Topic
Deep-dives on building AI systems that survive contact with production — architecture, evaluation, guardrails, and the engineering that sits between a model and a product.
19 articles
Everyone is building with AI today. Very few are building systems that actually work in production. This article explores the real gap between models and products, and why thinking like an AI engineer matters more than ever.
A behind-the-scenes look at how I designed and built a production-ready prompt engine - from dynamic templates and reusable components to multi-layer guardrails, structured output, and parallel execution.
Building real-time computer vision on low-end mobile devices comes with unique challenges - especially when every second of inference drains battery. Here's how I optimised similarity search using HNSW and hybrid re-ranking to cut battery usage by over 50%.
Andrej Karpathy’s talk at Y Combinator’s AI Startup School explores Software 3.0, the rise of LLMs as programmable computers, vibe coding, and building partial autonomy apps. Learn how AI is transforming how we build - and who gets to build - the future of software.
OpenAI’s CFO has revealed plans for an AI agent capable of building, testing, and documenting software all on its own. What does this mean for developers - especially students and juniors just starting out? Here’s what you need to know.
OpenAI’s latest release, GPT-4.1, redefines AI capabilities with a massive 1 million-token context window, enhanced coding and instruction-following skills, and cost-effective performance. Discover how GPT-4.1 and its Mini and Nano variants are set to transform AI applications across industries.
Shopify’s leaked AI memo isn’t just a company directive - it’s a call to arms for every business leader. If you’re not thinking AI-first in how you hire, build, and scale, you’re already falling behind. Here’s a breakdown of the memo’s most urgent insights and how to act on them today.
With soft brushstrokes, dreamy colors, and heart-tugging nostalgia, Ghibli-style art has taken the internet by storm. But what exactly is “Ghibli-style,” and why did it melt OpenAI’s servers? Here’s a look into the origins, the viral trend, and the whimsical magic behind it all.
It didn’t arrive with fireworks or fanfare. DeepSeek slipped into the scene with a simple mathematical change - and suddenly, it was the name on everyone’s lips. But what’s really behind the hype? Let’s unpack how a humble paper sparked global buzz, and what it means for the future of AI, geopolitics, and the race for smarter, smaller, and more efficient intelligence.
Before 2017, AI models were powerful but painfully slow and complicated. Then came a research paper titled “Attention is All You Need” - a game-changer that flipped everything we knew about AI on its head. Here’s how it reshaped the future in the simplest way possible.
What if your phone could predict your next tap or swipe? UI-R1, a new AI model trained with reinforcement learning, is bringing us closer to smarter, more intuitive virtual assistants. By learning from visuals and language - not just massive datasets - UI-R1 shows how AI can interact with real-world mobile apps more accurately and efficiently.
How do large language models actually learn facts? A new study by Google DeepMind and ETH Zürich uncovers a surprising three-phase process - from slow starts to sudden insights and unexpected memory loss. These findings reveal why your AI assistant sometimes nails the answer - and sometimes confidently makes things up.
Multi-Agent AI Systems promise intelligent collaboration between agents, but why do they so often underperform? A new UC Berkeley study digs deep into the causes of these failures - highlighting critical design flaws, coordination issues, and verification challenges - and offers a roadmap for building better, smarter systems.
Generative AI is reshaping our world in ways reminiscent of the Industrial Revolution. From automating routine tasks and transforming job roles to revolutionizing the IT landscape and empowering solo entrepreneurs to launch unicorn startups, this new era is redefining creativity and innovation. Dive in to discover how embracing AI could be your ticket to a future full of endless possibilities.
Large Language Models (LLMs) have transformed the world of artificial intelligence, enabling machines to generate human-like text, answer questions, and even write code. But how do they actually work? This article breaks down the key concepts behind LLMs in a way that is easy to understand, with just enough math to show how things come together.
Generative AI is revolutionizing the legal industry by automating tasks such as legal research, contract drafting, and litigation support. Its applications lead to increased efficiency, accuracy, and access to legal services, while also presenting challenges like ethical concerns and regulatory issues. Despite these hurdles, AI’s integration is set to reshape legal practices significantly.
Artificial General Intelligence (AGI) denotes a hypothetical AI with human-like cognitive abilities, capable of solving diverse problems autonomously. Its attainment poses significant technical and ethical challenges, reflecting debates on consciousness and alignment with human values. While progress in AI continues, true AGI remains uncertain, potentially decades away or evolving gradually.
Artificial Intelligence is rapidly evolving, particularly through advancements in generative AI, large language models (LLMs), and autonomous AI agents. These innovations are reshaping industries by automating tasks, enhancing productivity, and augmenting human capabilities. However, limitations exist, such as biases and inaccuracies, necessitating careful integration and oversight. A balanced approach can maximize benefits while mitigating risks.
