Innovation doesn’t have to mean big spending. In fact, some of the best product breakthroughs I’ve led came from tight constraints.
“Constraints are the breeding ground for creativity.” — David Kelley, IDEO
In a fast-scaling tech org, the first 90 days make or break your new hires—and your culture. If onboarding is inconsistent, tribal, or just dumped into a Wiki, you’ll lose momentum before your engineers commit their first PR. But when onboarding is designed with intention, new hires ship faster, feel more confident, and connect to the team in meaningful ways. That’s what scales.
As teams grow, knowledge silos form. New hires struggle to find their footing. Culture begins to drift.
Mentorship fixes all of that.
Done right, mentorship creates a culture of learning, accelerates onboarding, and retains top talent during the chaos of scale. It’s the kind of low-cost, high-impact strategy most orgs overlook—until it’s too late.
Hypergrowth exposes every flaw in your team strategy—and every opportunity. Whether you’re scaling from a handful of developers to 100+ engineers or expanding internationally, your ability to build, structure, and develop your teams will make or break the business.
I’ve had the privilege (and pressure) of growing engineering teams from 6 to over 100 during my time at eXp Realty, and later leading engineering at Lone Wolf Real Estate Technologies through periods of acquisition, consolidation, and transformation.
When scaling a technology team, one of the most important decisions you’ll make isn’t architectural—it’s operational: How will your team work together?
Do you go fully remote and tap into a global talent pool? Stick to onsite and build in-person chemistry? Or strike a hybrid balance?
AI is excelling in efficiency, but can it truly replace human creativity? While automation streamlines operations, businesses must strike a balance between AI-driven processes and human ingenuity.
🎯 AI is a tool, not a replacement. The future belongs to businesses that integrate automation while fostering human creativity.
The rapid evolution of artificial intelligence (AI) and automation is rewriting the rules of work. From real estate to finance and SaaS, industries are leveraging AI-driven workflows to boost efficiency, reduce costs, and unlock new opportunities. But while the benefits are clear, so are the challenges: job displacement, ethical concerns, and the ever-growing need for AI literacy.
The landscape of software development is evolving at an unprecedented pace, driven by the rapid advancements in AI and automation. From AI-assisted coding to DevOps automation, the future of software engineering is being shaped by intelligent systems that enhance productivity and streamline workflows. But where is AI making the biggest impact, and what should developers and business leaders expect moving forward?
The transition from engineer to tech executive isn’t easy. Here are the top pitfalls and how to overcome them.
🚨 The Problem: Many new leaders struggle to let go of coding and get stuck in the weeds.
💡 The Fix: Focus on delegation, mentoring, and enabling teams.
We’re coming back to a topic which is near and dear me - Transition from Developer, or individual contributor, into a leader.
Stepping into an executive leadership role from a developer background requires more than technical expertise—it demands a complete shift in mindset, skills, and strategic vision. This article takes a deeper dive into the critical transition from developer to executive, expanding on leadership principles, financial acumen, stakeholder influence, and business strategy. Unlike high-level discussions, this guide provides structured frameworks, real-world examples (including Satya Nadella’s career path), and practical steps to help you navigate the shift. Whether you’re an aspiring leader or already in management, this article equips you with the insights needed to succeed at the executive level.
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