Feeling left behind with the AI hype?
Move Beyond Crap Code and Confusion with AI Tools
In just a few hours it will all make sense, and you will be stoked.
Frustration is easy when you're just starting out
You know AI could make you more productive and help with coding, writing, and research - but there's this nagging worry about getting it wrong. What if you mess up a project because you trusted AI too much? What if you ask your AI tool something and it makes you look clueless in front of your team?
The tutorials are either too basic ("AI is amazing!") or dive straight into technical jargon about neural networks and transformers. You're stuck in this frustrating middle ground where you want to step into the future, but you also don't want to screw anything up.
The gap between what AI promises and what you can actually deliver feels intimidating, and you're not sure how to bridge it safely.
The fear of being left behind is real, but so is the fear of jumping in too fast
You're a solid developer. You've got skills, experience, and you deliver quality work. But there's this tension between wanting to embrace the future and being scared of messing up what's already working. What if AI writes buggy code that breaks production?
What if you become too dependent on it and lose your edge? What if you invest time learning these tools and they turn out to be just hype? The uncertainty is paralyzing, and every day you don't make a decision feels like you're either missing out on a game-changer or dodging a bullet.
I've been through this. I went from uncertainty to inspiration.
Just over two years ago, when GitHub Copilot was in pre-release, I was asked to try out the VS Code integration and give some feedback. I hated it. It constantly interrupted me, broke my concentration, and was less than useful.
In early 2024 I began working with the VS Code team directly, helping them evolve Copilot through everyday use, trial and error, and blind exploration into "what's possible".
Eventually, I began to see patterns forming. Ways to use Copilot and other emerging AI tools for more than just code generation. Pretty soon I was seeing overall gains with my own work as well as internal engineering teams.
The key wasn't just learning the tools. It was discovering the patterns through hands-on exploration: how to structure problems, write effective prompts, review AI output, and integrate AI into development workflows.
A safe, confident approach to stepping into the AI future.
I have wrapped up everything I've learned over the last 2 years into a single workshop that will help you onboard into the world of AI without losing your mind:
Simple confidence-building wins so you can see AI's value without the fear, which means you'll actually embrace it instead of avoiding it due to uncertainty and misinformation. AI can be a
powerful tool, find out why.
Context and reliability techniques. Avoiding crap code is easy if you take the time, upfront, to let your AI tool know what you expect. This can go well beyond words in a markdown file, you can use actual code, SQL, or JSON files too.
Things other than code generation. Real efficiency gains can be realized with things other than code generation, including scripting and utility functions, Git commit message generation and more. You can even troll your colleagues.
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Code review that builds your confidence in the tooling. Yes, you can review the code yourself or ask a colleague to help out, but there are ways to get the tool to review itself, saving you even more time.
Putting handcuffs on the LLM with instructions and templates The simplest, most powerful technique that people don't know about. Tell the LLM precisely what you want to see and how you want it written, and never receive crap code.
Testing strategies that help, not hinder Build your test suite quickly using a combination of instructions and templates, but write the actual implementation yourself. LLMs are brilliant at helping you test your code confidently.
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