Saved from the Grave

Every developer has a graveyard. Mine has about a dozen tombstones in it—projects I built with genuine enthusiasm, shipped to real users, and then slowly watched decay as life moved on. The commit history tells the story: frequent updates, then sporadic patches, then silence. The issues tab fills up with bug reports from people who actually use the thing, and each notification brings a small pang of guilt that I've learned to ignore.

This is the natural lifecycle of side projects, especially in open source. You build something useful, people find it, and then maintaining it becomes a second job you never signed up for. The lucky projects get handed off to new maintainers. Most just... fade.

But something has changed. And I think it's going to fundamentally alter how much software we can keep alive.

The Compounding Problem of Bit Rot

Bit rot is what happens when you look away. Dependencies go stale. Security vulnerabilities get discovered in packages you forgot you were using. The framework you built on releases a new major version with breaking changes. APIs you depend on deprecate endpoints. The ecosystem moves on, and your project stays frozen in time.

The insidious part is how it compounds. Skip one round of updates and the next round gets harder. Skip two and you're looking at a weekend project just to get back to current. Skip a year and you might need to rewrite half the codebase. The activation energy required to maintain something grows exponentially with neglect.

This creates a brutal calculus for maintainers. You have limited time and energy. Do you spend it on the new thing that excites you, or on updating dependencies for a project you finished thinking about months ago? The new thing wins almost every time. It's not laziness—it's rational prioritization given finite resources.

The human cost is real. Users file issues that never get addressed. Forks proliferate as people patch things themselves, fragmenting the community. Good software that solves real problems becomes unsafe to use because nobody's keeping up with security patches. The maintainer feels guilty; the users feel abandoned. Everyone loses.

Why the Old Solutions Weren't Enough

We've had automated tools for years. Dependabot and Renovate will dutifully open pull requests when your dependencies are outdated. This helps, but it doesn't solve the problem.

These tools can tell you that something needs updating. They can't navigate the cascade of breaking changes that comes with it. When you update package A and suddenly your tests fail because package B is incompatible with the new version of A, and fixing that requires updating package C which has a new API—that's where humans have always had to step in. And that's exactly the tangled, tedious work that makes maintenance feel like a chore.

The other "solution" is willpower. I'll get to it this weekend. I'll set aside time next month. But this doesn't scale. You can't maintain ten projects on heroic weekend efforts. You can't even maintain three. The backlog grows faster than you can clear it.

What's Actually Different Now

Here's something worth noting: the models themselves are quickly becoming commoditized. Claude, GPT, Gemini—they're all pretty good. They all do a good enough job for most tasks. The real differentiator isn't the model. It's the infrastructure around it.

The new generation of AI coding tools isn't just autocomplete with better suggestions. Tools like Claude Code can reason about codebases holistically. They can read your entire project, understand how the pieces fit together, and make coordinated changes across multiple files while keeping everything consistent.

More importantly, they can handle the tedious parts of maintenance that humans find exhausting. Updating a dependency isn't intellectually challenging—it's just time-consuming and fiddly. Following the thread of breaking changes through a codebase is exactly the kind of systematic work that AI handles well and humans handle poorly.

But what really makes this work is the tooling layer. Project context files; things like CLAUDE.md orAGENTS.md,give agents the qualitative understanding of what a project is actually about. Not just the code structure, but the intent. What matters to the developers. What trade-offs they've made and why. Even something this simple dramatically improves an agent's ability to make good decisions.

Then there's the workflow infrastructure: subagents that can be spawned for parallel tasks, commands and skills that encode specific workflows, validation mechanisms that tell the agent how to verify its own work. How do I run linting and formatting? What needs to pass before I can call something done? How do I confirm the app is actually working? These can be embedded directly in the project and run anywhere—in the cloud or on a local machine. The agent doesn't just make changes; it knows how to check that those changes are correct.

A Sunday Afternoon Experiment

I decided to test this with Listr, a Nostr lists application I built. It's a real project with real users—built on SvelteKit with Svelte 5, TailwindCSS, the Nostr Development Kit, and a handful of other modern web dependencies. It was working, but it had grown stale. Dependencies were outdated. Users had reported bugs I hadn't found time to address. The kind of low-grade neglect that every maintainer recognizes.

On a Sunday afternoon, I pointed OpenCode at the project. My involvement was minimal—a prompt every thirty minutes or so while I did other things. The interface is a CLI that runs in your terminal; you give it a task and it works through it, reading files, making changes, running tests. I'd check in occasionally, approve what it had done, and point it at the next thing.

The results were more substantial than I expected. One user had reported that the feeds page wouldn't load at all. The cause? The app was trying to fetch from a relay that had gone offline and no longer existed. A frustrating bug for users, but exactly the kind of thing that's tedious to track down and trivial to fix once you find it. The agent found it and fixed it.

But we went beyond bug fixes. The app had been using server-side rendering in ways that added complexity without much benefit. We simplified the architecture, moving to a purely client-side approach that's easier to reason about and deploy. For long lists—contact lists with a thousand people, for example—the app used to take many seconds to load because it was rendering everything at once. We added pagination: show the first 50 entries, then load more as you scroll. The app now feels snappy.

We even added a new feature. Nostr recently introduced starter packs (kind 39089)—a new list type that lets users share curated groups of people to follow as a bundle. Adding support for them would have been a weekend project on its own. With the agent, it happened in one of those thirty-minute windows between prompts.

By the end of the afternoon, the project wasn't just current—it was better than it had been before. Dependencies updated. Bugs fixed. Architecture simplified. New feature shipped. This wasn't maintenance; this was meaningful improvement, accomplished in the background of a lazy Sunday.

Where This Is Heading

What I experienced was a transitional state. I was still in the loop, prompting periodically, reviewing changes. But we're rapidly moving toward something more autonomous.

The near-future version of this is a background agent that monitors your projects continuously. It watches for outdated dependencies and updates them. It triages incoming issues and fixes the straightforward bugs automatically. It keeps CI green and security patches applied.

The human gets involved only when the agent encounters something it can't resolve on its own: a design decision with no clear right answer, an ambiguous user request that needs clarification, a significant architectural change that warrants approval. Everything else just... happens.

This isn't about replacing developers. It's about extending what one person can realistically maintain. Instead of choosing between your three most important projects and letting the others die, you can keep a dozen projects healthy. The economics of maintenance change completely.

The Bigger Picture

Open source sustainability has always been framed as a funding problem, and it partly is. But it's also a time problem. Even well-funded maintainers burn out because there's just too much work. The ratio of maintenance burden to available human attention has been unsustainable.

AI doesn't solve the funding problem. But it dramatically changes the time equation. The amount of software one person can keep alive and healthy is about to increase by an order of magnitude. Projects that would have been abandoned can now stay maintained. The long tail of useful software—all those tools that serve small but real needs—gets longer and more viable.

We're not going to see fewer abandoned projects overnight. But the equilibrium is shifting. The activation energy required to maintain something just dropped significantly. The guilt-inducing backlog of issues becomes manageable. The graveyard gets smaller.

Your Projects Don't Have to Die

If you have projects gathering dust—and if you're a developer, you almost certainly do—the barrier to reviving them has never been lower. That repository you haven't touched in a year, with the outdated dependencies and the issues you've been ignoring? It's not a weekend commitment anymore. It might be an afternoon.

The tools are here. They're not perfect, but they're good enough to handle the maintenance work that kills projects through slow neglect. Your old code doesn't have to become a tombstone.

Try it with one of your neglected repos. You might be surprised what comes back to life.