Category: Recommended Read

All kinds of posts mainly to Arctiles or other blog posts that I find worth reading

AI Won’t Turn Everyone Into Developers- Because Most People Don’t Want to Be Developers

The AI hype claims that LLMs will make everyone a coder. I say: that’s pretty much BS. Most people don’t want to build software. They want their problems solved, preferably without lifting a finger.

Joan Westenberg nails this so well in her recent article: The “everyone will code” myth ignores decades of proof. We’ve had WordPress (since 2003) and desktop publishing tools (since the 1980s), yet most still pay for solutions or use templates.

The real shift? AI will make existing tools smarter — not turn everybody into vibe-coders.

Read her article that’s just so spot on: https://www.joanwestenberg.com/ai-twitters-favourite-lie-everyone-wants-to-be-a-developer/

14. February 2026
AI Won’t Fix Bad Software Teams

What do you do an Saturday when you don’t feel well to go out for some activities? Right! Let’s read about Software Engineering and AI!

Yesterday I read the complete series of the 22 blog posts listet on The AI-Ready Software Developer – Index – Codemanship’s Blog.
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11. January 2026
Spec-first Agentic Development is not Vibe Coding

Not even two weeks ago I wrote about “Reproducable Vibecoding” and that the specification as a permanent context to document all decisions is important.

I just stumbled across the article “Notes on Six Months of AI-Enabled Building” by Isaac Flath. There are a couple of good quotes in there, especially in the chapters “Your Thinking Style Determines Your Success“
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9. January 2026
The Malicious (Coding) Agent …
I just watched Agentic ProbLLMs: Exploiting AI Computer-Use and Coding Agents from Johann Rehberger on the #39c3. He shows quite impressive how the future threat model looks like, the more AI Agents are deployed.

In his talk he demoes a couple of attacks that were applied by using agents. I don’t want to summarize the talk here (you might want to read the heise online article instead), but it is ways beyond “simple prompt injection”!

But my most “aha”-moment was the statement to treat an Agent as a Malicous Internal. Which is probably the worst scenario you want to deal with. Usually you would like to trust your co-workers and not treat them as if they could stab you in the back while smilig at you.

Anyways, I’m pretty sure the technology will evolve into more secure ways. But it will also stay as a new way of attack in the future. I’d recommend checking it out!

https://media.ccc.de/v/39c3-agentic-probllms-exploiting-ai-computer-use-and-coding-agents

PS: I’d embed it here, but this obviously requires some CSS / WordPressTheme-magic …

Related links:
- 39C3: Power Cycles – media.ccc.de (all videos)
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31. December 2025
Recommended read: What Actually Makes You Senior

On Mastodon, I just found a link to the Terrible Software Blog. (Definitely a blog to follow, I just added it to my RSS reader).

I found this article pretty good: What Actually Makes You Senior.

But if you strip away the title, the salary, and the years of experience, there’s one core skill that separates senior+ engineers from everyone else: reducing ambiguity. Everything else flows from that.
Matheus Lima

It’s about the ability to handly fuzzyness and derisking projects. I really like the “they first make the problem clear. Then, and only then, they go to solve it.”

Check it out: https://terriblesoftware.org/2025/11/25/what-actually-makes-you-senior/

25. December 2025
How to write great agents.md(s) – A recommendation from GitHub

GitHub’s new guide, How to write a great agents.md: Lessons from over 2,500 repositories, pulls lessons from over 2,500 repositories to show how to document AI agents effectively. It’s not just about clarity but also about making collaboration, reproducibility, and scalability possible.

The guide breaks down how to structure agents.md files for real-world utility. It highlights common mistakes and explains why solid documentation is the backbone of any successful AI project. Whether you’re a developer, DevOps engineer, or just curious about AI tooling, this is a practical roadmap.

For anyone serious about AI development, this is a resource worth keeping: https://github.blog/ai-and-ml/github-copilot/how-to-write-a-great-agents-md-lessons-from-over-2500-repositories/

23. December 2025
Recommended Read about a Microservices Reality Check

Docker’s recent article “Do You Really Need Microservices?” delivers a good dose of pragmatism with regards to Microservices. The article doesn’t dismiss microservices outright— it acknowledges their value at massive scale — but it also hightlights an uncomfortable truth: most teams simply don’t operate at that scale.

What I particularly like are the warnings about hidden costs, the kind that reveal themselves after committing to the architecture and having to operati it over years and through a lot of releases. Operational overhead, debugging “fun” and the complexity of managing distributed systems aren’t just footnotes — they’re a true burden for teams without the resources to handle them properly.

Check it out: You Want Microservices—But Do You Need Them? | Docker

22. December 2025
The Cost of Going All-In on AI

On Mastodon, I just came across “I Went All-In on AI. The MIT Study Is Right.” from Josh Anderson. He spent three months building a product using only AI-generated code. The result? A working product, but also a dangerous realization: He no longer fully understood his own creation. When a small change was needed, he hesitated.
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7. December 2025
LLM Update in Production: When Prompts Fail — and What It Means for Your Applications

t3n recently wrote that OpenAI’s GPT 5.1 update might come with a surprise to desktop users: previously reliable prompts no longer behave as expected. While this may be just a minor annoyance in day-to-day chat interactions, think about what that means in production environments.
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6. December 2025
The Software Engineering Challenge Behind Effective RAG

Retrieval-Augmented Generation (RAG) is frequently marketed as a simple solution for grounding AI responses in real-world data. It sounds straightforward & easy: connect a vector database to a language model, pipe in your data, DONE! But maybe it might be just a little harder than that …
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4. December 2025

Category: Recommended Read

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