If the arrival of GenAI really marks the end of low-code (“as we know it”), there’s not much else we can do but trying to survive in a code-first world. Luckily, the disruption is at least as big for those professional roles that already used to work code-first. There’s an interesting thread about this on r/developersIndia subreddit that captures that “end of pro-code as we know it” side perfectly:

It’s not the case of every non-coder starting to work like the computer programmers have. LLMs are now injected as a ubiquitous layer between humans and the computer. Today, they seem to operate mainly as a translation service between the two, turning prompts into programming languages that existed way before ChatGPT. At a later point, most likely the code itself will also evolve into something quite different than what was used to create all the software we use today - which was written by humans.

Everyone who creates things via computers will get their cheese moved. I’m sympathetic to the pain expressed by these software developers who are now being pushed to “just use Cursor” instead of thinking about and learning from the code. It’s somewhat similar to how I am typing this sentence by hand now, even though ChatGPT could have created a full newsletter issue by the time I get to the end of it.

This technology now exists and is widely available, so no point in wishing it didn’t. I write critical pieces about how the marketing message in particular around Copilot has been less than truthful. That doesn’t mean I would hope to turn back time. No, I just wish all the AI bros and LinkedIn thought leaders would spend less time generating BS and being honest about what actually works today and what remains marketing fiction for now.

Using the powers of GenAI, I’ve now been able to work with things that involve code in a way that would have been impossible three years ago. It involves a lot of trial and error, success and failure. Today, I want to share some of the experiences I’ve had with code. As a person who realized already during my days in the university that regular programming was not what my brain was designed for, it’s been interesting to see how LLMs can now compensate for this part of my cognitive skills.

recently, I’ve written about new M365 Copilot agents like App Builder and Workflows (Frontier). Most of the new vibe coding tools in the MS BizApps space are not yet available for true production use in customer projects. Therefore, most of what I have been leveraging LLMs in relation to “coding” has been about designing and implementing Power Apps canvas apps and Power Automate cloud flows.

Whenever the business logic of a solution becomes more complex, the bliss of low-code starts to fade away. Retrieving data from APIs and manipulating them into suitable collections or arrays is, at the end of the day, more about traditional coding logic than anything a GUI from Microsoft could help you with. That’s where resorting to assistants like ChatGPT and Claude make sense, and it’s where I’ve leveraged them the most as part of my consulting business and customer projects.

First, I do love how LLMs can explain a piece of Power Fx or cloud flow WDL / OData filters you paste into them. This is very helpful especially when returning to a certain solution artifact after a break and needing to catch up on what I had built before resetting my brain. In the end, the kind of logic we mostly deal with in business applications is hardly rocket science, which means the current AI tools are quite fluent in analyzing them.

When it comes to suggesting solutions on what/how to build, the same models tend to forget the constraints of Power Apps and Power Automate. All the time, over & over again. This wouldn’t be so frustrating if the tools could directly manipulate the code and then run tests as coding agents to see how it works. In Power Platform, you mostly end up being the manual operator of the prompt-copy-paste-run-error-copy-paste-prompt loop. It gets frustrating pretty fast.

I’ve tried to educate my AI assistants by instructing them what to do. Recently, when Claude skills were launched, I tested whether I could get AI to first crawl for the common issues and then construct a cheat sheet for itself. The resulting markdown file looked nice and after a few rounds of editing it I was happy to upload the new skill to my assistant.

In theory, this sounds like the optimal path forward. Providing small yet persistent instructions to AI, with the ability to append them as you go, rather than trying to do proper fine tuning of LLMs.

In practice, it’s just like everything else with LLMs. They are not machines that follow exact orders, rather they statistically predict the next token. Which means that Claude is never guaranteed to reference the skills, even when it logically should. You can try and interrogate your AI assistant on “did you or did you not follow orders”, but the answer to that won’t be reliable either. So, we’re left with hoping that the code generated by AI applies to the rules of the game.

It really is a jagged edge when it comes to what AI can and can’t do. Some users manage to build and maintain entire fake religions like spiralism inside the mind of ChatGPT as a collective effort. At the same time, trying to get your AI assistant to remember what the syntax and limitations of a tool like Power Automate are feels like a futile struggle. It still feels more like casting spells than using regular tools.

I have been using WordPress as the blogging platform since 2008. When I stepped down both from the Microsoft MVP program as well as my co-founder role in my previous company, I wanted to try something different. One of those steps was starting this newsletter on beehiiv. But I also wanted to explore alternatives to WP.