1. 🔧 AI is a Tool
2. 🛠️ Exploration vs Implementation
3. 🤮 Everyone Hates AI Slop
4. 🌊 Adaptability
5. 🏋️‍♂️ Don’t Spoil the Mind
6. 🛣️ The Future of AI
7. ⚖️ Speed vs Quality

🔧 AI is a Tool

AI is a tool. Like every tool, its value depends on the person using it. It enables us to better delegate the busy work so we can focus on the real work.

• 👨‍💻 Software development is not writing code. It is building a correct model of a system. Code is the output of understanding.
• ✏️ Product Design is not creating UIs in Figma. It is understanding users, their problems, constraints, taste and tradeoffs to design the right experience. The interface is the output of that understanding.
• 🧑‍🎨 3D Design is not making 3D models. It is understanding space, function, constraints and style deeply enough to create content that feels right. The 3D models is just the output.

AI is leverage that can amplify human capability, speed and output. But the quality of the result depends on the person using it.

The goal is not to replace human expertise. The goal is to spend less time on busy work and more time on high impact work.

🛠️ Exploration vs Implementation

How we think about AI depends entirely on what mode we are in.

• ◀️ Exploration: Goal is to test, fail, understand and learn fast.
• ➡️ Implementation: Goal is to ship world-class products.

◀️ Exploration: Vibe Coding

In exploration mode we move fast, experiment freely, fail, understand and treat AI as an experimental tool that enables speed.

In AI Exploration you don’t need workflows, is just pure vibe coding/designing, quickly testing ideas to validate and learn and then throwing away the artifact but retaining the learning and insights.

In exploration, we let AI drive. We go with the vibes: prompt, generate, run, learn, understand, discard and repeat. We are not the architect here, and that is fine, because nothing ships. Vibe code freely in exploration. Never vibe code into production.

➡️ Implementation: AI Assisted Workflows

In implementation mode we are building for production, quality matters the most and AI usage needs caution, judgment and accountability.

• 📐 Architect: You design, AI implements. Keep ownership of the architecture. AI does the heavy lifting. Use engineering best practices like atomic iterations: break work into the smallest fully completable task and let AI execute it. One atomic task at a time, fully done and reviewed, before moving to the next.
• 🗜️ Compression: AI summarizes complexity. Use it to understand docs, research, codebases and errors faster. Use it to debug, review, refactor and document.