Tech firm fined $1.1m by California for selling high-school students’ data
I agree with Brian Marick’s response
No such story should be published without a comparison of the fine to the company’s previous year revenue and profits, or valuation of last funding round. (I could only find a valuation of $11.0M in 2017.)
We desperately need corporations’ attitudes to shift from “lawbreaking is a low-risk cost of doing business; we get a net profit anyway” to “this could be a death sentence.”
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Charity Majors gave the closing keynote at SRECon last year, encouraging people to engage with generative AI.
If I was giving the keynote at SRECon 2026, I would ditch the begrudging stance. I would start by acknowledging that AI is radically changing the way we build software. It’s here, it’s happening, and it is coming for us all.
Her agenda this year would be to tell everyone that they mustn’t wait for the wave to crash on them, but to swim out to meet it. In particular, I appreciated her call to resist our confirmation bias:
The best advice I can give anyone is: know your nature, and lean against it.
- If you are a reflexive naysayer or a pessimist, know that, and force yourself to find a way in to wonder, surprise and delight.
- If you are an optimist who gets very excited and tends to assume that everything will improve: know that, and force yourself to mind real cautionary tales.
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In a comment to Kief Morris’s recent article on Humans and Agents in Software Loops, in LinkedIn comments Renaud Wilsius may have coined another bit of terminology for the agent+programmer age
This completes the story of productivity, but it opens a new chapter on talent: The Apprentice Gap. If we move humans ‘on the loop’ too early in their careers, we risk a future where no one understands the ‘How’ deeply enough to build a robust harness. To manage the flywheel effectively, you still need the intuition that comes from having once been ‘in the loop.’ The next great challenge for CTOs isn’t just Harness Engineering, it’s ‘Experience Engineering’ for our junior developers in an agentic world.
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In hearing conversations about “the ralph loop”, I often hear it in the sense of just letting the agents loose to run on their own. So it’s interesting to read the originator of the ralph loop point out:
It’s important to watch the loop as that is where your personal development and learning will come from. When you see a failure domain – put on your engineering hat and resolve the problem so it never happens again.
In practice this means doing the loop manually via prompting or via automation with a pause that involves having to prcss CTRL+C to progress onto the next task. This is still ralphing as ralph is about getting the most out how the underlying models work through context engineering and that pattern is GENERIC and can be used for ALL TASKS.
At the Thoughtworks Future of Software Development Retreat we were very concerned about cognitive debt. Watching the loop during ralphing is a way to learn about what the agent is building, so that it can be directed effectively in the future.
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Anthropic recently published a page on how AI helps break the cost barrier to COBOL modernization. Using AI to help migrate COBOL systems isn’t an new idea to my colleagues, who shared their experiences using AI for this task over a year ago. While Anthropic’s article is correct about the value of AI, there’s more to the process than throwing some COBOL at an LLM.
The assumption that AI can simply translate COBOL into Java treats modernization as a syntactic exercise, as though a system is nothing more than its source code. That premise is flawed.
A direct translation would, in the best case scenario, faithfully reproduce existing architectural constraints, accumulated technical debt and outdated design decisions. It wouldn’t address weaknesses; it would restate them in a different language.
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In practice, modernization is rarely about preserving the past in a new syntax. It’s about aligning systems with current market demands, infrastructure paradigms, software supply chains and operating models. Even if AI were eventually capable of highly reliable code translation, blind conversion would risk recreating the same system with the same limitations, in another language, without a deliberate strategy for replacing or retiring its legacy ecosystem.
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Anders Hoff (inconvergent)
an LLM is a compiler in the same way that a slot machine is an ATM
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One of the more interesting aspects of the network of people around Jeffrey Epstein is how many people from academia were connected. It’s understandable why, he had a lot of money to offer, and most academics are always looking for funding for their work. Most of the attention on Epstein’s network focused on those that got involved with him, but I’m interested in those who kept their distance and why – so I enjoyed Jeffrey Mervis’s article in Science
Many of the scientists Epstein courted were already well-established and well-funded. So why didn’t they all just say no? Science talked with three who did just that. Here’s how Epstein approached them, and why they refused to have anything to do with him.
I believe that keeping away from bad people makes life much more pleasant, if nothing else it reduces a lot of stress. So it’s good to understand how people make decisions on who to avoid.
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