1. **Analyze the Request:** * **Role:** News summarization expert. * **Task:** Summarize the provided article text. * **Constraints:** Concise (2-4 sentences). Focus on important information. No copy-pasting. No restating the title only (add new info). Answer in English. * **Input:** Title ("Subverting AI Agent Logging with a Git Post-Commit Hook"), Source (hackernews), Article Text (essentially just the title repeated as a summary/description). 2. **Analyze the Input Text:** * The "Article Text" provided is extremely sparse: "Subverting AI Agent Logging with a Git Post-Commit Hook". * There is *no body text* provided in the prompt to summarize. * *Self-Correction/Reality Check:* Usually, in these prompts, if the text is missing, I should look for context or assume the prompt implies I should know the content based on the title if it's a famous concept, or strictly summarize what is there (which is impossible without adding external knowledge). * *However*, often in these datasets, the "Article Text" might be truncated or the user *expects* me to explain the concept briefly based on the technical subject matter implied by the title, treating it like a "explain this concept" task, or simply stating that the text is insufficient. * *Alternative interpretation:* The user *made a mistake* and didn't paste the body. * *Best approach for an "AI Assistant":* Since I cannot browse the live web for a specific Hacker News story in this specific isolated context (unless I have search tools, which I don't here), I must rely on the text provided. If the text is just the title, I can't summarize the *content* of the article. * *Wait, looking at the
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Last night I was chatting to my friend (and fellow Three Rings volunteer) Ollie about our respective workplaces and their approach to AI-supported software engineering, and it echoed conversations I’ve had with other friends. Some workplaces, it seems, are leaning so-hard into AI-supported software development that they’re berating developers who seem to be using the tools less than their colleagues! That’s a problem for a few reasons, principal among them that AI does not make you significantly faster but does make you learn less.1. I stand by the statement that AI isn’t useless, and I’ve experimented with it for years. But I certainly wouldn’t feel very comfortable working somewhere that told me I was underperforming if, say, my code contributions were less-likely than the average to be identifiably “written by an AI”. Even if you’re one of those folks who swears by your AI assistant, you’ve got to admit that they’re not always the best choice. I spoke to another friend, E, whose employers are going in a similar direction. E joked that at current rates they’d have to start tagging their (human-made!) commits with fake AI agent logs in order to persuade management that their level of engagement with AI was correct and appropriate.3 Supposing somebody like Ollie or E or anybody else I spoke to did feel the need to “fake” AI agent logs in order to prove that they were using AI “the right way”… that sounds like an excuse for some automation! I got to thinking: how hard could it be to add a git hook that added an AI agent’s “logging” to each commit, as if the work had been done by a robot?4 Turns out: pretty easy… Here’s how it works (with source code!). After you make a commit, the post-commit hook creates a file in .agent-logs/ , named for your current branch. Each commit results in a line being appended to that file to say something like [agent] first line of your commit message , where agent is the name of the AI agent you’re pretending that you used (you can even configure it with an array of agent names and it’ll pick one at random each time: my sample code uses the names agent , stardust , and frantic ). There’s one quirk in my code. Git hooks only get the commit message (the first line of which I use as the imaginary agent’s description of what it did) after the commit has taken place. Were a robot really used to write the code, it’d have updated the file already by this point. So my hook has to do an --amend commit, to retroactively fix what was already committed. And to do that without triggering itself and getting into an infinite loop, it needs to use a temporary environment variable. Ignoring that, though, there’s nothing particularly special about this code. It’s certainly more-lightweight, faster-running, and more-accurate than a typical coding LLM. Sure, my hook doesn’t attempt to write any of the code for you; it just makes it look like an AI did. But in this instance: that’s a feature, not a bug! 0 comments