I would not hire someone who eschews LLMs as a tool. That does not mean I would accept someone mindlessly shoving its code through review, as it is a tool, not a wholesale solution.
That ship seems to have sailed at the same time boxed software went extinct.
How many companies still have dedicated QA orgs with skilled engineers? How many SaaS solutions have flat out broken features? Why is SRE now a critical function? How often do mobile apps ship updates? How many games ship with a day zero patch?
The industries that still have reliable software are because there are regulatory or profit advantages to reliability -- and that's not true for the majority of software.
Not sure I agree. Where it matters people will prefer and buy products that "just work" compared to something unreliable.
People tolerate game crashes because you (generally) can't get the same experience by switching.
People wouldn't tolerate f.e broswers crashing if they can switch to an alternative. The same would apply to a lot of software, with varying limits to how much shit will be tolerated before a switch would be made.
So majority of software we have now is unreliable piles of sh*t. Seems to check out, with how often I need to restart my browser to keep memory usage under control
> Is the AI going to know about pacing? About character motivations? About interconnecting disparate plots?
For LLMs like GPT-4, this all seems reasonable to account for and assume the LLM is capable of processing, given appropriate guidance/frameworks (of which may be just classical programming).
I wonder if OpenAI will fast follow; usually they're the ones to throw down the gauntlet. That being said, you can play around with OpenAI with a similar architecture of vision + agent + exec + loop using Donobu, though it is constrained to web browsers.
We make some attempts to be human, though dedicated bot checks may well find Donobu. The things that work in Donobu's favor is that it is intended to be run locally, and so requests coming from it do not come from some giant Amazon or Google data center IP address range. Also, Donobu is not optimized for mass web scraping (it has deliberate delays included), so it does not tend to get rate limited. That all being said, there is some low hanging fruit we will be working on in order to be generally less bot-ish.
There is an API at localhost:31000 and there is a hidden one-shot mode to rerun a flow if you happen to work the command line just right, so it is technically feasible to integrate with CI/CD, though all that needs proper docs before we would expect anyone to reasonably do that. It is on our roadmap though, and the lift is not high.
Regarding exporting to Javascript, seems you found the button for it. Though we like to call it a "draft of a script", as it is generated by an eager-to-please LLM, so having a real engineer give it a look over is useful. It should be enough to get you off the ground though.
Yes, internally we have an Ubuntu build working but it has not been publicly released yet (its release is on the near-term roadmap). If you have an immediate need, please reach out, and we can work it out.
Thank you for the direct actionable feedback, we will improve that messaging.
Regarding debugging your specific problem, when an API is attempted to be added, the local process attempts a 1-token request to the cheapest model with the GPT platform (in your case, gpt-4o-mini on OpenAI) to verify that the key works. Though, if the account has no balance, this request may fail even though it costs a fraction of a fraction of a penny (though anything that fails that request will cause the API key to be considered invalid).
We really did! :) Though, there were some unfun challenges around that, like getting distribution to work without having to get people to go through the pains of installing the JDK. Thankfully, since our UI is just a web browser, we did not have to go down the path of JavaFX or anything like that; our UI is just plain JS/HTML making API requests to a propped up server on localhost:31000 (for the curious).
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