标签：#entire

I don’t know how to play an instrument, so obviously I built one as an app. Literally, everyone in my family can sign or play an instrument, and I’m the odd one out. And I know what you’re thinking, “Who cares? With AI, you can build almost anything.” I’m more excited about the technique I chose to build the app with my agent. Specifically I used context from the agent session that built the app to find and fix the most important gap in its Playwright tests. Here’s how I did it. Step 1: Install Entire Entire captures the prompts, transcripts, tool calls, and decisions behind agent-generated code, seamlessly connecting that underlying context to your Git commits through lightweight checkpoints. On macOS: brew tap entireio/tap brew install --cask entire Check out these instructions to install on your operating system. Step 2: Create the project I created an empty directory (or you can ask your agent to do this) mkdir music-app cd music-app Step 3: Enable Entire Before handing off any work to the agent, I initialized Entire directly within the repository because I wanted to capture my agent sessions: entire enable -y You can also target a specific agent (I personally use Codex): entire enable -y --agent codex This sets up the background hooks Entire relies on to capture agent activity, binding that session context directly to the commits generated along the way. Step 4: Turn the vague idea into a plan Rather than starting with a rigid technical spec, I simply shared my initial idea: I'm not entirely sure about the app i want to build..but i want to build a music app that enables me to play instruments even though idk how..this should use computer vision and it should be able to work with real instruments or just like "air" instruments as in there's no instrument there..but i am moving fingers and sounds are being made..and it should like im making real music. idk if this should be sonic pi..but i know i should use media pipe for it. lets start working on a plan togethe

Some engineering problems are only painful because they happen so rarely. Even with a coding agent, the frustration still feels the same. I’ll wrestle with a tool that isn't my daily driver, hit a wall of errors, finally find a resolution, and then I neglect to note the solution because the problem is "fixed." This happened to me recently with a custom, internal GitHub Actions workflow I use for a post-release DevRel task. To make it work, I need to pass a specific authentication token to run Entire in a headless mode. A couple of months ago, I sat down with my AI agent to configure this for the first time. Because Entire is new and our setup is completely undocumented, it took a grueling trial-and-error process to figure out how to generate the token via a local device-flow login. Eventually, we found the answer. I pasted the token into my GitHub secrets, the workflow turned green, and I went about my day without writing anything down. I rarely take notes now that I use coding agents, but it’s not a sustainable practice. I need something to take note of the resolution (even if it’s my agent). Today, when the token expired, I was back at square one. Why I Didn’t Make a Skill Normally, my instinct is to automate repetitive tasks by building a reusable workflow, like an Agent skill or a goose recipe. But a dedicated skill didn't make sense here: Low Frequency: This happens once every few months. Writing and maintaining code for a skill I barely use is textbook over-engineering. Security & Context: Generating an auth token involves sensitive device flows. I didn’t want a generic token-generation script floating around in my global automation suite. I wished my agent had a memory, so I could ask “ How did we get that GENERIC_ENTIRE_TOKEN last time?" and have it recall the context. Instead, I spent an hour re-debugging a problem I had already solved. It was just my agent and me making guesses. To break the loop before the next expiration, I decided to use Entire. (At the