Maintainable

Robby Russell

Do you feel like you're hitting a wall with your existing software projects? Are you curious to hear how other people are navigating this? You're not alone. On the Maintainable Software Podcast, Robby speaks with seasoned practitioners who have overcome the technical and cultural problems often associated with software development. Our guests will share stories in each episode and outline tangible, real-world approaches to software challenges. In turn, you'll uncover new ways of thinking about how to improve your software project's maintainability.

  1. 18 hr ago

    Diana Pfeil: Building Confidence in Probabilistic Systems

    Machine learning systems can degrade even when the underlying code has not changed. Diana Pfeil of Sunbeam Consulting joins Robby Russell on Maintainable to explain how changing data, model behavior, and non-deterministic outputs create a different kind of maintenance challenge. Rather than asking whether a system is simply correct or incorrect, teams need reliable ways to measure confidence in its behavior. Diana introduces evals as a way to test AI-generated outputs that may be phrased differently each time. She and Robby discuss using LLMs to judge other LLM outputs, reviewing production traces, sampling unusual cases, protecting sensitive data, and keeping humans involved when automated checks cannot provide enough confidence. They also explore how prompts should be versioned and tested against representative examples. The conversation turns to the operational costs that come with AI features. Model providers can deprecate dependencies quickly, prompts may behave differently after an upgrade, and teams must continue monitoring systems that might once have been considered finished. Diana encourages organizations to ask what can now be automated, how accurate the result needs to be, and whether the benefit justifies the additional maintenance work. Diana also makes the case for starting with the simplest model or deterministic process that can solve the problem. Teams can add complexity once the baseline proves insufficient, but designing around imagined future requirements often creates the wrong system. Her advice for engineers trying to introduce AI internally is equally direct: build a small prototype that solves a real problem, then let the result make the case. Episode Highlights [00:00:50] Maintaining Probabilistic Software: Diana explains why maintaining AI systems involves the code, changing data, model behavior, and confidence in the output. [00:02:35] What Are Evals?: Robby asks how teams test AI-generated results when the correct response may be worded differently each time. [00:04:38] Using an LLM as a Judge: Diana describes using one model to evaluate another and why the judge must be calibrated against human decisions. [00:06:39] Monitoring AI in Production: Diana introduces human review, traces, observability, and production sampling. [00:08:44] Recognizing Input Drift: A meeting-notes example shows how changing inputs can degrade an otherwise unchanged system. [00:12:06] Maintaining Models and Prompts: Diana outlines the code, model, prompt, and data-pipeline changes teams may need to make. [00:14:24] Versioning and Testing Prompts: Robby asks how prompt experimentation fits into source control and repeatable testing. [00:18:32] Building Confidence in a Black Box: Diana explains how evals and production reviews help teams avoid regressions. [00:21:16] Diana’s Machine Learning Background: Diana shares her path from recommendation systems at Amazon to startup leadership and consulting. [00:22:26] How Sunbeam Consulting Helps Teams: Diana describes advising leaders on AI strategy and helping teams build machine learning products. [00:24:28] Finding Useful Automation Opportunities: Diana explains how teams can identify previously unstructured work that may now be practical to automate. [00:27:40] The Cost of Automated Decisions: Robby and Diana compare human error with the oversight and infrastructure required by AI systems. [00:30:14] AI Is Not Free to Maintain: Diana discusses model deprecations, vendor dependencies, and the ongoing support required after launch. [00:36:45] Keeping Up With Rapidly Changing Tools: Diana explains why teams need room to experiment without constantly disrupting established workflows. [00:38:49] Why Simpler Models Often Win: Diana makes the case for starting with a baseline before introducing more sophisticated approaches. [00:45:18] Selling an AI Idea Without the Buzzwords: Diana recommends building a useful prototype and allowing the result to make the case. [00:46:30] The Inner Game of Tennis: Diana recommends W. Timothy Gallwey’s book about learning, judgment, and performance. Resources Mentioned Sunbeam Consulting Diana Pfeil on LinkedIn Pydantic Amazon Bedrock Guardrails Claude Code Cursor OpenAI Codex The Inner Game of Tennis by W. Timothy Gallwey Thanks to Our Sponsors! Your test coverage says 90%, but that might be misleading. Undercover CI looks at your Ruby pull requests and shows you which parts of your changes weren't tested- not just overall coverage, but what changed and what got missed, down to the method level. Visit undercover-ci.com and use code MAINTAINABLE for 15% off your first billing cycle. Free for public repos. Private repos with unlimited users also available. Mailtrap is a modern email delivery platform built for developers. Native SDKs, a secure Email API and SMTP, and a free tier with 4,000 emails a month. When you need help, you'll reach real people on 24/7 support, not an AI chatbot. Try Mailtrap for free! Subscribe to Maintainable on: Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts. Keep up to date with the Maintainable Podcast by joining the newsletter.

    Diana Pfeil: Building Confidence in Probabilistic Systems

  2. 16 Jun

    Chris Coyier: The Long Game of Maintaining CodePen

    What does it take to keep a product healthy after more than 15 years of continuous evolution? In this episode, Robby Russell talks with Chris Coyier, co-founder of CodePen, about the long game of maintaining software. Chris shares how CodePen has evolved over time, the trade-offs involved in migrating parts of the platform from Rails to Go, and the challenges of balancing maintenance work with the desire to build what's next. They also explore the human side of maintainability, the role of technical debt in shaping priorities, and why small teams often have to make very intentional decisions about where to invest their limited time and attention. Whether you're maintaining a side project, stewarding a legacy application, or helping a team navigate change, this conversation offers practical insights into building software that lasts. Key Topics Defining what "well-maintained software" really means Why maintainability is often more of a people problem than a code problem The origin story of CodePen Supporting a product that has evolved over 15 years Balancing maintenance work with product evolution Gradually migrating from Rails to Go Using GraphQL across multiple implementations Technical debt and its many interpretations Team size, communication overhead, and organizational design Simplifying software by embracing browser capabilities Links & Resources ChrisCoyier.net Chris Coyier on Bluesky CodePen ShopTalk Show CSS-Tricks Book Recommendation Understanding Comics: The Invisible Art (Goodreads) by Scott McCloud Thanks to Our Sponsors! Your test coverage says 90%, but that might be misleading. Undercover CI looks at your Ruby pull requests and shows you which parts of your changes weren't tested- not just overall coverage, but what changed and what got missed, down to the method level. Visit undercover-ci.com and use code MAINTAINABLE for 15% off your first billing cycle. Free for public repos. Private repos with unlimited users also available. Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on: Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts. Keep up to date with the Maintainable Podcast by joining the newsletter.

    Chris Coyier: The Long Game of Maintaining CodePen

  3. 5 May

    Sally Lait: Confidence Is the Real Metric

    Sally Lait joins Robby Russell on Maintainable to explore software maintainability through a different lens… not just code quality, but how teams work together over time. Sally is a fractional technology leader and advisor with more than two decades in the industry. You can follow her on LinkedIn or Mastodon. They start with a familiar question: what makes software well maintained? Structure and standards matter, but Sally shifts the focus to signals around the edges… documentation, onboarding speed, knowledge sharing, and especially how confident people feel making changes. That confidence becomes the thread throughout the conversation. Teams with high confidence move faster and adapt more easily. Teams with low confidence hesitate, avoid parts of the system, and struggle to make progress… regardless of what the code looks like. Robby and Sally also dig into why maintenance work often struggles to get traction. It rarely speaks for itself. Leaders need to connect it to outcomes the business already cares about… risk, hiring, delivery speed, and long-term sustainability. Sally references a LeadDev panel she moderated on why maintenance still feels “stuck in 2015”: Why Software Maintenance Is Stuck in 2015. They also discuss modernizing legacy systems and moving away from long-standing in-house software… work that is rarely just technical. It requires trust, clear communication, and navigating the emotional attachment teams have to what they’ve built. The episode closes with advice for engineers joining older codebases: stay curious, build relationships early, and use onboarding gaps as opportunities to improve things for the next person. Episode Highlights [00:01:02] What Makes Software Maintainable: Technical quality matters, but cultural signals often tell the deeper story. [00:05:45] Why Progress Still Feels Slow: Even with improvements, teams can feel stuck due to perception gaps. [00:07:30] Communicating Small Wins: Lack of visibility into incremental progress impacts morale and confidence. [00:12:40] Influencing Without Manipulating: Maintenance work needs to be framed in business terms. [00:16:00] Technical Debt as a Hiring Problem: Outdated systems affect recruiting and retention. [00:20:22] Modernizing a Siloed System: Unlocking legacy data required both technical and organizational change. [00:26:55] Building Trust for Change: Surprise proposals fail… alignment takes time. [00:32:39] Letting Go of “Our Baby”: Replacing systems involves emotional and cultural dynamics. [00:46:25] Joining an Older Codebase: Practical advice for onboarding and building confidence quickly. Resources Mentioned Sally Lait Sally Lait on LinkedIn Sally Lait on Mastodon Why Software Maintenance Is Stuck in 2015 (LeadDev Panel) Lara Hogan The Murderbot Diaries by Martha Wells Death of the Author by Nnedi Okorafor Sally’s Reading & Reviews Site Thanks to Our Sponsors! Your test coverage says 90%, but that might be misleading. Undercover CI looks at your Ruby pull requests and shows you which parts of your changes weren't tested- not just overall coverage, but what changed and what got missed, down to the method level. Visit undercover-ci.com and use code MAINTAINABLE for 15% off your first billing cycle. Free for public repos. Private repos with unlimited users also available. Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on: Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts. Keep up to date with the Maintainable Podcast by joining the newsletter.

    Sally Lait: Confidence Is the Real Metric

  4. 14 Apr

    Rein Henrichs: The Real Work of Maintenance Happens Before You Touch the Code

    Software maintenance is often framed as a technical problem. Refactoring code, fixing bugs, or upgrading dependencies. In this conversation, Robby Russell talks with Rein Henrichs about a different lens, one centered on understanding. Rein is a Principal Software Engineer at Procore, where he works within a large, long-lived system used across the construction industry. Rather than focusing on tooling, Rein emphasizes that well-maintained software is software that makes sense to the people maintaining it. To explain this, Rein introduces the idea of the line of representation, drawing on the work of Richard Cook. Engineers do not interact directly with systems. They rely on representations such as logs, dashboards, and code. These are approximations, not reality, echoing ideas from Plato’s Allegory of the Cave. When those representations break down, teams lose shared understanding, what Rein describes as “common ground.” This often shows up as weak signals. Subtle indicators that something is not quite right. They are easy to ignore, but over time they lead to confusion and slower decision-making. Incidents make this especially visible. Rein explains how teams build alignment under pressure, highlighting that the role of an incident commander is coordination, not control. Clear communication matters as much as technical correctness. The conversation also explores how large systems behave in practice. They rarely fail completely. Instead, they degrade in multiple ways at once. While SLOs can help teams respond to customer-facing issues, they do not capture internal clarity or alignment. Rein references W. Edwards Deming to highlight a common trap. Not everything that matters can be measured. High-performing teams often rely on judgment, experience, and shared context. Toward the end, Rein connects these ideas to The Field Guide to Understanding Human Error by Sidney Dekker, challenging the idea that incidents are simply caused by mistakes. Instead, they emerge from the same behaviors that usually lead to success, just under different conditions. For teams working in complex systems, the takeaway is straightforward. Maintaining software depends on maintaining understanding. Links & Resources Procore Rein Henrichs on LinkedIn Concepts & References How Complex Systems Fail – Richard Cook The Field Guide to Understanding Human Error – Sidney Dekker W. Edwards Deming Gerald Weinberg – Secrets of Consulting Referenced in this Conversation Kent Beck: You’re Ignoring Optionality and Paying for It Charity Majors: Deploys Are Just the Beginning Heidi Helfand: The Art and Wisdom of Changing Teams Thanks to Our Sponsor! Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time!  Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on: Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts. Keep up to date with the Maintainable Podcast by joining the newsletter.

    Rein Henrichs: The Real Work of Maintenance Happens Before You Touch the Code

  5. 31 Mar

    Russ Olsen: The Hidden Cost of Forgetting Why the Code Looks Like That

    Software doesn’t become hard to maintain only because the code is messy. It often becomes hard to maintain because the reasoning behind it disappears. In this episode of Maintainable, Robby Russell talks with Russ Olsen about trade-offs, legacy systems, and why maintainability depends on context more than dogma. Russ brings decades of experience across very different kinds of systems, each with its own definition of what “maintainable” actually means. A central theme is that software must be understandable to the people maintaining it. Teams tend to document implementation details well, but often fail to capture system-level intent and the trade-offs behind major decisions. Russ makes the case for preserving that thinking, including the alternatives that were rejected, so future maintainers don’t have to rediscover it the hard way. The conversation also touches on Russ’s book Eloquent Ruby, Second Edition. Rather than teaching syntax, the book focuses on how Ruby is actually used in practice and why common patterns exist. That leads into a discussion about where elegance improves maintainability, and where it turns into unnecessary cleverness. From there, the episode shifts into the realities of working in legacy systems. Russ explains how teams develop pessimism over time, often accepting flawed assumptions about how their systems behave. In some cases, major issues turn out to be far simpler than expected. The challenge is that teams stop looking. Robby and Russ also discuss the value of fresh perspective. New engineers or outside contributors can surface assumptions that longtime maintainers overlook. Russ suggests finding “pinch points” in a system as a practical way to understand behavior without needing to fully untangle everything at once. Later, the conversation explores developer quality of life. Long build and deploy cycles create daily friction that teams often underestimate. These slow feedback loops quietly degrade productivity and morale over time. The episode also tackles rewrites. Russ warns that teams frequently underestimate how much knowledge is embedded in existing systems. Code that looks questionable may reflect constraints no one documented. In practice, most successful rewrites happen incrementally, not all at once. The conversation wraps with a reminder that software development is fundamentally a social process. Russ argues that engineers undervalue storytelling, even though it’s one of the most effective ways to connect technical work to real human outcomes. Episode Highlights [00:00:40] Defining maintainability: Why context matters more than a universal standard [00:02:01] Beyond code comments: Documenting system intent and trade-offs [00:08:14] Who Eloquent Ruby is for: Understanding how Ruby is used in practice [00:16:21] Elegance vs. cleverness: Where maintainability starts to erode [00:23:18] Legacy pessimism: Why teams stop questioning assumptions [00:29:25] Pinch points: A practical way to understand complex systems [00:32:05] Developer experience: The hidden cost of slow feedback loops [00:38:26] Rewrites: Why they fail and what teams overlook [00:44:00] Storytelling: Connecting technical work to real-world impact Resources Mentioned Russ Olsen on LinkedIn Eloquent Ruby, Second Edition Getting Clojure Zen and the Art of Motorcycle Maintenance A History of Western Philosophy Thanks to Our Sponsor! Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time!  Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on: Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts. Keep up to date with the Maintainable Podcast by joining the newsletter.

    Russ Olsen: The Hidden Cost of Forgetting Why the Code Looks Like That

  6. 17 Mar

    Joel Oliveira: Predictability Is a Maintainability Feature

    Long-lived software systems rarely stay tidy. Over time they accumulate decisions, workarounds, and layers of history that can make even simple changes feel risky. For engineers responsible for maintaining those systems, the challenge often becomes less about writing new code and more about understanding what already exists. In this episode of Maintainable, Robby Russell speaks with Joel Oliveira, Engineering Manager at ezCater, about what helps software remain understandable and adaptable as it evolves. Joel starts with a principle that often gets overlooked: predictability. When patterns are consistent and code is organized in familiar ways, engineers can navigate a codebase with confidence. Frameworks like Ruby on Rails reinforce this by encouraging shared conventions, making it easier for developers to orient themselves when working in a new application. The conversation also explores how common metrics can be misleading. Test coverage is often treated as a proxy for quality, but Joel explains that it can create a false sense of confidence. Instead, he values testing most as a thinking tool. Practices like test-driven development can help engineers clarify interfaces and better understand the problem before committing to an implementation. Joel also shares a story from ezCater about replacing an aging image-cropping service that had become difficult to maintain and required frequent restarts due to a memory leak. Rather than patch the system again, the team introduced ImageProxy, an open source image processing tool created by Evil Martians. Because the image URLs flowed through a single object in their GraphQL layer, the team could introduce an adapter and gradually route traffic to the new service using feature flags. This allowed them to migrate safely and incrementally instead of relying on a risky “big bang” change. Robby and Joel also discuss how engineers’ perspectives shift over time. Early in a career it is easy to look at legacy code and label it as bad. Joel now sees older systems as layers of decisions shaped by real constraints. Approaching them with empathy makes it easier to improve them thoughtfully. The episode closes with advice for engineers maintaining complex systems: frame problems as opportunities. By documenting impact and proposing incremental improvements, teams can steadily move their software toward a healthier future. Maintainable software rarely comes from one heroic refactor. More often, it’s the result of many small improvements made by teams who understand their systems and care about how they evolve. Episode Highlights 00:02:18 – Predictability as a Maintainability Feature Joel explains why predictable patterns and conventions make large codebases easier to navigate. 00:07:41 – When Test Coverage Misleads Why high coverage can give a false sense of quality. 00:12:05 – Consulting vs. Product Engineering How switching environments shaped Joel’s approach to code. 00:16:32 – Replacing a Legacy Image Service ezCater’s migration away from a failing Node-based image service. 00:21:14 – Migrating with Adapters and Feature Flags How the team gradually moved traffic to ImageProxy. 00:26:03 – Developing Empathy for Legacy Code Why older systems deserve understanding, not blame. 00:30:47 – The Shift to Engineering Management Joel reflects on moving from IC work to leading teams. 00:34:52 – Advice for Improving Complex Systems Small, consistent improvements matter more than big rewrites. Thanks to Our Sponsor! Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time!  Use the code maintainable to get a 10% discount for your first year. Check them out! Links / References Joel Oliveira — LinkedIn Joel Oliveira — Website Joel Oliveira — Mastodon (@jayroh) ezCater ImageProxy Subscribe to Maintainable on: Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts. Keep up to date with the Maintainable Podcast by joining the newsletter.

    Joel Oliveira: Predictability Is a Maintainability Feature

  7. 3 Feb

    Lucas Roesler: The Fast Feedback Loop Advantage

    Maintaining software over time rarely fails because of one bad decision. It fails because teams stop getting clear signals… and start guessing. In this episode, Robby talks with Lucas Roesler, Managing Partner and CTO at Contiamo. Lucas joins from Berlin to unpack what maintainability looks like in practice when you are dealing with real constraints… limited context, missing documentation, and systems that resist understanding. A big through-line is feedback. Lucas argues that long-lived systems become easier to change when they provide fast, trustworthy signals about what they are doing. That can look like tests that validate assumptions, tooling that makes runtime behavior visible, and a habit of designing for observability instead of treating it as a bolt-on. The conversation also gets concrete. Lucas shares a modernization effort built on a decade-old tangle of database logic… views, triggers, stored procedures, and materializations… created by a single engineer who was no longer around. With little documentation to lean on, the team had to build their own approach to “reading” the system and mapping dependencies before they could safely change anything. If you maintain software that has outlived its original authors, this is a grounded look at what helps teams move from uncertainty to confidence… without heroics, and without rewriting for sport. Episode Highlights[00:00:46] What well-maintained software has in common: Robby asks Lucas what traits show up in systems that hold together over time.[00:03:25] Readability at runtime: Lucas connects maintainability to observability and understanding what a system actually did.[00:16:08] Writing the system down as code: Infrastructure, CI/CD, and processes as code to reduce guesswork and improve reproducibility.[00:17:42] How client engagements work in practice: How Lucas’ team collaborates with internal engineering teams and hands work off.[00:25:21] The “rat’s nest” modernization story: Untangling a legacy data system with years of database logic and missing context.[00:29:40] Making data work testable: Why testability matters even when the “code” is SQL and pipelines.[00:34:59] Pivot back to feedback loops: Robby steers into why logs, metrics, and tracing shape better decision-making.[00:35:20] Why teams avoid metrics and tracing: The organizational friction of adding “one more component.”[00:42:59] Local observability with Grafana: Using visual feedback to spot waterfalls, sequential work, and hidden coupling.[00:50:00] Non-technical book recommendations: What Lucas reads and recommends outside of software.Links & ReferencesGuest and CompanyLucas Roesler: https://lucasroesler.com/Contiamo: https://contiamo.com/SocialMastodon: https://floss.social/@theaxerBluesky: https://bsky.app/profile/theaxer.bsky.socialBooks MentionedThe Wheel of Time (Robert Jordan): https://en.wikipedia.org/wiki/The_Wheel_of_TimeAccelerando (Charles Stross): https://en.wikipedia.org/wiki/AccelerandoCharles Stross: https://en.wikipedia.org/wiki/Charles_StrossThanks to Our Sponsor!Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time!  Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on: Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts. Keep up to date with the Maintainable Podcast by joining the newsletter.

    Lucas Roesler: The Fast Feedback Loop Advantage

  8. 21 Jan

    Brittany Ellich: Using AI to Maintain Software, Not Rewrite It

    Rewrites are seductive. Clean slates promise clarity, speed, and “doing it right this time.” In practice, they’re often late, over budget, and quietly demoralizing. In this episode of Maintainable, Robby sits down with Brittany Ellich, a Senior Software Engineer at GitHub, to talk about a different path. One rooted in stewardship, readability, and resisting the urge to start over. Brittany’s career began with a long string of rebuild projects. Over time, she noticed a pattern. The estimates were wrong. Feature development stalled. Teams burned energy reaching parity with systems they’d already had. That experience pushed her toward a strong belief: if software is in production and serving users, it’s usually worth maintaining. [00:00:57] What well-maintained software actually looks like For Brittany, readability is the first signal. If code can’t be understood, it can’t be changed safely. Maintenance begins with making systems approachable for the next person. [00:01:42] Rethinking technical debt She explains how her understanding of technical debt has evolved. Rather than a fixed category of work, it’s often anything that doesn’t map directly to new features. Bugs, reliability issues, and long-term risks frequently get lumped together, making prioritization harder than it needs to be. [00:05:49] Why AI changes the maintenance equation Brittany describes how coding agents have made it easier to tackle small, previously ignored maintenance tasks. Instead of waiting for debt to accumulate into massive projects, teams can chip away incrementally. (Related: GitHub Copilot and the Copilot coding agent workflow she’s explored.) [00:07:16] Context from GitHub’s billing systems Working on metered billing at GitHub means correctness and reliability matter more than flash. Billing should be boring. When it’s not, customers notice quickly. [00:11:43] Navigating a multi-era codebase GitHub’s original Rails codebase is still in active use. Brittany relies heavily on Git blame and old pull requests to understand why decisions were made, treating them as a form of living documentation. [00:25:27] Treating coding agents like teammates Rather than delegating massive changes, Brittany assigns agents small, well-scoped tasks. She approaches them the same way she would a new engineer: clear instructions, limited scope, and careful review. [00:36:00] Structuring the day to avoid cognitive overload She breaks agent interaction into focused windows, checking in a few times a day instead of constantly monitoring progress. This keeps deep work intact while still moving maintenance forward. [00:40:24] Low-risk ways to experiment Improving test coverage and generating repository instructions are safe entry points. These changes add value without risking production behavior. [00:54:10] Navigating team resistance and ethics Brittany acknowledges skepticism around AI and encourages teams to start with existing backlog problems rather than selling AI as a feature factory. [00:57:57] Books, habits, and staying balanced Outside of software, Brittany recommends Atomic Habits by James Clear, sharing how small routines help her stay focused. The takeaway is clear. AI doesn’t replace engineering judgment. Used thoughtfully, it can support the unglamorous work that keeps software alive. Good software doesn’t need a rewrite. It needs caretakers. References MentionedGitHub – Brittany’s current role and the primary environment discussedGitHub Universe – Where Brittany presented her coding agent workflowAtomic Habits by James Clear – Brittany’s recommended book outside of techOvercommitted - Podcast Brittany co-hostsThe Balanced Engineer Newsletter – Brittany’s monthly newsletter on engineering, leadership, and balanceBrittany Ellich’s website – Central hub for her writing and linksGitHub Copilot – The AI tooling discussed throughout the episodeHow the GitHub billing team uses the coding agent in GitHub Copilot to continuously burn down technical debt – GitHub blog post referencedThanks to Our Sponsor!Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks. It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications. Keep your coding cool and error-free, one line at a time!  Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on: Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts. Keep up to date with the Maintainable Podcast by joining the newsletter.

    Brittany Ellich: Using AI to Maintain Software, Not Rewrite It
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About

Do you feel like you're hitting a wall with your existing software projects? Are you curious to hear how other people are navigating this? You're not alone. On the Maintainable Software Podcast, Robby speaks with seasoned practitioners who have overcome the technical and cultural problems often associated with software development. Our guests will share stories in each episode and outline tangible, real-world approaches to software challenges. In turn, you'll uncover new ways of thinking about how to improve your software project's maintainability.

Information

  • Creator
    Robby Russell
  • Years Active
    2019 - 2026
  • Episodes
    229
  • Rating
    Explicit
  • Copyright
    © 2019-2026 Maintainable Software Podcast
  • Show Website
    Maintainable

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