This is a part of my “AI in SF” series, where I share real AI engineering workflows of SF startups. I recently interviewed an engineer from Pallet (they’re hiring - more on that at the end). Here’s an insight that will make your AI-generated code better.

Most developers use Cursor like expensive autocomplete. They let it generate whatever code it wants, fight with inconsistent outputs, and spend more time debugging AI mistakes than they save.

There’s a better way. During my interview with Vidhur from Pallet, I learned about a simple technique that made their AI-generated code dramatically better: the “gold standard” file approach.

Social media is full of people showing off their perfect little demo apps claiming AI is revolutionary, meanwhile, AI keeps suggesting fixes for files that don’t exist or rewrites working code into broken messes.

Does that sound familiar?

Here’s the thing — the “vibe coders” are not wrong about AI being powerful. They’re just not dealing with what you’re dealing with.

You’re working on a real codebase, with real dependencies, real business logic, and real users. Real codebases are messy.

I spent ten years of my life running a development agency, and Cursor has legitimately saved me weeks of work, but only after I stopped expecting it to just “figure things out.” Now, I’m going to share with you the workflow that made Cursor work for me on complex projects.

Reddit discovered the funniest thing in tech this week, and it shows exactly how broken the AI narrative is.

The newly released GitHub Copilot agent was given permission to make pull requests on Microsoft’s .NET runtime, and the results couldn’t be funnier.

The AI confidently submitted broken code, while human developers patiently explained why it didn’t work. Over and over again, for days.

AI code generation is error-prone. Why, then, are programmers still using it?

Everyone from YC partners to Fiverr’s CEO has been proclaiming that “90% of code is AI-generated” or that they’re becoming “AI-first” companies.

The subtext they’re forcing on us is clear: programmers who don’t embrace AI will be left behind.

But after two years of daily AI coding — from the earliest Cursor version to the latest agentic tools — I’ve uncovered the truth: AI coding tools are simultaneously terrible and necessary.

MCPs are a way for AIs to interact with the outside world. An MCP can allow AI to read emails, post tweets, message your friends, and much more.

We are used to interacting with the digital world via apps and windows—but MCPs enable an AI to do everything that humans do, without using any apps.

Here’s a quick guide on setting up and using your first MCPs in Windows.

You know by now that AI can dramatically speed up your development process (when used correctly.)

But the key is knowing how to communicate with the AI properly.

Here’s my collection of prompts that actually work in real-world scenarios.

The first time I encountered Big Tech was at age 15 when I won Google Code In. They flew me and my family to San Francisco and showed us around the Googleplex. I arrived with wide eyes, eager to see where the “smartest people in the world” worked.

But, what I found… disturbed me.

Everyone wore the same badges, slept in nap pods, played the same games, and ate at the same cafeterias. I couldn’t escape the realization that I was looking at a daycare for adults.

That day, I silently promised myself I would never work in such an environment.