What’s the one engineering practice your team claims to value… but consistently avoids when under pressure?
Posts by Dave Farley
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5/5
11. One Route to Production
12. Traceability
13. Automated Deployment
14. Observability
4/5
6. Informed Decision Making
7. Small Steps
8. Fast Feedback
9. Automated Testing
10. Version Control
3/5
1. Release Capability
2. Deployment Pipeline
3. Continuous Integration
4. Trunk-Based Development
5. Small Autonomous Teams
2/5
Below are the 14 markers for successful Continuous Delivery within an organisation...
Little bonus game... can you guess which one of these I think is the most important to get right? 👀
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High-performing teams reduce complexity, get fast feedback, improve continuously, and stay connected to real value.
How does your organisation stack up against these 5 ideals?
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5. Customer focus is the ideal that gives the other four their purpose. All the Jira tickets, architecture diagrams, and status meetings ultimately only exist to serve a real human need. Our job is about understanding the problem we are trying to solve.
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4. This ideal frightens ineffective leaders because it sounds soft. It isn't. Engineering relies on the scientific method: make a hypothesis, run an experiment, and learn. You cannot run experiments or surface defects in fear.
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3. Improving the work is the work. In badly run orgs, refactoring, fixing automation, and improving the pipeline are treated as "nice-to-haves". Fixing a flaky test or eliminating a manual process today isn't just morally good, it's economically stupid to do anything else.
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... We use Continuous Delivery (fast tests, small batch sizes, reliable pipelines) to systematically remove friction so developers can actually focus.
5/9
1. Our real enemy is complexity. You can't go fast or safely if every simple change requires spelunking through 14 different subsystems. As I always say, the quality of a system is defined by how easy it is to change it. If your system is hard to change, that's a bug.
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1. Our real enemy is complexity. You can't go fast or safely if every simple change requires spelunking through 14 different subsystems. As I always say, the quality of a system is defined by how easy it is to change it. If your system is hard to change, that's a bug.
3/9
Gene’s "Five Ideals" for rescuing broken software projects map absolutely perfectly onto the empirical, scientific truths of Continuous Delivery and Modern Software Engineering.
Here is how those 5 Ideals translate into real engineering practice:
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Are we writing fiction, or are we practising engineering? If you know my work, you know I generally prefer data and science over fiction. But Gene Kim’s fantastic novel, The Unicorn Project, made me realise something...
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100% Test Coverage is a LIE, Here’s Why... | Sam Newman
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100% Test Coverage is a LIE, Here’s Why... | Sam Newman
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I updated my personal site! I've been wanting to give it a "proper" design overhaul for years, but the time investment needed meant that I kept putting it off. Thanks to Claude, and to having a bit more "free" time right now, I finally did it! Take a look:
trishagee.com
The Junior Developer CRISIS: How to Build a Team When AI Does the Entry-Level Work | @tastapod.com
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The Junior Developer CRISIS: How to Build a Team When AI Does the Entry-Level Work | @tastapod.com
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Every software team will eventually face a heart-stopping mistake, whether it is a developer accidentally deleting live data during a migration or a junior ops engineer unplugging a production server to use a vacuum cleaner... 😬
If you had to deploy a single line of code to production right now, how much of that process is automated and how much relies on 'hoping' nothing breaks?
Why "bad things" happening is actually the best chance for a development team to learn.
This is the story of how a costly mistake taught me a BIG lesson about team culture in software engineering.
📽️ Available on @modernswe.bsky.social ➡️ youtu.be/Q0gxen_uXlw
The $1M MISTAKE That Taught Me THIS About Team Culture | @davefarley77.bsky.social 📅 TONIGHT @ 7PM
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Is AI actually dragging our industry 20 years backwards? We are currently in a Gen AI hype bubble, and it is bringing out the absolute worst impulses in engineering management.
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Interested to hear thoughts on mutation testing... have you tried it? What's your experience?