#pre-applying

#pre-applying

I got a degree from Douglas College in programming and business management. I understood the business side more and was better at that than at being a coder.

While AI is great for drafting an email in seconds, the foundation - your personas, your data hygiene and your compliance - still requires a human at the helm. By using AI-driven knowledge bases, her team is reducing that drag, ensuring sales finds what they need without the manual 'where is this file?' fire drill.

Most of these companies start the journey from a functional standpoint, avoiding extra layers that may "divert users' attention", such as refined flows, potential edge cases, and, sometimes, proper visual design foundations and user experience. Here, the goal is to ship the product first to validate its value, then address other considerations.

Well, our guest today argues that the best way is by moving to a more project-driven model of work, up and down the organization from the corporate level to individual teams. He wants us to both ruthlessly prioritize as well as stay fluid so that we're identifying strategic goals, assembling teams to go after them, evaluating as we go, and then either continuing, shifting, or disbanding based on our outcomes.

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I'm always amazed at how easily we give our time to others without thinking, and then are mad later when it was wasted. What exactly did we think was going to happen? That everyone was going to be prepared, productive, and appreciative? Time has become the ultimate luxury-we never have enough of it, and are jealous of those that have it. For too many of us, endless meetings, back-to-back emails, and constant interruptions leave little room for focused, meaningful work.

Your AI pilot showed 94% accuracy improvements. The LLM is yielding solid results. You're getting defunded anyway. The reason? You solved a problem AI can solve. Your budget-holder needed you to solve theirs. Companies launch AI pilots that produce results, then stall at scale. The team's diagnosis: "They don't get it." What's really going on: These projects never earned budget-holder buy-in.

During my eight years working in agile product development, I have watched sprints move quickly while real understanding of user problems lagged. Backlogs fill with paraphrased feedback. Interview notes sit in shared folders collecting dust. Teams make decisions based on partial memories of what users actually said. Even when the code is clean, those habits slow delivery and make it harder to build software that genuinely helps people.

Her payment form wasn't connecting to the payment processor, and every attempt ended in an error message that made no sense. I understood her frustration. As a founder myself, I was acutely aware of the pain of trying to run a business and feeling like nothing was going your way. When I dug into her form, I found the problem a few minutes later: a mismatch between test mode and live credentials.

An AI agent is simply a model that receives input, follows defined goals and rules, makes step-by-step decisions, and uses tools to take actions. Instead of viewing AI agents as autonomous digital workers, break it down. First principles thinking says this definition captures the essence of how AI agents function and operate within business workflows.

To find the typical example, just observe an average stand-up meeting. The ones who talk more get all the attention. In her article, software engineer Priyanka Jain tells the story of two colleagues assigned the same task. One posted updates, asked questions, and collaborated loudly. The other stayed silent and shipped clean code. Both delivered. Yet only one was praised as a "great team player."

In order to make the best use of 2023 budgets, brands need to find a balance between scale and suitability. And, in a digital world, where platforms are able to reach huge global audiences, scale is at the fingertips of every brand. But, back in May, a panel session on the Tech Lab Stage raised the question: how can businesses manage the seemingly mutually exclusive concepts of safety and scale?

Much of the conversation about how to work effectively with generative AI has focused on prompt engineering or, more recently, context engineering: the semi-technical skill of crafting inputs so that large language models produce useful outputs. These skills are helpful, but they are only part of the story.

"I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue."

AI reveals a hidden, outdated assumption: that humans will continue to serve as the "digital glue," manually connecting disparate systems, teams, and decisions. For decades, enterprise software perpetuated a model of sequential handoffs, in which people managed data entry, reconciled conflicts, chased approvals via email, and updated spreadsheets. This structure was manageable when uncertainty was low and delayed decisions were affordable.