Over the holidays, Alex Lieberman had an idea: What if he could create Spotify “Wrapped” for his text messages? Without writing a single line of code, Lieberman, a co-founder of the media outlet Morning Brew, created “iMessage Wrapped”—a web app that analyzed statistical trends across nearly 1 million of his texts. One chart that he showed me compared his use of lol, haha, 😂, and lmao—he’s an lol guy. Another listed people he had ghosted.
Lieberman did all of this using Claude Code, an AI tool made by the start-up Anthropic, he told me. In recent weeks, the tech world has gone wild over the bot. One executive used it to create a custom viewer for his MRI scan, while another had it analyze their DNA. The life optimizers have deployed Claude Code to collate information from disparate sources—email inboxes, text messages, calendars, to-do lists—into personalized daily briefs. Though Claude Code is technically an AI coding tool (hence its name), the bot can do all sorts of computer work: book theater tickets, process shopping returns, order DoorDash. People are using it to manage their personal finances, and to grow plants: With the right equipment, the bot can monitor soil moisture, leaf temperature, CO2, and more.
Some of these use cases likely require some preexisting technical know-how. (You can’t just fire up Claude Code and expect it to grow you a tomato plant.) I don’t have any professional programming experience myself, but as soon as I installed Claude Code last week, I was obsessed. Within minutes, I had created a new personal website without writing a single line of code. Later, I hooked the bot up to my email, where it summarized my unread emails, and sent messages on my behalf. For years, Silicon Valley has been promising (and critics have been fearing) powerful AI agents capable of automating many aspects of white-collar work. The progress has been underwhelming—until now.
this post was submitted on 16 Jan 2026
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In all fairness, while this is a particularly bad case, the fact that it's often very difficult to safely fiddle with environment variables at runtime in a process, but very convenient as a way to cram extra parameters into a library have meant that a lot of human programmers who should know better have created problems like this too.
IIRC, setting the timezone for some of the Posix time APIs on Linux has the same problem, and that's a system library. And IIRC SDL and some other graphics libraries, SDL and IIRC Linux 3D stuff, have used this as a way to pass parameters out-of-band to libraries, which becomes a problem when programs start dicking with it at runtime. I remember reading some article from someone who had been banging into this on Linux gaming about how various programs and libraries for games would
setenv()to fiddle with them, and races associated with that were responsible for a substantial number of crashes that they'd seen.setenv()is not thread-safe or signal-safe. In general, reading environment variables in a program is fine, but messing with them in very many situations is not.searches
Yeah, the first thing I see is someone talking about how its lack of thread-safety is a problem for TZ, which is the time thing that's been a pain for me a couple times in the past.
https://news.ycombinator.com/item?id=38342642
Back on your issue:
Note also that a number of webservers used to fork to handle requests
and I'm sure that there are still some now that do so, though it's certainly not the highest-performance way to do things
and in that situation, this code could avoid problems.
searchs
It sounds like Apache used to and apparently still can do this:
https://old.reddit.com/r/PHP/comments/102vqa2/why_does_apache_spew_a_new_process_for_each/
But it does highlight one of the "LLMs don't have a broad, deep understanding of the world, and that creates problems for coding" issues that people have talked about. Like, part of what someone is doing when writing software is identifying situations where behavior isn't defined and clarifying that, either via asking for requirements to be updated or via looking out-of-band to understand what's appropriate. An LLM that's working by looking at what's what commonly done in its training set just isn't in a good place to do that, and that's kinda a fundamental limitation.
I'm pretty sure that the general case of writing software is AI-hard, where the "AI" referred to by the term is an artificial general intelligence that incorporates a lot of knowledge about the world. That is, you can probably make an AI to program write software, but it won't be just an LLM, of the "generative AI" sort of thing that we have now.
There might be ways that you could incorporate an LLM into software that can write software themselves. But I don't think that it's just going to be a raw "rely on an LLM taking in a human-language set of requirements and spitting out code". There are just things that that can't handle reasonably.