Revibing with Amp is the practice of using Sourcegraph’s Amp as an agentic refactor engine to bend an existing codebase into a new shape and vibe, without throwing everything away and starting from scratch. Instead of treating a legacy project as a monolith to be replaced, Amp lets you iterate in place. You ask high-level questions, give short specs, and Amp uses whole-code understanding to propose multi-file changes, open pull requests and help you keep behaviour intact while the internals evolve. Revibing with Amp works best when you treat the tool as a collaborator inside a spec-driven, test-heavy workflow, not as a magic “rewrite this app” button.
# What Amp Is Good At Amp shines when the change you want is structural, repetitive and visible in the code. It is very good at understanding where things live. Because it is built on Sourcegraph’s indexing and search, Amp can find all the call sites of a function, all the uses of a model, or every endpoint that touches a particular piece of data. This whole-code awareness is what makes safe, large-scale edits possible. It is strong at proposing and applying multi-file edits. You can describe a refactor in natural language and Amp will update many files together, adjusting imports, fixing types and updating tests, then wrap the result in a branch and pull request. Amp also fits well into git-centric workflows. Its default posture is “generate a PR for humans to review”, so you can keep your normal code review, CI and governance processes while using AI to handle most of the grunt work.
# Revibing Patterns That Work Really Well Certain revibing moves fit Amp almost perfectly. Renames and concept shifts are a natural fit. If you decide that `Review` should be called `Note` throughout the system, or that `Shelf` should become `Orbit`, Amp can chase every reference across backend, frontend and tests, and keep the change coherent. API migrations work well. When you have an old endpoint shape that needs to be upgraded everywhere, you can describe the old versus new contract and let Amp update controllers, clients and test fixtures, then run your test suite. This is especially powerful for version bumps of internal APIs. Internal structural refactors benefit greatly. Extracting helpers, consolidating duplicate patterns, flattening inheritance hierarchies or introducing new modules can be described once and applied widely. Amp can see where patterns repeat and suggest common abstractions you might miss. Documentation and comments are fertile ground. Once you have reshaped code, you can ask Amp to sync docstrings, README sections and architecture notes with the new reality, turning otherwise tedious clean-up into a quick spec and a PR. In a BookWyrm-style context, revibing with Amp can help you isolate subsystems such as notifications, reading events or ActivityPub handlers into clearer modules, ready for deeper redesign or extraction into new services.
# Revibing Areas That Are Hard Other parts of a system remain stubbornly difficult, even with Amp. Implicit business rules are hard to move safely. When behaviour emerges from a web of conditionals, data quirks and side effects, Amp can refactor the surface but it cannot guarantee that it has preserved all the invisible assumptions. Without strong tests and domain knowledge you can easily introduce subtle regressions. Cross-service behaviour is tricky. Revibing one codebase in isolation does not automatically keep it aligned with other services, jobs or external consumers. If you change a payload shape or timing assumption, Amp cannot magically fix dependent systems it cannot see. Performance-sensitive sections demand extra care. Hot paths, tight loops and caching layers may look logically equivalent after a refactor but behave very differently under load. Amp does not replace realistic benchmarks and performance tests. Security, privacy and compliance invariants are fragile under large edits. Automated refactors can accidentally loosen input validation, error handling or access control. Amp can help you reason about these concerns, but careful human review remains essential. User experience and “vibe” are not purely mechanical. Amp can rearrange components and rename things, but the feel of a UI, the pacing of flows and the social expectations around a feature are socio-technical. Revibing with Amp can improve code that implements a feature, but cannot automatically regenerate a community or a culture around it.
# Using Amp Safely For Revibing Safe revibing with Amp starts with tests. The more of the system’s behaviour you can pin down with unit, integration and end-to-end tests, the more freedom you have to ask Amp for sweeping changes and trust the results. It helps to work in small, explicit specs. Before asking Amp to refactor, write a short specification in your wiki describing what should change and what must not. Then give Amp that spec as context, let it propose a PR, and review the diff against your intent. Incremental waves are more robust than “big bang” rewrites. You can use Amp to revibe one subsystem at a time, merging clean, well-described PRs instead of one enormous branch. This makes rollbacks and blame much easier if something goes wrong. Observability should be part of the plan. When you change a heavily used path, pair Amp’s refactor with extra metrics, logging or tracing so you can see if error rates or latencies shift after deploy. Revibing is not complete until the running system proves it is healthy.
# Revibing With Amp In A Commons Context In a commons-oriented project, revibing with Amp is not just about cleaner code. It is a way to align a legacy codebase with new governance, new concepts and new collaborators without burning everything down. You can stand up the old system as a reference implementation, use Amp to map and document it, and then gradually reshape it towards your Guide To Everything, Bookshop Coop Store or federated book commons vision. Amp handles the mechanical work of renaming, extraction and migration while humans focus on tests, specs and regovernance. Revibing with Amp, done well, lets a small cooperative keep evolving its tools at the pace of its ideas. The trick is to lean on Amp where structure is clear and repeatable, and to keep humans in the loop wherever meaning, trust and community are at stake.