so excited to see how this technology will evolve and to be part of this journey into a new era of innovation.