AI & Engineering
AI coding tools, agentic workflows, LLM gateways and adopting AI in engineering teams without losing quality.
Running an LLM Gateway in Your Business: Keeping Context and Code In-House (and How It Differs from Bedrock)
Once more than a handful of people use AI tools, you need to know which models are being called, with what data, at what cost. An LLM gateway gives you that control point. Here's how to configure one so your code and context stay under your control, and why it's a different thing from Amazon Bedrock.
AI Coding Accuracy and Spec-Driven Development: What Works at Startup, Scaleup and Enterprise
AI coding tools are only as accurate as the context you give them. After two years of rolling them out, the single biggest accuracy lever I've found isn't the model, it's the spec. Here's how to do spec-based development without drowning in process, at each stage of company growth.
Claude Fable 5.1 vs GPT-5.6: My Take as a CTO Who Still Ships Code
The comparison everyone asks me about this year is Anthropic's Claude Fable 5.1 against OpenAI's GPT-5.6. Both are genuinely frontier models. The right choice depends less on benchmarks and more on the shape of your work, your budget and the tooling around the model.
Deploying an LLM Gateway with LiteLLM: Virtual Keys, Budgets and Routing Claude Code Through It
A hands-on guide to running LiteLLM as an internal LLM gateway on AWS: architecture, configuration, per-team virtual keys and budgets, PII guardrails, and pointing developer tools like Claude Code at it.
Spec-First AI Development in Practice: Kiro, Plan Modes and Spec Kit Compared
'Vibe coding' works for prototypes and falls apart on production systems. The tools have responded with spec-first workflows: Kiro's requirements-design-tasks flow, plan modes in Claude Code and Cursor, and GitHub's open-source Spec Kit. Here's how each works and how to choose.
AI Editor Wars (Cursor vs Claude Code vs Kiro): An Honest CTO's Comparison
I've used Cursor daily for over a year, experimented extensively with Claude Code, and evaluated Kiro since its launch. Here's my honest take, not a marketing comparison, but a practitioner's view from a CTO who still writes code.
AI Adoption Strategy for Engineering Teams: How to Do It Without Losing Quality
Introducing AI tooling to an engineering team is easy. Getting the team to use it well (maintaining quality, developing good intuitions about AI output, not creating a dependency) is hard. Here's our approach.
From Autocomplete to Agents: How Agentic Coding Changed Our Workflow in 2025
In the first half of 2025 AI coding tools stopped finishing our lines and started finishing our tickets. Here's what I've learned about which tasks suit agents, how to set a repository up for them, and why code review matters more now, not less.
Model Context Protocol Explained for Engineering Leaders
MCP, announced by Anthropic in November, makes connecting assistants to your tickets, docs and databases easy. That's exactly why engineering leaders need to think about the security model before their teams do it anyway.
A Year of AI Coding Assistants: What I've Learned Rolling Them Out in 2024
2024 was the year AI coding assistants went from clever autocomplete to something closer to a pair programmer. Here's how I'd roll them out, the guardrails I'd insist on, and how to measure impact without falling for vanity metrics.