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Very cool. I've played around with something similar a few months ago (without the sandbox). The tricky part is to find a diff format that the AI can use intuit

Very cool. I've played around with something similar a few months ago (without the sandbox). The tricky part is to find a diff format that the AI can use intuitively to patch code in place, as otherwise you can blow out the token limits quite fast. I wonder to what extent AI is going to kill comprehensibility of the infrastructure. Over the weekend I did a bit of hacking with a friend. He wants to move out of AWS to Hetzner to save money, but wasn't experienced with Linux sysadmin so I was teaching him some stuff, and simultaneously pitching a little prototype I made of a server config tool. It's sort of like a higher level Ansible: you specify what packages and containers you want, the URLs they should be served on, and it goes ahead and configures unattended upgrades, Docker and Traefik to deliver that outcome. The idea was to extend it to configuring backups with BorgBackup and other common sysadmin-y tasks. He was enthused! The next day though, things changed. He'd signed up for GPT-4 and it'd just blasted out a pile of Ansible configs for him. He doesn't know Ansible but it didn't matter, he just iterated with the AI a few times and now the problem is solved. This makes me wonder if there's much point anymore in improving the usability of systems software (programming languages, databases, operating systems, clouds, anything driven by textual configuration). A basic assumption that underlies making better tools is that user's time is valuable, that intuitive and simple systems therefore have value. But the AI's time isn't valuable. It can read some poorly written docs, then the source code of the system, then people getting frustrated on StackOverflow, synthesize all that together and spit out whatever pile of inscrutable config files are needed and it can do so within seconds. Given this experience, if we extrapolate it forwards, then maybe within a couple of decades many organizations will be running on infrastructure that nobody understands at all. You get cases today where maybe individual programs or workflows are only understood by one guy, but those are extremes and it's understood that it's a bad situation. Maybe in future it'll be normal and to do anything companies will have to ask an AI to do it for them. We already see skills atrophy around things like UNIX sysadmin because nowadays everyone learns AWS instead, which is one reason they can charge so much money for it, so I think as people retire knowledge of how to run Linux systems will steadily disappear. But conceivably people will stop learning the cloud too, and then to do anything with servers it'll be AI or the highway. You can also apply this to programming languages. Why engage in a high effort project like inventing a Java or Rust if an AI can spit out correct C++ for you, given enough tries at writing tests? Does the motivation to produce anything beyond incremental improvements to existing tools disappear now? I keep flipping back and forth on the answer to this. On one hand, it seems pretty pointless to further develop this prototype now. Maybe Ansible or AI written shell scripts is the end of the road for making Linux easier to use. On the other hand, AI is remarkably human like. It can also make mistakes, and also benefits from well written docs, simple interfaces and good error messages. So maybe usability still has a purpose, albeit maybe we now need to master a new field of "usability studies for AI".

naasking 3y ago
> This makes me wonder if there's much point anymore in improving the usability of systems software (programming languages, databases, operating systems, clouds, anything driven by textual configuration). A basic assumption that underlies making better tools is that user's time is valuable, that intuitive and simple systems therefore have value. Yes, but the nature of the problem changes a little. Because LLMs are not guaranteed to produce correct results, this might incentivize configuration and programming languages where you can provide a separate specification, possibly generated by another LLM, against which the program or configuration is checked against. This then potentially saves the user a lot of iteration to correct simple mistakes. Another aspect is that simpler systems/languages are also simpler for LLMs to understand, so an LLM generating a program in a simpler, more expressive programming language has a higher chance of being correct.
politician 3y ago
> Given this experience, if we extrapolate it forwards, then maybe within a couple of decades many organizations will be running on infrastructure that nobody understands at all. 100%. I fully expect to see the fickle AI spirits of the Warhammer 40K universe retconned into the grandchildren of LLM-driven program synthesis.
bdamm 3y ago
> maybe within a couple of decades many organizations will be running on infrastructure that nobody understands at all We're already there. Not a single person is alive today that can fully comprehend any production deployment, from the atoms in the server room all the way up to the atoms projecting rendered browser output to an amazon shopper's eyeballs. Any even this seems like an arbitrary place to draw the limits of our infrastructure. But nevermind the blank spots in physics and psychology; even if we stick just to the server processes from conventional OS and chips, hardware and firmware, data and databases, there is nobody alive for whom all of this is a white box.
- mike_hearn 3y ago
  No single person, sure, but at every level there are people who do understand what's going on at that level. Abstractions help but behind the abstraction there are people who know how it works. What I'm getting at is, what if we get to the point where at some levels of the stack there are literally no people who understand that level? If your company's prod infrastructure is miles and miles of AI written and maintained shell scripts, or config files for tools that haven't been worked on for decades because AI-generated boilerplate is cheaper than adding new features? To the extent that even if you hire the right people they can't reverse engineer the stuff and even if they could the cost of making changes to it without AI is prohibitive, because it's all so low level? It's a weird thing to contemplate but seems like one of the more plausible scenarios in which society becomes as dependent on AI as we are today on electricity.

naasking 3y ago

> This makes me wonder if there's much point anymore in improving the usability of systems software (programming languages, databases, operating systems, clouds, anything driven by textual configuration). A basic assumption that underlies making better tools is that user's time is valuable, that intuitive and simple systems therefore have value. Yes, but the nature of the problem changes a little. Because LLMs are not guaranteed to produce correct results, this might incentivize configuration and programming languages where you can provide a separate specification, possibly generated by another LLM, against which the program or configuration is checked against. This then potentially saves the user a lot of iteration to correct simple mistakes. Another aspect is that simpler systems/languages are also simpler for LLMs to understand, so an LLM generating a program in a simpler, more expressive programming language has a higher chance of being correct.

politician 3y ago

> Given this experience, if we extrapolate it forwards, then maybe within a couple of decades many organizations will be running on infrastructure that nobody understands at all. 100%. I fully expect to see the fickle AI spirits of the Warhammer 40K universe retconned into the grandchildren of LLM-driven program synthesis.

bdamm 3y ago

> maybe within a couple of decades many organizations will be running on infrastructure that nobody understands at all We're already there. Not a single person is alive today that can fully comprehend any production deployment, from the atoms in the server room all the way up to the atoms projecting rendered browser output to an amazon shopper's eyeballs. Any even this seems like an arbitrary place to draw the limits of our infrastructure. But nevermind the blank spots in physics and psychology; even if we stick just to the server processes from conventional OS and chips, hardware and firmware, data and databases, there is nobody alive for whom all of this is a white box.

mike_hearn 3y ago
No single person, sure, but at every level there are people who do understand what's going on at that level. Abstractions help but behind the abstraction there are people who know how it works. What I'm getting at is, what if we get to the point where at some levels of the stack there are literally no people who understand that level? If your company's prod infrastructure is miles and miles of AI written and maintained shell scripts, or config files for tools that haven't been worked on for decades because AI-generated boilerplate is cheaper than adding new features? To the extent that even if you hire the right people they can't reverse engineer the stuff and even if they could the cost of making changes to it without AI is prohibitive, because it's all so low level? It's a weird thing to contemplate but seems like one of the more plausible scenarios in which society becomes as dependent on AI as we are today on electricity.