Dev Interrupted

Dev Interrupted focuses on the evolving landscape of engineering leadership, sharing insights into enhancing team productivity, embracing a data-driven approach, and adopting innovative strategies for software development. It covers topics such as career development, team dynamics, continuous improvement practices, and technical challenges in growing tech environments.

Career Development Team Dynamics Continuous Improvement Technical Challenges Leadership in Engineering Productivity Strategies Software Development Practices Engineering Culture

The hottest Substack posts of Dev Interrupted

And their main takeaways

No more coding vibes in the efficiency era

205 implied HN points • 19 Jun 25
🕹 Technology Software Engineering Productivity Artificial Intelligence Metrics Leadership
  1. The focus on just hiring more engineers is outdated. Now, it's important to measure productivity based on real outcomes and impact rather than just feelings.
  2. AI can help with tasks, but it doesn't understand your specific business context. It's important to use AI wisely and not rely on it for critical thinking or decision-making.
  3. To improve productivity, teams need clear context and communication about goals. Understanding the 'why' behind their work is essential for success.

AI won’t thrive in legacy interfaces

23 implied HN points • 26 Jun 25
🕹 Technology AI Developer Tools Software Interfaces Workflows
  1. AI needs better interfaces to work effectively. The old ways just can't keep up with how we now want to collaborate with AI.
  2. The command line is still really important for developers. It’s precise and helps focus on the entire system, but it needs to evolve to work well with AI.
  3. We need a whole new environment for developers that communicates clearly with AI. It should understand everyday language and give developers clear visibility into what AI is doing.

Inside Amazon's AI Video Generator | Kabir Bedi

18 implied HN points • 24 Jun 25
🕹 Technology AI Software Engineering Innovation Product Development
  1. Amazon is using AI to make video creation super easy for businesses of all sizes. Now, anyone can create a professional-looking video with just one click.
  2. Bringing engineers and scientists into direct talks with customers has helped Amazon gather valuable feedback for improving their products. This shows how important customer input is for innovation.
  3. The hiring process at some tech companies, like Cursor, is changing by letting candidates work on real projects right away instead of doing tests. This focuses more on skills than traditional interviews.

Faster coding isn't enough

32 implied HN points • 12 Jun 25
🕹 Technology AI Software Development Engineering Workflow
  1. AI is changing software development, but it's mostly helping with coding and testing. Other important parts, like planning and reviewing, still need a lot of human effort.
  2. Relying too much on AI for speed can be a mistake. It's better to focus on improving the entire development process, not just trying to code faster.
  3. To use AI effectively in development, teams should create clear rules, encourage trying new things, and make sure quality and security aren't compromised.

Google DeepMind's AI playbook for engineering at hyperspeed | Philipp Schmid

18 implied HN points • 17 Jun 25
🕹 Technology AI Engineering Development Innovation Cloud Computing
  1. AI is changing how engineers work and learn. It's making it easier for new people to start coding and improving team collaboration.
  2. Using AI tools effectively requires continuous learning and adapting. Engineers should stay open-minded and embrace new technology to thrive.
  3. There's still a gap between what leaders expect from AI and what developers actually experience. Just adding AI doesn't guarantee better productivity, so thoughtful integration is key.
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AI is the future of the SDLC

32 implied HN points • 10 Jun 25
🕹 Technology AI Software Productivity Development Innovation
  1. AI will make software development faster and more efficient. It can help save time and reduce the amount of work needed to complete projects.
  2. Adopting AI in software development should be done with a clear plan. It's important to set rules and guidelines for how AI is used to ensure it benefits the team.
  3. There's a debate about the impact of AI on coding. Some people are skeptical, but many believe that AI will change how we work in really positive ways.

The biggest time sink for engineering teams: testing

37 implied HN points • 05 Jun 25
🕹 Technology Software Development Quality Assurance AI Integration Automation
  1. Testing is often the biggest delay for engineering teams, slowing down new feature releases.
  2. AI-powered testing tools can automate repetitive tasks, allowing QA teams to work more efficiently and focus on strategy.
  3. The role of QA professionals is shifting towards design and analysis, rather than just executing tests, as automation takes over routine tasks.

Now is the time to rethink engineering productivity

18 implied HN points • 03 Jun 25
🕹 Technology Software AI Productivity Engineering Coding
  1. Engineering teams need to focus more on actively improving productivity rather than just collecting data. It's important to turn insights into actions for better results.
  2. AI coding assistants can struggle and require guidance, as they might not always provide accurate code. Understanding when to rely on AI and when to take control is key.
  3. Using pen and paper can boost creativity and memory. Sometimes stepping away from screens leads to fresh ideas and deeper thinking.

Can Your AI Strategy Be Future-Proof? | Galileo’s Vikram Chatterji

18 implied HN points • 18 Feb 25
🕹 Technology AI Engineering Data Analysis Productivity
  1. AI models sometimes miss important details, like humans do. For example, they may overlook obvious outliers in data visualizations.
  2. Banks are changing their hiring tactics to attract tech talent by offering more flexibility and modern tools. This helps them stay competitive against tech firms.
  3. In a world where AI is growing, the ability to focus deeply is becoming more valuable than just knowing how to use AI tools. Staying focused can help engineers excel.

Hunting for a Flywheel | Refactoring’s Luca Rossi

18 implied HN points • 04 Feb 25
🕹 Technology AI Engineering Software Leadership Research
  1. Developer success depends on feeling happy and respected. When developers are motivated, they can work faster and better.
  2. AI is becoming important for all industries, not just tech. Companies like Goldman Sachs are hiring AI experts to improve efficiency.
  3. Automating tasks like code reviews can help teams focus on important work. Tools that make this easy can boost a team's productivity.

The AI Shift: Preparing for What’s Next

32 implied HN points • 05 Dec 24
🕹 Technology AI Tools Software Development Workflows Talent Acquisition Code Quality
  1. AI tools can help developers work faster, but they need to be careful about the quality of the code. It's important for developers to review what AI produces to ensure it meets necessary standards.
  2. AI is a permanent part of software development, but it has its flaws. Many AI-generated codes can be incorrect, so developers should set up proper checks to keep the software secure and reliable.
  3. To prevent burnout and improve productivity, developers should focus on important projects and let automation tools help with code reviews. Changing hiring practices can also help bring in fresh talent and support better workflows.

Getting to the Root of Engineering Improvement with DORA Core

177 implied HN points • 04 Jan 24
🕹 Technology Development Research Data Analysis Continuous Improvement
  1. DORA Core offers a concise framework of capabilities, metrics, and outcomes to help teams apply research findings.
  2. DORA constantly updates its methodology to keep pace with technological changes and evolving practices.
  3. The DORA Core model shows how capabilities predict performance, which then predicts outcomes, aiding in continuous improvement efforts.

Low-Code Won’t Replace You | Camunda’s Bernd Ruecker

18 implied HN points • 28 Jan 25
🕹 Technology AI Development Automation Software
  1. Low-code tools help developers focus on more complex tasks instead of repetitive ones. They make it easier for non-tech users to build applications too.
  2. Understanding when to use low-code solutions versus custom coding is important. Each has its strengths, and using the right one can save time and effort.
  3. Despite the rise of low-code, experienced developers are still needed. The role of developers is evolving, but they won't be replaced anytime soon.

Why Teams Will Break This Year (and How to Fix Them) | Battery Venture’s Dharmesh Thakker

14 implied HN points • 11 Feb 25
🕹 Technology AI Tools Software Development Engineering Teams Tech industry Productivity
  1. AI could greatly help developers by automating routine tasks and improving productivity. It's important for teams to embrace these changes to stay effective.
  2. Communication is crucial in engineering teams. It's vital to allow junior developers to learn from their mistakes and for everyone to share insights openly.
  3. Good management practices are often lacking but very valuable. Establishing clear goals and regular check-ins can help teams perform better.

Season 4 Finale: A Fond Farewell, What’s Next, & Would You Rather?

23 implied HN points • 17 Dec 24
🎙 Podcasts Technology Engineering Leadership Software AI
  1. The show is ending its fourth season but is excited to change things up next year. They will introduce new ideas, formats, and even have live events.
  2. Programmers need focus time to be productive, and it's important to set aside non-negotiable blocks in the calendar to minimize distractions.
  3. In 2025, leaders want to see real results from AI investments instead of just hype. It's all about proving that AI can make a positive impact on their work.

Developer Productivity Will Decline in 2025 | Predictions from LinearB’s Ori Keren

23 implied HN points • 10 Dec 24
🕹 Technology Software AI Productivity Cybersecurity Engineering
  1. Developer productivity may decrease in 2025 due to an influx of AI tools. Short-term challenges might arise before these tools bring long-term benefits.
  2. In 2025, engineering leaders need to embrace data-driven decision-making. It's important to measure performance to optimize team productivity effectively.
  3. Cybersecurity will be a big focus in 2025, with AI-driven threats and new vulnerabilities. Teams will need to improve their security measures and collaborate better to stay safe.

Making Sense of Agentic AI | ThoughtWorks’ Birgitta Boeckeler

28 implied HN points • 12 Nov 24
🕹 Technology AI Software Programming Development Automation
  1. AI tools can help software teams improve their work, but it's important to pick the right ones that actually make a difference. Sometimes the hype around AI doesn't match up with real-world results.
  2. Governance matters when it comes to programming languages. A strict control model can limit a language's potential for growth, so a more open approach might be better.
  3. Reddit is gaining popularity as users appreciate its less polished, more authentic content. It shows that not all platforms need to rely heavily on AI to attract people.

The Need for Speed | 2025 Engineering Benchmarks

14 implied HN points • 21 Jan 25
🕹 Technology Software Development Engineering Productivity Data Privacy Artificial Intelligence
  1. Smaller pull requests can increase both speed and quality of software development. This helps teams work faster without compromising standards.
  2. Longer cycle times often lead to more errors and project failures. It's essential to keep cycle times short to maintain software quality.
  3. Investing in developer experience (DevEx) is important for a team's productivity. If you don't invest enough, unexpected work and issues can slow down progress.

Back to First Principles | Productiv CTO Ashish Aggarwal

28 implied HN points • 29 Oct 24
🕹 Technology Software Development Cloud Computing Startup Culture Developer Productivity Artificial Intelligence
  1. Developers have 'bad days' when tools fail, processes are messy, or team communication is weak. Senior devs often feel frustrated with organization problems, while junior ones may take failures personally.
  2. The term 'zombiecorn' describes startups worth over $1 billion that struggle to grow and find their market. They often have high spending, depend heavily on funding, and face challenges with customer growth.
  3. Google is working on an AI called Project Jarvis that could take control of your browser to do tasks. But there's concern it might make Google's other services, like Search and Maps, less reliable.

Managing Developer Feedback Effectively | Atlassian’s Andrew Boyagi

14 implied HN points • 14 Jan 25
🕹 Technology Software Development DevOps Productivity Feedback Company Culture
  1. Using surveys alone isn't enough for getting developer feedback. It's better to use data and metrics to understand their issues more clearly.
  2. Setting clear goals for improving developer experience can help align teams better and boost productivity. Everyone needs to be on the same page.
  3. Company culture plays a big role in connecting development efforts with business goals. A positive culture makes it easier for teams to work together effectively.

Promoted from Dev to Team Lead: 8 Things They Didn’t Tell Me

168 implied HN points • 18 May 23
💼 Business Leadership Management Communication Culture Productivity
  1. Being promoted to a team lead involves a shift in focus from technical skills to people and processes
  2. Great devs turn into great leads by honing their instincts and adapting their behavior
  3. Effective communication as a leader involves focusing on the 'why' behind tasks, nurturing a positive team culture, and setting clear paths for team members

The 11 Types Of Toxic Pull Requests (According To 4.5 Million Code Branches)

121 implied HN points • 31 Aug 23
🕹 Technology Software Engineering Code Review Workflow management Collaboration Continuous Integration
  1. Poorly managed pull requests can harm developer productivity by creating bottlenecks.
  2. Common issues in pull request management include lack of process, standardization, and visibility.
  3. Toxic pull requests can be categorized into 11 types, such as outdated code, lacking documentation, and risky changes.

From Rockets to Roadmaps: An Engineer's Guide to Product Success

14 implied HN points • 03 Dec 24
🕹 Technology Software Development Engineering Culture AI Applications Product Management Team Dynamics
  1. Engineers can drive product vision, leading to faster and more innovative development. This shifts the focus from just coding to solving real business problems.
  2. With AI making coding easier, engineers who understand customer needs and market trends will stand out. Their blend of technical skills and business savvy is crucial for success.
  3. Collaboration and teamwork are key in software development. It's not just about individual contributions but how teams work together to create better solutions.

The Hero Culture Trap | Sowmya Subramanian

9 implied HN points • 07 Jan 25
🕹 Technology Engineering Culture Metrics Business Leadership
  1. Building a good team means moving from putting out fires to being proactive. Focus on planning ahead so your team doesn’t always have to deal with emergencies.
  2. Rushing to ship new features isn't always best. Sometimes, following others and quickly adapting ideas can work better in the long run.
  3. When writing code, clear instructions lead to better results. If you’re vague, your code might end up messy and confused.

Zenful Estimates: Ditching Task Estimates to Build a Faster Team

51 implied HN points • 14 Mar 24
🕹 Technology Software Engineering Project management Agile methodologies Engineering Culture
  1. Engineering task estimates are often costly in time and resources, leading to inaccuracies and increased stress within the team.
  2. Distinguishing between task estimation and project estimation can help teams prioritize better and allocate resources more effectively.
  3. By adopting a 'Zenful' approach that focuses on project estimates rather than granular task estimates, teams can save time, reduce stress, and improve overall efficiency.

How to Run a Data-Driven Engineering Org Without Being a Performance Tyrant

88 implied HN points • 22 Jun 23
💼 Business Management Data-driven Leadership Teamwork Productivity
  1. Stack ranking is natural human behavior, but it may not be suitable for engineering teams.
  2. Data-driven does not mean stack ranking; software development teams are more like bands where everyone depends on each other.
  3. Avoid data-driven leadership anti-patterns by knowing your 'why', measuring more than individual stats, and not relying on easily available metrics.

From Fear to Function: Rethinking Engineering Processes | Stytch’s Julianna Lamb

9 implied HN points • 26 Nov 24
🕹 Technology Software Engineering AI Process Productivity
  1. Having the right engineering process can actually boost your team's speed and help everyone take responsibility for their work. It's about finding the right balance, not too much or too little process.
  2. Many developers feel scared of strict processes, but a flexible approach can reduce problems and improve workflow. It's all about making processes work for your team, not against it.
  3. Using AI tools can improve productivity and keep developers focused on challenging tasks. Instead of replacing jobs, these tools help with repetitive work, allowing for better project focus.

Are Only 20% of Devs Happy? | Stack Overflow’s Erin Yepis

9 implied HN points • 19 Nov 24
🕹 Technology Software Engineering Data Productivity
  1. Only about 20% of developers say they are happy in their jobs. This suggests many people in the field are feeling dissatisfied.
  2. Factors like low pay, workplace culture, and issues with technical debt are major reasons behind this unhappiness. It's important to look at these issues to help improve developer satisfaction.
  3. A new project called Flock aims to address problems with the popular Flutter toolkit. The creators want to make a community-driven platform that fixes bugs and speeds up development.