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Build an AI-native engineering team that ships fast

Practical how-tos, model benchmarks, and research on AI-driven development for fast-moving teams.

The change in how factories were laid out was the key to making electrification efforts pay off.
Engineering
September 18, 2026Evan Marshall

The Electrification of Factories: What Software Teams Can Learn From the Transition

Factory electrification reshaped production only after owners redesigned the floor. AI software teams need the same shift in workflow, review, and verification.

Ito AI code review finding on a DoltLite pull request showing a critical runtime analysis failure in database file replacement.
Bug Catch of the Week
September 4, 2026Evan Marshall

Bug Catch of the Week: How a Failed Database Swap Could Delete Your Only Copy

DoltLite's replacement flow could delete the old database before the new copy was safe. Here's how runtime testing exposed the failure path before release.

Layers of testing to bring quality into your software factory
Engineering
August 28, 2026Barron Caster

How to Build Quality Into Your Software Factory

Software factories need speed & quality. Ship fewer bugs by testing in layers; fast tests first, then deeper validation

Chart showing frontier AI model abilities converging, with small quality differences across model tiers.
Engineering
August 21, 2026Evan Marshall

The AI Model Plateau: Why Your Infrastructure Matters More Than the Next Release

AI model routing and evaluation now decide your production results. Learn how companies are adapting to make the most of the highly competitive LLM landscape.

the differences between static and runtime analysis
Guide
August 14, 2026Evan Marshall

Why AI Code Review Needs to Read Your Code and Run It

A practical guide to static and runtime analysis in AI code review, including what each approach catches, where it falls short, and why teams use both.

dolthub logo
Case Studies
August 4, 2026Evan Marshall

What Happened When DoltHub Ran Ito on 43 Pull Requests

DoltHub reported a 2:1 fixed-to-dismissed ratio for Ito bug findings, and surfaced pre-existing issues in 26 of 43 tested PRs over six weeks.

Google Trends chart comparing search interest in MTTR and mean time to recovery from 2014 to 2026, showing a sharp peak around 2021-2022
Engineering
July 23, 2026Evan Marshall

MTTR is the Wrong Metric for AI-Era Engineering Teams

AI tools produce 41% more bugs and 98% more pull requests. MTTR can't keep up. Here's how MTTF shifts your team from incident response to prevention.

Bar chart: median seconds for fourteen models to complete the same coding task
Engineering
July 21, 2026Evan Marshall

Your fastest model is probably not your fastest model

Tokens per second measures how fast a model emits, not how fast it finishes. Our 13-model probe shows the gap that flips both the speed + cost leaderboards.

How Moo makes your worktrees better by isolating each of their environments
Engineering
July 14, 2026Evan Marshall

Moo: Giving Your Agents the Runtime Isolation git worktrees Need

git worktree isolates your files. Moo isolates the database, ports, and services, saved per commit. Together, they give your agents fully isolated machines. Learn how to use Moo, the benefits, and why your agents need it.

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