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Someone generates a short AI video; it's almost right, except the jacket should be yellow instead of red. With most video generation tools, fixing that means writing a new prompt and hoping the rest of the shot survives the regeneration intact — the lighting, the framing, the way the character moved. That specific frustration, having…

JSON is one of the most common formats for storing and exchanging data in Python applications. For small files, Python's built-in json module works perfectly well: import json with open("data.json") as f: data = json.load(f) The problem starts when the file becomes large. json.load() parses the entire JSON document and creates the corresponding Python…

Large language models are remarkable text generators, but they have a well-documented weakness: they hallucinate. Without access to verified, up-to-date information, they confidently produce plausible-sounding answers that are factually wrong. Retrieval-Augmented Generation (RAG) emerged as the standard solution, grounding model outputs in external knowledge sources and dramatically reducing hallucinations across enterprise applications. But as organizations…

Something crazy is happening in the AI development community, and I think we are witnessing the beginning of a major shift in the software industry. Developers are no longer just vibe coding websites, dashboards, and simple applications. They are attempting to recreate entire commercial software suites, including Adobe Creative Cloud and Microsoft Office. And the…

On September 29, OpenAI announced Dots at its DevDay 2026 conference: always-on AI agents that run on their own cloud computers, connect to more than 4,000 apps, and keep working after you close your laptop. It's the most concrete version yet of something the AI industry has been describing for years: an agent that acts…

Newcomers headed for data and AI work tend to treat the basics of Python as a waiting room. The plan is often just to get through them quickly and arrive at the prime time libraries, where the real work is assumed to happen. It is an understandable plan. However, it produces a particular kind of…

Previous articles in this series discussed constraining output space as well as reusing the prompt prefix with a key-value cache, both framed as approaches to small language model (SLM) narrow automation optimization. Let's finish this series off with the third entry, focused on batching by length instead of looping item by item. As in our…

Introduction Data professionals live in their browsers. You're reading technical documentation, analyzing research papers, reviewing model cards, working through GitHub repositories, and summarizing industry reports — often all in the same afternoon. Increasingly, AI assistants sit alongside that work. The tools most people reach for (Chrome with Gemini, Perplexity, ChatGPT in a pinned tab) are…

Sponsored Content       It's no secret that AI agents burn massive amounts of tokens on search results and file retrievals. They pull in dozens of full-length files, logs, and comment blocks, and just reading through those matches can consume tens of thousands of tokens, not to mention the recursive loops that lock…

Introduction Spaghetti code is hard to work with because its logic is tangled. A function in Python can handle several related steps and still be perfectly readable. Problems start when different responsibilities become tightly connected, dependencies are unclear, and changing one piece of logic requires tracing through unrelated parts of the code. Breaking code into…