Whether you’re prompting a large language model or asking a colleague for help, the quality of the context you provide is often the difference between getting what you want, or not. In other words, context engineering isn’t just for LLMs. It’s unreasonably effective with humans too.

The importance of a differentiated data model is rising dramatically. AI is commoditizing code. Technical execution is table stakes rather than a competitive advantage. Your product's core data abstractions determine whether new features compound into a moat or just add to a feature list. Here's how to get it right.

How many of us have experienced team building that left feeling like it was a waste of time and money? If you've had that experience, I have some suggestions.

Sign up. Remind me. Subscribe. Upgrade. Connect. Accept. Agree. Scan. Tip. Review. I have had it with all the constant prompts and requests for my attention. I just want to be left alone. The modern world is suffocating my zen.

Last week OpenAI published a report detailing why AI models hallucinate. The reason that AI models hallucinate is not because the model is "broken" or the math behind the models is wrong. Instead, the reasearchers claim that hallucinations are a predictable, systemic outcome of how we train and, more importantly, how we test these systems. In short, the models have been taught that it’s better to guess than to admit they don’t know the answer.

Shopify Chief Executive Tobi Lütke sent an internal memo that said Shopify won’t make new hires unless managers can prove artificial intelligence isn’t capable of doing the job. Teams at the e-commerce company need to integrate AI into their workflows, and doing so will be expected of all employees going forward, according to the memo.