nolita VS rbenv

Full disclaimer up top: I have been working on agents for about a year now building what would eventually become HDR [1][2].

The first issue is that agents have extremely high failure rates. Agents really don't have the capacity to learn from either success or failure since their internal state is fixed after training. If you ask an agent to repeatedly do some task it has a chance of failing every single time. We have been able to largely mitigate this by modeling agentic software as a state machine. At every step we have the model choose the inputs to the state machine and then we record them. We then 'compile' the resulting state-transition table down into a program that we can executed deterministically. This isn't totally fool proof since the world state can change between program runs, so we have methods that allow the LLM to make slight modifications to the program as needed. The idea here is that agents should never have to solve the same problem twice. The cool thing about this approach is that smarter models make the entire system work better. If you have a particularly complex task, you can call out to gp4-turbo or claude3-opus to map out the correct action sequence and then fall back to less complex models like mistral 7b.

The second issue is that almost all software is designed for people, not LLMs. What is intuitive for human users may not be intuitive for non-human users. We're focused on making agents reliably interact with the internet so I'll use web pages as an example. Web pages contain tons of visually encoded information in things like the layout hierarchy, images, etc. But most LLMs rely on purely text inputs. You can try exposing the underling HTML or the DOM to the model, but this doesn't work so well in practice. We get around this by treating LLMs as if they were visually impaired users. We give them a purely text interface by using ARIA trees. This interface is much more compact than either the DOM or HTML so responses come back faster and cost way less.

The third issue I see with people building agents is they go after the wrong class of problem. I meet a lot of people who want to use agents for big ticket items such as planning an entire trip + doing all the booking. The cost of a trip can run into the thousands of dollars and be a nightmare to undo if something goes wrong. You really don't want to throw agents at this kind of problem, at least not yet, because the downside to failure is so high. Users generally want expensive things to be done well and agents can't do that yet.

However there are a ton of things I would like someone to do for me that would cost less than five dollars of someones time and the stakes for things going wrong are low. My go to example is making reservations. I really don't want to spend the time sorting through the hundreds of nearby restaurants. I just want to give something the general parameters of what I'm looking for and have reservations show up in my inbox. These are the kinds of tasks that agents are going to accelerate.

[1] https://github.com/hdresearch/hdr-browser

When I was technical blogging on how to learn from open-source code [1], I used it quite frequently to get unstuck and/or to figure out how to tease apart a large question into multiple smaller functions. For example, I had no idea how to break up this long `sed` command [2] into its constituent parts, so I plugged it into ChatGPT and asked it to break down the code for me. I then Googled the different parts to confirm that ChatGPT wasn't leading me astray.

If I had asked StackOverflow the same question, it would have been quickly closed as being not broadly applicable enough (since this `sed` command is quite specific to its use case). After ChatGPT broke the code apart for me, I was able to ask StackOverflow a series of more discrete, more broadly-applicable questions and get a human answer.

TL;DR- I quite like ChatGPT as a search engine when "you don't know what you don't know", and getting unblocked means being pointed in the right direction.

1. https://www.richie.codes/shell

2. https://github.com/rbenv/rbenv/blob/e8b7a27ee67a5751b899215b...

Let’s get started. I prefer to manage my Ruby installations on my development machine with chruby paired with ruby-install. Another outstanding set of tools is rbenv with ruby-build. I highly recommend installing Ruby with one of those two sets of tools. Follow the instructions on their project’s READMEs. For this article, I’ll be running Ruby (MRI) v3.3.0.

By setting up your environment in isolation, you can prevent yourself from a lot of issues when experimenting with code. It makes your code behave more predictable due to the defined state of the runtime environment you are working with. This article should provide you with enough information to get started, but obviously, there is a lot more power embedded in NVM, Virtual Environment and RBEnv. So make sure to check their documentation.

There is this good resource that also talk about different ruby version manager from the Rbenv repository. With some links to benchmarks of ASDF and Rbenv.

rbenv (my personal favorite)

One suggestion would be to setup your install based on a development environment using git and a Ruby version manager like rvm or rbenv to allows you to setup a user controlled gemset and execution path.

rbenv for Ruby

git clone https://github.com/rbenv/rbenv.git ~/.rbenv

On MacOS, I haven't had a problem with character encodings in the terminal in ruby for a while, but used to. When I used to, it had to do with how ruby had been installed, in particular that it needed to be compiled linking against an appropriate readline library. Are you trying to use the ruby that came with debian? You might have more luck installing ruby yourself, and using a ruby version manager. rbenv might be the simplest for you.

For my local machine, I use RVM (head). Other options are rbenv and asdf.