Category: Found
Links, news articles, clippings, etc. – classic blog-style.
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Enterprise Java apps are much more than just code, AI benchmarks don’t measure the Java SDLC enough
There’s countless dependencies in other frameworks and libraries, plus the build pipeline and even production platform are part of the app. Enterprise Java applications frequently span hundreds or thousands of modules connected through Maven or Gradle builds, Spring dependency injection, Jakarta EE services, annotation processors, generated code, legacy XML configuration, container orchestration, proprietary build plugins,…
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find your customer
Compensation theater: Everyone cares about the compensation (base salary, bonus, and stock). That’s the data they care about, but where did this pile of compensation come from? Who decided how big it is? And how is it allocated? It is a long, complicated, and large process that began over a year ago. Every meeting I…
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why big companies build their own software, and hire consultants to do it
But selling anything to big enterprises is hard. You are asked to meet with a dozen people in the organization, spending hours and hours trying to explain and persuade. Half the people you meet with are skeptics who are going to argue that your service won’t work. The other half are going to argue internally…
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here come the normies
Consulting giant Accenture is trying to figure out how to stop non-technical workers from blowing through companies AI token budget on trivial tasks like converting PDFs to presentation slides, according to leaked audio obtained by 404 Media. Across the industry Accenture is seeing “soaring token spend,” according to the audio. To consider: There is a…
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Harness engineering is the new DevOps
Are we going to see harness engineering teams? Yes: these are already emerging. It’s too early to say, but that feels wrong in the same way that DevOps teams were a misunderstanding of DevOps-the-movement. My belief is that while there is friction in having every team create their own harness, that friction is generative and…
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training your replacement
Yes: the work is moving from writing code to specifying, verifying, and curating knowledge. But: the code is the better spec. Their point: code is the only artifact that’s unambiguous, executable, and – this is the big one – deterministic. A prose spec is fuzzy and an agent will interpret it a little differently every…
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potential fatigue
The old system said: ‘Obey.’ The new system says: ‘Realize your potential,’ which can be a much crueler thing because potential’s infinite, and people, last time I checked, are not. There’s no stopping point in an achievement society. ‘Potential’ is the modern world’s term for whatever you haven’t done yet and, frankly, should already kinda…
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How NatWest is using AI
The NatWest CIO goes over how they’re using AI with some use cases. Some: “Today at NatWest, over 40% of all code is either AI-generated or AI-assisted.” AI use: “If you’re a relationship manager, if you’re a branch manager, or you’re sitting as a teller in the branch, whatever that role is, you’ve got better…
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🤖 Ten do’s and don’ts for LinkedIn posts, according to actual research, based on pre-slop posts
Summarized by AI, July 2, 2026. Original” What Users Post and Engage With on LinkedIn: A Mixed Methods Study Most LinkedIn advice comes from people whose job is giving LinkedIn advice, which should make you suspicious. So here’s something rarer: an actual peer-reviewed study of 1,001 individual users’ posts, coded by topic and compared on…
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don’t keep PII
high-value credential – a passport – was used in an ancillary low-value authentication system: ID verification for cannabis dispensaries. And it’s the low-value system that got hacked, putting the high-value credential at risk.” It’s always best to delete whatever you no longer need. 🔗 One Million Passports Leaked Online
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Reducing token usage
Better Defaults (not Usage Caps) — Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage…
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“this is the voice of American business that is being channeled through me”
“Livid” was the word he used to describe enterprise customers who are “paying for tokens that create no value,” while handing over their data to the major firms and, in the end, taxing the public who will end up paying for it in some form. The reason, he ventured, was the “models have completely… irresponsibly…
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🤖 Findings and CIO Retention Guide: Gender, Priorities, and Exit Triggers in High Tech
Original: Beyond the Pipeline: A Gender Lens on Priorities and Exit Triggers in the High-Tech Industry by researchers at The Academic College of Tel Aviv-Yaffo, Communications of the ACM. Summarized by Claude AI on July 2, 2026. The Findings Men are far more committed to staying. 44.9% of men selected “no intentions to leave” vs.…
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Enterprise AI Gets Uber-ed, Smaller Models Cut Energy 90%, and Stop Building Innovation Labs – Related to your interests, Wednesday
Also: Target’s RAG pipeline, the 13-person B2B buying group, Uber on AI dev productivity, and prompt injection as role confusion. ICYMI Original content published since last time. War for Talent – Software Defined Talk #578 – “This week, we discuss Fable follow-up, Gartner’s AI Governance MQ, and the war for AI talent. Plus, Matt and…
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Elusive Enterprise AI ROI: bring in the FDEs
Enterprise AI needs a lot of custom fitting and one off integration, so you need a lot of post-sales engineering. On the one hand, this is lock-in in the making, but lock-in to your own, stack. You build a stack that is unique to you. On the other hand, it means you get exactly what…
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From the school of culture eats technology for breakfast
Yes: The environments capable of creating meaningful change are almost always the ones that shorten the distance between assumption and evidence – ensuring that learning happens before institutions become too committed to decisions that are difficult to reverse. But: The question has never really been whether governments need experimentation. It is whether they are willing…
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