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@@ -29,7 +29,7 @@ So what can be done? I don't know. Formal education is likely a lost cause: ince
* Security mindset: as well as being directly useful for ensuring security, always thinking about where your assumptions might be flawed or how something might go wrong is vital for reliability.
* Good code structuring, e.g. knowing when to disaggregate or aggregate modules. I think that lots of people, particularly when using OOP, are too quick to try and "break apart" interdependent code in a way which makes development much slower without actually providing much flexibility, but thousand-line files with global variables everywhere are hard to work on.
If you have been paying any attention to anything within the past [two years](https://openai.com/blog/openai-codex) or so, you're probably also aware that AI (specifically large language models) will obsolete, augment, change, or do nothing whatsoever to software engineering jobs. My previous list provides some perspective for this: ChatGPT (GPT-3.5 versions; I haven't used the GPT-4 one) can model computers well enough that it can [pretend to be a Linux shell](https://www.engraved.blog/building-a-virtual-machine-inside/) quite accurately, tracking decent amounts of state while it does so; big language models have vague knowledge of basically everything on the internet, even if they don't always connect it well; ChatGPT can [also](https://twitter.com/gf_256/status/1598104835848798208) find some vulnerabilities in code; [tool use](https://til.simonwillison.net/llms/python-react-pattern) [is continually](https://openai.com/blog/function-calling-and-other-api-updates?ref=upstract.com) [being](https://gorilla.cs.berkeley.edu/) [improved](https://twitter.com/emollick/status/1657050639644360706) (probably their quick-script-writing capability already exceeds most humans'). Not every capability is there yet, of course, and I think LLMs are significantly hampered by issues humans don't have, like context window limitations, lack of online learning, and bad planning ability, but these are probably not that fundamental.
If you have been paying any attention to anything within the past [two years](https://openai.com/blog/openai-codex/) or so, you're probably also aware that AI (specifically large language models) will obsolete, augment, change, or do nothing whatsoever to software engineering jobs. My previous list provides some perspective for this: ChatGPT (GPT-3.5 versions; I haven't used the GPT-4 one) can model computers well enough that it can [pretend to be a Linux shell](https://www.engraved.blog/building-a-virtual-machine-inside/) quite accurately, tracking decent amounts of state while it does so; big language models have vague knowledge of basically everything on the internet, even if they don't always connect it well; ChatGPT can [also](https://twitter.com/gf_256/status/1598104835848798208) find some vulnerabilities in code; [tool use](https://til.simonwillison.net/llms/python-react-pattern) [is continually](https://openai.com/blog/function-calling-and-other-api-updates) [being](https://gorilla.cs.berkeley.edu/) [improved](https://twitter.com/emollick/status/1657050639644360706) (probably their quick-script-writing capability already exceeds most humans'). Not every capability is there yet, of course, and I think LLMs are significantly hampered by issues humans don't have, like context window limitations, lack of online learning, and bad planning ability, but these are probably not that fundamental.
Essentially, your job is probably not safe, as long as development continues (and big organizations actually notice).