Tips for Balancing Speed and Quality in AI Coding

Founders, If your engineering teams haven't yet embraced AI tools like ChatGPT, GitHub Copilot, or AWS Whisper, it's a critical time to reconsider. These tools are transforming the landscape of software development. As a seasoned developer, I’ve been using these AI tools daily. They're not just about coding faster; they're about coding smarter. My typical workflow involves starting with a detailed TODO comment to structure my code. Then, AI takes over, drafting both code and unit tests. I review and refine the AI-generated code, usually finding just a minor issue or two. The rest is efficiently covered by the AI-generated unit tests. This way, I can spend more time designing the software systems than typing the code, and I also enjoy a more holistic view but still keep myself in the coding details. 🚀 This approach has revolutionized my productivity. I've experienced a 10x increase! Complex projects that once needed a team are now manageable solo. I've become proficient in 10+ programming languages overnight, enabling me to pick the best tools for each project without the daunting learning curve. The quality of my work has improved dramatically, and I complete tasks faster and with higher quality. This efficiency gives me more time to learn, experiment, and expand my skill set. ⚠️ A word of caution: If your teams aren’t adopting this pattern, you risk falling behind. In this fast-paced tech race, competitors leveraging AI can move faster, innovate quicker, and deliver superior solutions. AI in software development isn't just the future; it's the present. It's time to embrace these tools and transform how we build, test, and refine our software. Let’s lead the charge in this AI-driven era! #ai #copilot #productivitytips #softwaredevelopment

I've spent the last few months deep-diving into the latest and greatest in AI. I've been coding for the first time in ages, and experimenting with everything I can get my hands on. It's been fascinating. The last time I felt this much excitment about a new technology was the first time I experienced the world wide web on Mosiac via a DEC terminal after sneaking into a computer lab at University of New Hampshire back in 1993. (I know I'm aging myself here!) Sasha and I have been vibe coding quite a bit to build live interactive prototypes and play with different ideas for Orbit.me. AI tools like Cursor and Lovable have been a huge accelerator, but, like many others, I've found there are many quality and consistency issues with the final output. What I found is that vibe coding can produce an 80% product. But the last 20% is the hardest part.  I can safely say that software engineering is not dead. The world still needs real software engineers to build finished, high-quality, and secure software products. AI accelerates and enhances software engineering, but does not replace it. Today I came across this insightful talk by Sean Grove of OpenAI. There are many great nuggets in this talk, but I found the comments on vibe coding interesting. He says we're doing it all wrong. We're producing code with a long series of prompts and then throwing away the prompts. This is like writing source code, compiling it to binary, then throwing away the source code. The series of prompts represents the specifications — a declaration of the outcome you are looking for. Instead, we should spend time carefully refining the specs to ensure the AI model generates the best output.  Using a series of prompts to produce code is always going to produce low-quality and highly varried output. In retrospect, it's obvious that focusing on the specs and agreeing upon the requirements up front is a valuable use of time. This has always been true in engineering, but we've somehow forgotten this best practice in the rush to use AI-driven coding tools to generate outputs as fast as possible. AI is changing how we execute, but the essential best practices are staying the same. What do you think? What has been your experience? https://lnkd.in/eU-K_9qx