Engineering teams don’t struggle because they lack talent or effort. They struggle because modern software delivery is complex, fast-moving, and fragmented across too many tools. EvolveDev exists to bring clarity to that complexity. The Problem: Engineering Work Is Invisible Until Something Goes Wrong Most engineering organizations run on good
Every engineering team knows it’s coming. The calendar inches toward the end of the year. Release deadlines loom. Stakeholders get nervous. And suddenly, someone says the words that change the mood instantly: “We’re entering code freeze.” In theory, code freeze is simple: No new features. Only critical fixes.
Engineering teams don’t lack metrics. They lack belief in them. Every organization I’ve encountered has dashboards. Lots of them. Velocity charts. Deployment counts. Cycle time graphs. A lovingly curated spreadsheets that someone updates right before the leadership meeting. And yet, the same questions keep coming up: * “Why does
(And your dashboards are making it worse.) You Have an Interpretation Problem. Somewhere in every engineering org, there’s a dashboard. It has charts. It has colors. It has numbers that look important. And yet, every week, the same questions come up in leadership meetings: * “Why is this taking longer
(And why your velocity chart is silently judging you.) Engineering used to be simple. Well—“simple” in the way assembling IKEA furniture is simple: technically doable, emotionally scarring. You wrote code. You shipped code. If things exploded, you patched it at 11 PM with coffee and regret. But 2025 engineering?
If there’s a single thread tying the last week of tech news together, it’s sovereignty. The wind is blowing in a different direction. "Rented" power is getting old for developers. The smartest teams know that convenience isn't worth much if the landlord changes the
The narrative is settled: AI coding tools work. Data shows 78% of companies are now using AI, and upwards of 90% of tech workers use AI tools for tasks like code generation. Microsoft customers report a 30% boost in developer efficiency, and the market for generative AI in software development
In mid-November, many teams experienced git push and pull operations failing, even though the GitHub website itself loaded mostly fine. For developers, that translated into broken CI pipelines, stuck deployments, and confused incident channels where nothing in their systems had changed, yet every build was red. This was not an
As a leader in engineering, you always have one question on your mind: "Will we ship on time?" To answer it, you've made a list of heroic, hard-earned "hacks." You are a detective, a coach, and a leader all at the same time. Does
The world of software engineering is experiencing a fundamental and rapid transformation, driven primarily by the escalating sophistication of AI coding assistants and a critical, complementary focus on embedded security and resilient architecture. In the past seven days alone, news and discussions across the tech landscape confirm that we are
A new report from McKinsey called "The State of AI in 2025: Agents, Innovation, and Transformation" just brought to light the main problem that every tech leader is facing: adoption is huge, but actual, measurable value is hard to find. First, here is our summary of the report&
The age of AI experimentation is over. A new industry report, analyzing over 135,000 developers, confirms that AI-assisted engineering is now ubiquitous, with adoption soaring past 91%. On the surface, the ROI seems obvious. The data shows: * Developers are saving an average of 3.6 hours per week. * Daily
The buzz around AI in software development has been deafening. From code generation to automated reviews, the promises are grand: faster delivery, fewer bugs, and happier developers. But for engineering leaders and savvy developers, the question has always been: "where's the proof?" Can AI truly deliver
AI isn’t just transforming engineering metrics; it’s forcing all of us to rethink what success in software delivery really means. When I first started tracking DORA and SPACE frameworks, both were considered gold standards for quantifying how well teams deliver software: DORA with its sharp focus on speed
When software delivery feels chaotic, characterized by stagnant deployment frequency, creeping lead times, and an alarming change failure rate, the engineering data you already collect is the key to clarity. As an engineering leader, I once faced the piercing question: “Where exactly does our time go?” That moment launched me
Every engineering leader knows the tension between ambitious goals and limited bandwidth. Balancing people, priorities, and timelines isn’t just about who’s available; it’s about ensuring your teams can deliver sustainably, without constant context-switching or burnout. That’s where engineering resource planning comes in. It’s more than
The buzz is deafening. AI is here, and according to the latest 2025 State of Engineering Management Report, 90% of engineering orgs are already using AI-assisted coding tools, seeing an average 25% productivity jump. That's fantastic. But as a leader, you know that "productivity" number hides
I've been watching the enterprise software space closely, and this week's announcements from Salesforce and Amazon have me convinced we're at an inflection point. Agentic AI platforms are no longer futuristic concepts; they're here, and they're going to fundamentally change
I've been observing how the software engineering landscape is experiencing unprecedented transformation throughout 2024. A recent research surveying hundreds of engineering professionals reveals critical insights into the evolving state of engineering management and the challenges teams are facing as we move into 2025. A State of Constant Transformation