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So if you design call it loop, call it graph, call it workflow, it's kind of all the same. If you design something that gets a trigger or gets an input and does something for you and maybe there's a decision in the way, boom, there's your graph.
Anyone using GitHub knows that it isn't cut out for agents. The workflow, the review process, the merge queue and many things are made for the previous era.
In addition to reliability issues, it often engenders a mind-numbing workflow and an environment where junior developers will never acquire the expertise to become senior developers capable of designing complex systems.
I'm watching people write these huge markdown files to give the agent context. How about you write documentation to give me context so I can have fully working examples.
Now the thing is you can of course write examples in your documentation and Rust makes all examples into tests. This means that if you change the underlying code now your test fails. And so this means that you can't even you can't forget to update your examples in your documentation.
And what this is is that in the guide level explanation, you explain your feature as if you were writing a guide as if the feature already existed. And in the reference level explanation, you explain your feature again as if it already existed, but as if it would be in the language reference instead of a tutorial.
So, I I think there's a lot of things we still try to imagine. What would be an AI-driven world look like, right? I think people are trying to like retrofit what already exists to fit what they think is a new workflow.
So, I think that the whole workflow of like reviewing code is very outdated. Like, I don't think the I think that the senior member, instead of like giving feedback on the code, they should be giving feedback on like how you give instruction to AI to produce better.
It used to be that you have documentation for other people who are going to use your library, but like you shouldn't do that anymore. Like you should have instead of HTML documents for humans, you have markdown documents for agents.
That's why I warn in my security documentation, don't use cheap models. Don't use Haiku or a local model. Even though I, I very much love the idea that this thing could completely run local. If you use a, a very weak local model, they are very gullible. It's very easy to, to prompt inject them.
Immediately cease trying to perform meaningful work via a chatbot (e.g. ChatGPT, Gemini on the web, etc.). Chatbots have real value and are a daily part of my AI workflow, but their utility in coding is highly limited because you're mostly hoping they come up with the right results based on their prior training, and correcting them involves a human (you) to tell them they're wrong repeatedly.
I don’t think this is very productive (expert users of a piece of software are notoriously bad at being able to tell if an explanation will be clear to non-experts), so I needed to find a way to identify problems with the man pages that was a little more evidence-based.
even if the tech is diffusing fast uh this time around for true economic growth to appear it has to sort of diffuse to a point where the work the work artifact and the workflow has to change and so that's kind of one place where I think uh the change management required for a corporation to truly change I think is something we shouldn't discount
I would say though that something that is kind of annoying to me is that we haven't yet figured out the bridging from the tinkering to the workflow quite as seamlessly as I would like.
The overall workflow is intuitive, especially for those new to formal evaluation processes. The UI guides you through creating datasets, running experiments, and annotating results.
It's basically the Vim of audio editing software, so I wouldn't recommend it if you don't have the time and energy to heavily invest in customizing it for your workflow, but I can move like lightning in this thing.
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