1,564 karma · joined July 29, 2016
ooof, that is a lot!
And we're supposed to believe them and start one-shotting our projects...
Curios to learn more, you do have some reference discussion or posts about that.
Edit: hmmm it seems actually to be more of a problem with the symbols that are constructed by combining two emojis with a zerowidth char. Like the heart on fire one.
For models such as Opus 5.5 nowadays, it seems that there is a fair overlap between those two intuitions in the family of tasks such as the recently shared HackerTube or Simone's game. But when we (humans) are in a different mindset about a task, we have to start editing md prompt files to coerces the model to abide by the "common sense" for the current task.
And yes model "common sense" is probably RLed or emergent patterns, but the point still stands.
It is a continuous fight in markdown guidance files to try to convince the models to refer back to the human in order to resolve ambiguities or important decisions like those. They've been RLed to hell and back for "safe" task completion and not "prematurely" stopping (which was an earlier failure mechanism). Which is useful and we've bore its fruits recently, but not without drawbacks.
is that actually a thing that was caught before?
Aside from this paper and K3.1 specifically, is there somewhere where I can read more a high-level introduction about the state of things in this niche? like the different types of faults like these models fail and ideally how to fish them out?
Those two types of reasoning, are those well definitely in the field generally, or are they mini-personal evals you run?
It has been frustrating trying to quantify why one pseudo-frontier model seems better at some non-coding tasks than another pseudo-frontier one.
> AI client is doing this on its own
Well, the llm api on its own cant do anything other than return a stream of text. The harness takes that and decides, this text is part of the response, this text is an edit tool call, this is a bash call to compile, runs the commands and calls the api again (as long as the llm didn't emit a stop_token. They now have a tool_call token the llm sends in between intermittent responses). And saves a session record to disk so that you can exit and resume the conversation later. It also auto injects things like agents.md etc...
I'd say thats the bare minimum. But then usually also includes hooks, introducing tool calls to allow the main session to launch "subagent", and the bits and pieces pile on....
So in effect, all what we're quarreling about here is whether it is _misleading_ to use "Likewise" to transition between two paragraphs without implying that the details of the first apply to the second?
I've been feeling that: much about keeping my sanity in this age is about noticing those patterns we keep falling in and arranging for "easier" ways to fall into healthier patterns. The annoying thing is that it doesn't seem that those "unhealthy" patterns were that easy to predict in order to avoid. Like some 15 years ago would it have been as obvious that we would get to such a consistently predictable pattern of "comfy spot on the couch or bed" and the unhealthy social and personal repercussions of that?
I just wish I start being able to predict those pitfalls earlier than the current few years per tech evolution.
That might be true, but it is definitely not the world we live in.
> Local-first session search, analytics, insights, and token use statistics for coding agents, supporting Claude Code, Codex, and more than 20 other agents.
solid piece of software
To be honest, I don't think the space between GPL/MIT and commercial closed source is explored enough. I'm aware there's a few examples of things in between, but they are not common knowledge and they don't satisfy everyone. It is not a space that is easy to search online for established wisdom and comparisons in.
I imagine because we're talking about a subcategory of "bad guys" who still like to stay within the confines of the law (supposedly at least).
Does that entail that LLMs are not in fact erasing our societal voices, only making it easier to adopt bland-corporate en-mass?
https://rmoff.net/2025/11/25/ai-smells-on-medium/
He doesn't link many examples, but at the end he gives the example of an author pumping out +8 articles in a week across a variety of topics. https://medium.com/@ArkProtocol1
I don't spend time on medium so I don't personally know.