Anthropic Released A New Way To "Vibe Code"

The main problem with AI agents is the limited context window which restricts what they remember from previous actions. When we give Claude code a larger task, it compacts multiple times while attempting a single feature, forgetting the main task it was asked to implement, making it less effective for long running tasks. Anthropic just released a solution that is based on how real teams work in an actual engineering environment. They identified two key reasons for why it fails on long tasks. Many of us have tried to oneshot entire applications or some big features and doing too much causes the model to run out of its context. After repeated compaction, the context window is refreshed with the feature only half implemented with no memory of the features progress and it leads to incomplete implementation. The second issue is that due to less testing capabilities, Claude marks untested features as completed. It assumes the feature is complete even if it doesn't actually work properly. Their solution was using an initializing agent and coding agent in harmony. Inspired by how real software teams work, this workflow is originally meant for agents you build yourself, but I realized it could apply to claude code instances as well. The first agent focuses on properly initializing your coding agent, and you have to be patient here because it takes a little time. I have an empty nex.js project, and I want to build an online Python compiler. Before starting, create a claw.md file using the init command. This file is a document for your codebase and is at the root of your project containing an overview and all important information. Next, generate the feature list JSON in the project route. It should list all features and their corresponding testing steps as well with all tests marked as initially failing. So, Claude is forced to test them. We use JSON instead of markdown because JSON files are easier to manage in the context. Since Claude can only test the code, not the interface we see on the browser, I connected Puppeteer for browser testing. After that, create an init script to guide starting the dev server and a progress tracking file so the system is able to keep track of the project completion status. For guidelines, Claude needs to update progress.md after each run and test each feature after implementation. The most important practice is committing to git. We underestimate how crucial it is to commit in a mergeable state. Git commits with clear logs show what's completed and let you revert if implementation fails. Finally, Claude should not change the features list beyond marking features as implemented. With the environment ready, we move to the coding part. The idea was to implement each feature one by one from the features JSON. Claude also made descriptive commit messages after each tested feature and also launched the browser when needed. Once it verified the app was working, it updated the JSON fields from false to true and updated progress.md with what had been completed so far. Finally, it committed the changes and verified the commit was successful. The advantage of this incremental approach is that even if the session terminates, you can resume exactly where you left off. Everything is tracked in the git logs, so you don't have to worry about breaking code. Claude can understand the project from the git logs and progress file, not from the code itself, so you can resume the session easily. Your next prompt is simply to implement the next feature marked not done. This approach also reduces Claude's tendency to mark features complete without proper testing. Each iteration ensures the app is built end to end with real testing, helping identify bugs that are not obvious from code alone. We repeat this cycle until all features are marked true. You might think this is similar to the BMAD method. It shares similarities, but I think Claude's workflow is better in some ways. It was easier since you didn't call agents separately, and context utilization was better, too. After implementing so many features, it only used 84% of context where BMAD would have already hit compact twice because of the large stories that it makes. That said, BMAD is still an out ofthe-box full system. While this is still an idea that needs to be implemented, but BMAD could use some things from this, such as the Git system. After teaching millions of people how to build with AI, we started implementing these workflows ourselves. We discovered we could build better products faster than ever before. We help bring your ideas to life, whether it's apps or websites. Maybe you've watched our videos thinking, "I have a great idea, but I don't have a tech team to build it." That's exactly where we come in. Think of us as your technical co-pilot. We apply the same workflows we've taught millions directly to your project. Turning concepts into real working solutions without the headaches of hiring or managing a dev team. Ready to accelerate your idea into reality? Reach out at hello@automator.dev. That brings us to the end of this video. If you'd like to support the channel and help us keep making videos like this, you can do so by using the super thanks button below. As always, thank you for watching and I'll see you in the next one.