I recently watched Jack vibe code a new feature for goose, and it was inspiring to see how he approached it. There was no overthinking, no perfectionism - just curiosity and a willingness to experiment. Here are some of my takeaways from watching him work:
Embrace the Learning Mindset Over Success
Instead of fixating on immediate results, challenge yourself daily with AI tools and enjoy the process of learning. Regularly read AI research to stay inspired and informed about new developments. When using AI to generate code, resist the urge to peek at the output before submitting. Instead, trust the process and focus on the journey rather than the outcome.
Start with Natural Language, Not Technical Specs
Begin by describing what you want in plain language, rather than rigid technical requirements. You can even dictate your prompts or use conversational language to interact with the AI. When appropriate, let the AI make decisions about technology choices, like which programming language to use. For example, you might say, "build a Go game with cool aesthetics," and let the AI figure out the details.
Use Visual Communication
Sometimes visuals communicate more effectively than words. Take screenshots of issues and paste them into your AI tool to provide clearer feedback. When something isn't working right, showing rather than just telling can help the AI understand the problem faster.
Iterate Fearlessly
When you encounter errors, don't give up. Just say "retry." Embrace the iterative process of prompting, observing, refining, and repeating. Every failure is a learning opportunity, not a dead end, so keep experimenting and adjusting.
Let AI Handle the Architecture
Focus your energy on defining the "what" and let the AI figure out the "how". Avoid overprompting. Sometimes the best results happen when you step back and give the AI space to make technical decisions. Your role is to provide creative direction while trusting the AI to handle the structure.
Build in Public and Share
Normalize the process of coding with AI by building in public. Share your experiments, even the messy ones, to inspire and educate others. Document your workflow and findings so the community can learn from your journey.
Test Continuously
Don't assume that something works just because it was generated. Always test new features immediately after implementation, and use real user interactions to guide your improvements. Continuous testing ensures your work stays reliable.
Develop Your AI "Taste"
Over time, you'll learn to recognize good versus poor AI output. Develop an intuition for when to accept what the AI gives you, when to push back, and when to ask for changes. Your creativity and judgment remain essential to achieving great results.
Use Context Strategically
Give the AI enough context to understand your goals clearly. Use memory or history features when available to maintain consistency across sessions. If a conversation gets too long or confusing, take a break to reset the context and start fresh.
Think Beyond Traditional Coding
Remember that AI opens up opportunities for non-coders to build meaningful applications too. Focus on solving problems rather than writing perfect syntax. Even without traditional coding skills, effort and thoughtfulness are still required to create something impactful.
Security and Quality Still Matter
AI doesn't replace the need for human oversight. Always review generated code for security vulnerabilities and ask the AI to analyze its own output for optimization opportunities. Never skip the crucial step of checking for quality and safety.
Build What Inspires You
Start with projects that genuinely excite you. Use AI as a way to explore ideas you might not have tackled otherwise. Let curiosity lead you and make your learning journey more engaging and meaningful.
Top comments (6)
best tips i can give you : Just don't.
This was a good read and sort of confirmed for me that I'm on the right track when using AI tools. Thank you for the insight!
The thing I see repeating in these tips is keep giving AI compagnies money until you have to take responsibility.
So instead of hiring overpriced consultants that avoid responsibility, just give prediction machine compagnies money. And instead of doing it on a project base do it company wide so that it is less noticeable that the bill is even higher.
Vibe coding can be a good tool is certain scenarios, but it should be a short process not a long term thing.
Great article, do you have a link to the video you watched?
he's my CEO. It was an internal session, sorry
And also please leak the api keys pleeeeees pls