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2h ago · 16 min read · The Wrong Measure Software doesn't fail when it stops working. It fails when the cost of keeping it working exceeds what anyone is willing to pay. That distinction sounds simple. Its consequences are
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3h ago · 11 min read · Oracle observability post #2 — picking up from where we left off on the configuration gaps most teams miss after an OEM 24ai install. There's a debate that comes up in almost every Oracle architectur
Join discussionI do fancy stuff with Oracle APEX #orclapex
12 posts this monthAI Governance Platform
3 posts this monthBuilding data layer of AGI
2 posts this monthHey everyone, I am a developer based in India
1 post this monthI do fancy stuff with Oracle APEX #orclapex
12 posts this monthAI Governance Platform
3 posts this monthBuilding data layer of AGI
2 posts this monthHey everyone, I am a developer based in India
1 post this monthIn my view, both are based on the same automation principles. The main difference is the purpose. Gaming automation is usually focused on improving or simplifying gameplay, while productivity automation is designed to solve real-world problems, save time, and increase efficiency in business or personal workflows. Technically, both rely on predefined rules, triggers, and automated actions, but their end goals are different.
This is a great breakdown of how design patterns shift the focus from 'making it work' to 'making it maintainable.' I’ve found that the real shift happens when you stop seeing patterns as just theoretical structures and start seeing them as solutions to specific 'code smell' scenarios. For anyone currently digging into these patterns, I’ve been working on a tool that summarizes technical deep-dives and video documentation into concise, readable formats. It’s been helping me get through architectural documentation much faster—you can check it out at ytskim.com. Out of curiosity
Really like how you framed the shift here — the "beyond prompts" point lands. The part I'd add from my own experience: the hardest part of context engineering in practice isn't deciding to use context, it's the unglamorous structuring decisions — chunk ordering, what to evict when the window fills, where to place the question relative to the evidence. That's where I've seen output quality actually move, often more than prompt wording. I wrote up a fuller breakdown of where prompting stops being enough and context takes over here, which complements your piece nicely: <a href="https://scienti
We've kept the runnable check as the load-bearing part of our specs too. Without it the agent grades itself generously on what "done" means. Have you found agents actually read the [A] layer first when prompted, or does it need a hook to enforce the order?
The missing piece for me is a repo contract, not just repo metadata. Something explicit that says: what commands are allowed what paths are off-limits which verifier actually counts as done what proof a retry needs before it can continue That turns a repo from 'maybe safe' into something an agent can operate against without folklore. That's a big part of the direction we've been taking with MartinLoop.
Every week, there’s a new JavaScript framework trending on Twitter. As a student developer, it’s easy to feel FOMO. You start learning React, switch to Next.js, see a post about Svelte, and suddenly y
Excellent breakdown. I always tell beginners to focus on problem decomposition—taking a massive, vague feature request and breaking it down ...
It’s also a matter of career longevity. The developers who survived the transition from jQuery to Angular, and then Angular to React, were t...