A roundup of essays exploring how AI is transforming software development, from the 70% problem to tooling choices and career strategies.

Overview

This recap highlights key articles from the Elevate newsletter that examine how AI is reshaping software engineering. Each piece tackles a different dimension of AI-assisted development, from practical coding challenges to career strategy and leadership.

The 70% Problem: Hard Truths About AI-Assisted Coding

This article identifies a recurring pattern in AI-assisted development: AI tools can generate roughly 70% of a working solution, but the remaining 30% presents significant challenges. The piece examines the complexities of the human-AI handoff, current tool limitations, and implications for development speed and efficiency.

Beyond the 70%: Maximizing the Human 30% of AI-Assisted Coding

A companion piece that shifts focus to practical solutions for closing the completion gap. This article offers techniques for handling ambiguity, debugging AI-generated code effectively, and maintaining oversight when AI handles the majority of code generation.

MCP: What It Is and Why It Matters

An introduction to the Model Context Protocol (MCP), which functions as a standardized interface between AI models and different execution environments. The article explores how MCP can make agent workflows more modular and reliable by providing consistent integration patterns.

Why I Use Cline for AI Engineering

A detailed look at Cline, a free VSCode plugin, and why it's become a go-to tool for AI engineering work. The article examines what distinguishes Cline from alternatives, weighs its strengths and weaknesses, and discusses the importance of tooling that comprehends both code structure and developer intent.

Future-proofing Your Software Engineering Career

A practical guide offering concrete strategies for navigating a career in software engineering as AI transforms the field. The piece explores how engineers can evolve their skills and mindset to remain valuable and successful as AI changes the fundamental nature of development work.

Leading Effective Engineering Teams in the Age of GenAI

A resource for engineering leaders on managing teams during AI-driven transformation. The article discusses strategies for integrating AI tools effectively, adapting to shifting work dynamics, and maintaining team productivity and morale as AI reshapes standard engineering practices.

AI-Driven Prototyping: v0, Bolt, and Lovable Compared

A comparative analysis of three AI-powered development tools: v0 by Vercel, Bolt by StackBlitz, and Lovable. The piece evaluates their practical use for building MVPs, examines their constraints and trade-offs, and considers how each tool approaches reducing friction between concept and implementation for different development scenarios.

Personal Software: The Unbundling of the Programmer?

A broader perspective on democratization in software development. This article questions what happens when AI enables anyone to build software, exploring whether we're simply empowering existing developers or fundamentally changing who can create software. It considers tools that adapt to users rather than requiring users to adapt to tools.

Looking Forward

These articles represent ongoing experimentation with understanding AI's impact on developers and how to make development tools more thoughtful, adaptable, and empowering. More explorations are planned as the field continues evolving rapidly.