Engineering

Thinking about the GPT-5 presentation fiasco yesterday(friends don’t let friends use Dalle for charts) and the resulting, almost overwhelmingly negative reaction to the style of the speakers and the substance. I’m wondering if what we’re seeing is less a problem with LLMs having hit a ‘wall’ and more with the ’enshittification’ of OpenAI itself? They’ve never been particularly strong on the pure research side of things. Their main strength has always been productizing scientific breakthroughs in consumer products. Take the fundamental ‘attention is all you need’ paper and transformers architecture. Neither of those were OpenAI breakthroughs. Instead, their incredibly talented early team identified ways to capitalize on those important insights with their own breakthroughs in model training and scaling. ...

You’ve been there. You and your AI coding buddy are in the zone. It’s feeding you perfect snippets of code, it understands your weirdly named variables, it’s practically reading your mind. You’ve built half a dozen functions, and the project is humming along. Then you close the window. You come back an hour later, open a new chat, and ask it to build the next piece of the puzzle. The AI stares back at you with the digital equivalent of a blank expression. It has no idea what your project is, what a user_auth_service is, or why you keep muttering about the global_config.json. It has, for all intents and purposes, become incredibly dumb. ...

Ever tried Elevator Saga? It’s that addictive JavaScript challenge where you control elevators to transport impatient virtual humans. Sure, you could spend hours crafting the perfect algorithm… or you could do what I did and let AI handle the heavy lifting. Tools Claude 3.5: Model I prefer for generating code and specifications. It’s like having a coding buddy who never sleeps. Whisper: For transcribing my ramblings into something resembling a coherent spec. LLM: A command-line tool to interact with AI models like Claude and generate code from my documentation and spec. My Lazy Developer Workflow I built a four-step AI pipeline that took me from “what even is an elevator API?” to passing the first four levels with minimal effort: ...

The stadium buzzed with electric anticipation. It was the final seconds of the Super Bowl, and the underdog team trailed by four points. Everything hinged on this last play. The quarterback scanned the defense and noticed a telltale formation—the opponents had fallen into a pattern they’d dissected tirelessly in practice. Seizing the moment, he called an audible at the line of scrimmage. Instantly, the offensive line adjusted their stances, receivers altered their routes, and the running back shifted to provide crucial protection. The ball was snapped. In a seamless display of teamwork and adaptability, the play unfolded flawlessly, culminating in a game-winning touchdown. ...

In many engineering organizations I’ve been part of, the drive for rapid delivery often results in shortcuts and “good enough” solutions. When managed effectively, this ability to quickly provide value can be a significant competitive advantage. However, poor management or defaulting to this approach can lead to what I call “complexity bombs” – ticking time bombs of technical debt that explode into major issues as systems grow and evolve. These can sometimes manifest unexpectedly, particularly in distributed systems, causing sudden leaps in complexity.ystems. ...