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Large language models (LLMs) keep getting bigger and better. But the cost of running them — generating text, answering questions, powering real-time applications — is scaling up, too. Obviously, model accuracy is important, but for real-time AI-based web applications, it can’t come at the expense of efficiency. In a paper we presented at the International Conference on Learning Representations (ICLR), we provide a framework for navigating this accuracy-versus-efficiency tradeoff, by connecting scaling laws directly to architectural-design decisions. The […]

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In this quiz, you’ll test your understanding of Python’s Array: Working With Numeric Data Efficiently. By working through this quiz, you’ll revisit the differences between Python’s array module and the built-in list, the meaning of type codes, how to create and manipulate arrays as mutable sequences, and the performance trade-offs of using a low-level numeric container. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every […]

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What are the limitations of using a file-based agent workflow? Why do massive context windows tend to collapse? This week on the show, Mikiko Bazeley from MongoDB joins us to discuss agentic architecture and context engineering. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

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This article explains the practical differences between AI workflows, autonomous agents, and multi-agent systems through real-world analogies, production trade-offs, and code examples. It argues that workflows are best for deterministic, structured tasks with predictable execution paths, while agents are better suited for open-ended problems requiring dynamic tool selection and adaptive reasoning. Multi-agent systems introduce specialized coordination between multiple agents but also increase operational complexity, debugging overhead, and cost. The piece also explores hybrid architectures, beginner mistakes, production reliability, […]

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In this tutorial, we build an advanced Django-Unfold admin dashboard. We start by installing Django, Django-Unfold, and the required dependencies, then we create a fresh Django project with a shop application. We configure Unfold with a modern admin theme, custom sidebar navigation, dashboard callbacks, product badges, tabs, filters, actions, and a custom admin homepage. We also define realistic e-commerce models such as categories, products, customers, orders, and order items, seed the database with sample data, and launch the […]

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OpenAI ChatGPT Plus vs Anthropic Claude Pro vs Google Gemini AI Pro vs xAI SuperGrok vs Moonshot Kimi K2.6 vs Meta Muse Spark vs MiniMax M2.7 vs Microsoft Copilot Pro vs Perplexity Pro — Evaluated Across 10 Categories – with a closing section on consumer data on Reddit. Which AI Subscription Should I Choose? In 2026, the $20/month AI subscription market is the most ferocious battleground in tech. What once bought you priority access to GPT-4 now unlocks […]

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By “SaaS dinosaurs,” I mean the big, shiny, pricey software products. CRMs like Pipedrive. Project tools like Jira. Platforms like Monday.com. You probably heard this line already: “SaaS is dead.” It is NOT and I will tell you why. AI makes software easier to build. That part is true. Building a “new Jira” is no longer impossible for a small team. It is still a little challenging, but doable. When I first tried AI-assisted engineering, I expected thousands […]

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A new engineer joined our team recently. Smart, capable, armed with AI agents that could scaffold an entire service in an afternoon. Within the first week, they opened a pull request with a note: “Why is this service structured like this? I asked Claude and it suggested a cleaner approach.” They were not wrong. The suggested architecture was cleaner, more idiomatic, easier to test. If we were building it today, we would probably do it that way. But […]

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