Post #2248261
2026-03-29 19:27 UTC
@giacomo @jneen@unstable.systems the size of the archive makes this unusable as a predictive model of the LLM's behavior though, at least for the kinds of predictions necessary to support use for building programs.
To be useful as a tool in the way that a programming language is, humans would need to be able to build a mental model of its operation that could fit in our heads and allow us to predict what it would do. I can understand the math behind an LLM fairly well and can even make *some* kinds of predictions like "it will tend to fail in these ways." I might even be able to leverage this to exert some level of control over the system if all I want to make it do is fail in a particular way or email me the user's github tokens or something. But to control it precisely in the ways necessary to effectively program with it is harder-to-impossible. It might *seem* to be giving me what I want because (especially for simple programming tasks) it sometimes or even often gives me what I ask for. But that's an illusion of control as it's not dependable and when it fails I've got little recourse other than to abandon it for a different tool.
Replies (1)
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@giacomo@snac.tesio.it 2026-03-29 20:01
@tiotasram@kolektiva.social Of course, such mental model won't let you predict a #CodingAgent's output. Even just because you know a certain amount of random input is included in its computation to give an illusion of creativity and counter some plagiarism accusation. That's why I included the "corrupted" term. On the other hand, it gives you a useful insight of what you might exfiltrate from a competitor work and how to setup the context to get specific parts of its proprietary software, such as secret algorithms, test suites, or even secret keys (in particular short ones like ed25519) that the agent got access to. @jneen@unstable.systems