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53m ago · 11 min read · Real-scale terrain, exact-ground physics, weather and a living crowd — on WebGPU. The hard problems, and the calls we made to solve them. OVGrid is a real-scale planet you explore as an avatar, in a b
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1h ago · 24 min read · And if you think you have time to "wait and see," you really, really don't. Let me tell you about a friend of mine. Seven years of experience. Mid-level engineer. $190K total comp at a solid company
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5h ago · 5 min read · A few years ago, I worked on a project where everyone was excited. The requirements looked solid. Users could register. Users could log in. Products could be added to the cart. Orders could be placed.
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6h ago · 8 min read · When I started building an application that extracts key concepts and vocabulary from a text, I thought I could handle personalisation through the prompt alone. It turned out that getting reliable res
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7h ago · 8 min read · Overview In a challenge lab you’re given a scenario and a set of tasks. Instead of following step-by-step instructions, you will use the skills learned from the labs in the course to figure out how to
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I do fancy stuff with Oracle APEX #orclapex
12 posts this monthEmpowering Tech Enthusiasts and Students with Informative DevOps Blogging 😊
1 post this monthSoftware for care providers
1 post this monthI do fancy stuff with Oracle APEX #orclapex
12 posts this monthEmpowering Tech Enthusiasts and Students with Informative DevOps Blogging 😊
1 post this monthSoftware for care providers
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.
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
This is a significant step for developers and businesses already using AWS infrastructure. Having OpenAI frontier models and Codex available within the AWS ecosystem can simplify deployment, security, and scalability. I've been using ChatGPT, Codex, and Windsurf in my own projects, and tighter integration with cloud platforms could make AI-powered development workflows much more efficient. Looking forward to seeing how developers leverage this in real-world applications.
This breakdown really clarified something I've been thinking about while building my own AI projects. The point about combining AI flexibility with workflow structure is exactly what I experienced — I built a travel chatbot with Gemini API and the hardest part wasn't the language understanding, it was designing reliable flows around it. The spectrum idea at the end is underrated. Not every use case needs a full autonomous agent — sometimes a focused scripted flow is simply better. Great read! 🙌
As organizations increasingly invest in AI-driven solutions, understanding the distinction between AI, Machine Learning, and Deep Learning becomes essential for making informed technology decisions. This article provides a clear and concise overview of these transformative technologies.
The primary bottleneck in contemporary software deployment is rarely computational capacity; rather, it is the finite cognitive bandwidth of the engineering cohort. High cognitive load manifests when
This is a well-articulated point. In my experience building AI systems, the cognitive load issue shows up most when you're juggling model lo...
Couldn't agree more, clean documentation and good internal systems save way more time than most people realize., less mental overhead means ...
