All articles
Drawing on Q2 2026 Dev Barometer data and established cognitive science literature, this paper examines why junior and senior developers converge on the same durable competencies despite different relationships with AI-assisted development.
By Francisco Anello
10 min read
Most agentic AI initiatives don't fail at the model level. Earning autonomy, building accountability into architecture, and getting data production-ready are where the real work happens.
By Bob Leibholz
8 min read
New research from BairesDev finds that most tech leaders feel pressure to overstate AI progress, even as structural barriers continue to stall execution.
By Justice Erolin
11 min read
A practical guide to microservices architecture, from API gateways and service discovery to sagas, observability, and failure containment.
By Valfrid Silva
13 min read
A dedicated development team gives you a full engineering unit in weeks, not months. Learn when the model fits, what it costs, and how to evaluate providers.
By Matías Etchemendi
17 min read
Procurement teams are backlogged, operating with fragmented systems, and unable to capture strategic value. This article explains why traditional fixes fail and why AI represents a structural solution, not just automation.
By Keith McFarlane
10 min read
Python 3.13 and 3.14 introduce major changes, from free-threading and JIT experiments to typing improvements that support larger and more AI-heavy codebases.
By Sean Wang
12 min read
The framework for building high-performing engineering teams hasn't changed, but it's no longer enough. Here's the case for adding AI fluency and a 10x mindset to the foundation that still holds.
By Michael Goldstein
10 min read
A practical decision framework for choosing software architecture patterns based on scalability, team structure, operational complexity, and long-term system constraints.
By Tiago Machado
9 min read
Software development outsourcing trends are shifting fast in 2026. New H-1B costs, tighter hiring, and growing IT budgets are reshaping how engineering teams source external capacity.
By Bob Leibholz
11 min read
AI inference costs are unpredictable, hallucination risks are harder to detect in finance, and access control gaps can trigger regulatory action. Here's how to navigate all three.
By Izcóatl Estañol
17 min read
The teams seeing 10x returns from AI aren't using better tools. They're operating on better codebases. Here's the economic case for treating code quality as engineering infrastructure.
By Bryon Jacob
12 min read
Skip the hype cycle. Here is a practical breakdown of which trends change your delivery risk this year and which ones can wait.
By Enrique Ruibal
10 min read
Most AI oversight setups look right on paper and break down in practice. This piece helps engineering leaders identify where the gaps are before a failure does it for them.
By James Booth
13 min read
From AI Pilot to Production: The Organizational Shifts Engineering Teams Can’t Skip – Webinar Replay
AI is moving beyond pilots into production, exposing the limits of existing workflows. Engineering teams must rethink how they build, validate, and operate software in an AI-first environment.
By Bob Leibholz
8 min read