3 ms·

My view is at the end of 35 years in the industry the current wave of “software” and “systems” engineering coming out of the AI harness community is pretty garb

My view is at the end of 35 years in the industry the current wave of “software” and “systems” engineering coming out of the AI harness community is pretty garbage. But web technology and distributed systems followed a very similar arc, as did protocols and memory management; and microcode before them. But it feels like this is particularly bad because the mistakes being made are plainly obvious to the grey heads who have been here a while. As opposed to before when mistakes were in new domains being explored, these are mistakes made before and have good solutions to. I feel people too readily blame the LLMs themselves for this, but I’ve found LLMs know the history of computing thought evolution better than anyone I’ve ever encountered. Once you push them in the right direction, ground them in the philosophy of thought of hard won engineering ideas, they are astoundingly precise and accurate in their read and application (keeping every session grounded is the trick!). So it’s not the machines making these same mistakes with ready conceptual frameworks around them, it’s the 22 year old gatekeepers dashing head first into wall after wall, when we painstakingly built the door two feet to the left about the time they were gestating.

odjeifnejdjej 12d ago
> but I’ve found LLMs know the history of computing thought evolution better than anyone I’ve ever encountered … Yeah… that’s… pretty much how these LLMs work and their main selling point, actually. Not sure what the “I’m such a greybeard with 350 years worth of experiencie” preamble gets you here besides just making you look like you barely understand what you are talking about.
- fnordpiglet 12d ago
  Because, knowing what to induce in its behavior is the key - by understanding things like Postels law, I can direct the agentic loop towards patterns of development that avoid the cascade of issues that seem endemic in modern agentic harnesses. In fact, this is a key insight, so it’s surprising you didn’t get the punchline. Trying to build things in a new way on a machine composed of the corpus of all the old ways is stupid. By eliciting the corpus of tried and true methods over the history of computing and process engineering as the grounding for how to develop and behave in processes, you offload a huge amount of the work in getting software that’s “right” for the domain you’re working in. But not being aware of them and being heedless that the techniques hard won were hard won by people at least or more smart than you facing similar problems is leading to a cycle in software that’s needlessly dumb, making stupid mistakes that are unnecessary. There was a great Star Trek NG episode where Picard is stranded on a planet with a creature that can only speak in metaphorical language. Every sentence it uses refers to an event in the past and you have to understand it’s history to understand it today. Because LLMs are entirely trained on historical corpus, there is no time in history when all the techniques and processes and system design thinking was more relevant. Like that episode, Darmok, you can elicit a wealth of experience by simply referring to a technique from the past - if you know what it is. Another way to put it , those that aren’t aware of their history are doomed to repeat it. The sad part of the situation is the coding agent you’re working with is fully trained on it, but unless you intentionally activate the semantic space and bring the concepts into its J-space you won’t maximize the value of that corpus. So, we are using these tools which are mostly crap duct-taped together, which suffer from flaws well understood for decades, mostly because of the driving human being ignorant of the available corpus.

odjeifnejdjej 12d ago

> but I’ve found LLMs know the history of computing thought evolution better than anyone I’ve ever encountered … Yeah… that’s… pretty much how these LLMs work and their main selling point, actually. Not sure what the “I’m such a greybeard with 350 years worth of experiencie” preamble gets you here besides just making you look like you barely understand what you are talking about.

fnordpiglet 12d ago
Because, knowing what to induce in its behavior is the key - by understanding things like Postels law, I can direct the agentic loop towards patterns of development that avoid the cascade of issues that seem endemic in modern agentic harnesses. In fact, this is a key insight, so it’s surprising you didn’t get the punchline. Trying to build things in a new way on a machine composed of the corpus of all the old ways is stupid. By eliciting the corpus of tried and true methods over the history of computing and process engineering as the grounding for how to develop and behave in processes, you offload a huge amount of the work in getting software that’s “right” for the domain you’re working in. But not being aware of them and being heedless that the techniques hard won were hard won by people at least or more smart than you facing similar problems is leading to a cycle in software that’s needlessly dumb, making stupid mistakes that are unnecessary. There was a great Star Trek NG episode where Picard is stranded on a planet with a creature that can only speak in metaphorical language. Every sentence it uses refers to an event in the past and you have to understand it’s history to understand it today. Because LLMs are entirely trained on historical corpus, there is no time in history when all the techniques and processes and system design thinking was more relevant. Like that episode, Darmok, you can elicit a wealth of experience by simply referring to a technique from the past - if you know what it is. Another way to put it , those that aren’t aware of their history are doomed to repeat it. The sad part of the situation is the coding agent you’re working with is fully trained on it, but unless you intentionally activate the semantic space and bring the concepts into its J-space you won’t maximize the value of that corpus. So, we are using these tools which are mostly crap duct-taped together, which suffer from flaws well understood for decades, mostly because of the driving human being ignorant of the available corpus.