There’s a lot of engineers who will refuse to wake up to the revolution happening in front of them.
I get it. The denialism is a deeply human response.
So far the only output is the "How I use AI blogs", AI marketing blogs, more CVEs, more outages, degraded software quality, and not much of shipping anything.
Is there any examples of real products and not just anecdotes of "I'm 10x more productive!"?
The two main takeaways. Create a CLAUDE.md file that defines everything about the project. Have Claude feed back into the file when it makes mistakes and how to fix them.
Now it creates well structured code and production level applications. I still double check everything of course, but the level of errors is much lower.
An example application it created from a CLAUDE.md I wrote. The application reads multiple PDF's, finds the key stakeholders and related data, then generates a network graph across those files and renders it in an explorable graph in Godot.
That took 3 hours to make, test. It also supports OpenAI (lmstudio), Claude and Ollama for its LLM callouts.
What issue I can see happening is the duplication of assets in work. Instead of finding an asset someone built, people have been creating their own.
Even If I like this tech, I still dont want to support the companies who make it. Yet to pay a cent to these companies, still using the credits given to me by my employer.
1. There are actually less software jobs out there, with huge layoffs still going on, so software engineering as a profession doesn't seem to profit from AI.
2. The remaining engineers are expected by their employers to ship more. Even if they can manage that using AI, there will be higher pressure and higher stress on them, which makes their work less fulfilling, more prone to burnout etc.
3. Tied to the previous - this increases workism, measuring people, engineers by some output benchmark alone, treating them more like factory workers instead of expert, free-thinking individuals (often with higher education degrees). Which again degrades this profession as a whole.
3. Measuring developer productivity hasn't really been cracked before either, and still after AI, there is not a lot of real data proving that these tools actually make us more productive, whatever that may be. There is only anecdotal evidence: I did this in X time, when it would have taken me otherwise Y time - but at the same time it's well known that estimating software delivery timelines is next to impossible, meaning, the estimation of "Y" is probably flawed.
So a lot of things going on apart from "the world will surely need more software".
On the flip side, anyone who believes you can create quality products with these tools without actually working hard is also deluded. My productivity is insane, what I can create in a long coding session is incredible, but I am working hard the whole time, reviewing outputs, devising GOOD integration/e2e tests to actually test the system, manually testing the whole time, keeping my eyes open for stereotypically bad model behaviors like creating fallbacks, deleting code to fulfill some objective.
It's actually downright a pain in the ass and a very unpleasant experience working in this way. I remember the sheer flow state I used to get into when doing deep programming where you are so immersed in managing the states and modeling the system. The current way of programming for me doesn't seem to provide that with the models. So there are aspects of how I have programmed my whole life that I dearly miss. Hours used to fly past me without me being the wiser due to flow. Now that's no longer the case most of the times.
