You know what's a bad idea from an engineering (that thinky thing we used to do as part of building software) perspective?
Building a dependency on an expensive remote API into your system.
This isn't just me bloviating, I've been down this road before. In my case I had a project using LLMs to automatically edit videos provided by Hollywood content owners. It seemed like a decent application, but LLMs are structurally unsuited for dealing with user data like this. The way that the prompt is evaluated means there is no separation between system and user input, so once you start dealing with a wide variety of topics you pretty quickly run into walls.
One example - ChatGPT refusing to summarize and pick a top segment from a news program because it contained references to a murder-suicide, and both murder and suicide are included in the many prohibited topics that are filtered in ChatGPT replies. This was through their API, not the regular user interface, so it is in theory as unrestricted as access gets. But because the LLM cannot be trusted to behave properly around the topic, they have to filter anything which touches it.
Structurally, I don't see a way this can be overcome - LLMs by design mix the entire prompt together, it's not like a parameterized SQL query where you can isolate the user and system data. That means that a long or bold enough user input is often enough to outweigh the system prompt, and that causes the LLM to veer into unpredictable territory.
I tend to agree quite a bit.
I created a ambient background agent for my projects that does just that.
It is there, in the background, constantly analysing my code and opening PRs to make it better.
The hard part is finding a definition of "better" and for now it is whatever makes the longer and type checker happy.
But overall it is a pleasure to use.
This stuff is negative value.
But the more you read the article the more the point is lost. The prescriptions given aren't ambient?
CLI: a good command-line interface makes it easy for an agent loop to interact with your system and saves tokens.
Specs: Declarative configs, schemas, manifests. Artifacts that state the desired outcome, not the steps.
Reconciliation loops: you declare the target state, let the system continuously converge toward it. Detect if something drifts.
The gripe seems to be specifically with being able to chat with the AI. Yes, ideally the AI just knows to do stuff. But the chat interface is also the reason every Bob and Sarah has chatGPT in their pocket. It's also just growing pains.
It might be nice to have something simple and cheap for basic text classification, but I'm not sure what to use. (My websites are written in Deno.)
Moltbot is OpenClaw, AutoGPT was born significantly before. I just couldn’t read after the first paragraph, I’ve lost the trust entirely, whatever/whoever wrote it.