> The run baseline was captured without a physical MAC; the current device is not durably bound to it.
> Engineering mode confirmation is the ESPHome component read-back; the LD2410 UART acknowledgement is not observed, so this is not proof the radar itself applied the sensitivity change.
It was only when native English speakers—or those I presumed were—started calling out how bad "GPT/Claude speak" has become that I realized I wasn't actually losing my grip on English as a second language. For a second, I thought, Oh, I learned this language on my own, but it seems I've hit a wall and need to study further. It didn't help that I've also been trying to acquire Swedish as a third language for a while now.
One thing that I find is that it doesn't seem to grasp levels of jargon-use. Like I ask a basic question, okay, a few questions later, suddenly there's abbreviations and weird formulations everywhere.
Not the original commenter, but I did this in all of them.
Their skills formats are basically identical, so I setup simlinks from their own skills directories into a shared one so Claude, Codex, Cursor, and anything else that comes out will all read and write to the same shared skills.
It's great having access to the same skills no matter the harness being used
I long for the day when AI will just say that directly: "your soldering sucks man" instead of the bizarre made up and jargon packed language they use now.
Sometimes when I get frustrated reading Opus/Fable 5+ output I pause my rage out briefly to wonder if it's because I'm just too dumb for the model or if the model is just terrible at English.
I'm not sure that telling it to "try explaining that again, simply and briefly" is helping my ego.
It's often simply misleading / bad writing. Here's one I just got about some crashes:
"If the crashes stop, the factory overclock is marginal; run a small negative offset."
This looks like it's saying: "If the crashes stop then we know the factory overclock is marginal." (This makes no sense.)
What it's trying to say is: "If the crashes stop then we can run a small negative offset, because the factory overlock is marginal."
What I would write: "If the crashes stop, we can avoid crashes by underclocking slightly. The speed difference between that and factory clock is marginal."
I'm guessing it's because the way the first one was written looks real smart and sophisticated, which I'm presuming the models are rewarded for, especially when they're fed all kinds of PhD papers and so on as high quality, high weight data
Is it possible that the first message is more information dense/less likely to be ambiguous than the latter? It’s clearly being selected for for some reason, maybe it’s an artifact of the tokenizer or specific training data, but I don’t know. If the use of jargon was complete cruft, I would expect it to be selected against during reinforcement learning
You’d think that, I thought that… but then I realized I’m just kidding myself thinking its output makes sense. It doesn’t. It doesn’t. Sometimes it might as well just speak tongues.
In other words, it ain’t you. It’s the model. It’s just genuinely bad.
Then you switch to ChatGPTs lineup and realize how things can actually be better. It took about a week to really get the feel for how to use their models… then I basically switched. I’ll check in every now and then when they actually make a deal about how opus “now makes sense”.
But honestly I’m half convinced Anthropic actually prefers the output of opus 5. I dunno why, but how else could you explain how such a thing got shipped? I mean somebody in the pipeline had to say “dude this model doesn’t make sense, you think we should fix it?” Right? Like it’s a pretty massive drop in quality for such a major brand in this space, you know? How did it make it out the door?!?
yeah, I just switched to the GPT models and it's a breath of fresh air.
as for the other guy, the claude talk is definitely not less ambiguous, it often is incredibly ambiguous and hard to parse, I have no clue why it produces such output, if not to fingerprint it?
it's really weird man. when Opus 5 came out, I was really confused. I saw a bunch of hype about how it's better than fable, but I just felt frustrated with it, although at times it'd do fine, but especially in Claude Code it'd just delve into the whole "load bearing" type of lingo real fast and I'd get a headache.
I don't think it's worth using even if it scores 2 points higher in some bs benchmark
it's definitely surprising how the magic and smoothness of 4.6 and such is no longer there with the >5 models
Same. I read it as "if the crashes stop [ when we test by reducing the clock ] then we know that the overclock applied by the factory is marginal [ ie it barely passed QC or maybe there wasn't proper QC to begin with ] so running with a small negative offset [ ie what we just tested ] can be expected to fix the problem for good". No idea if my reading is right given all the context I'm missing. Either way it's absolutely shit writing in the same way that golfed code is shit code (except when participating in a code golf competition).
I suspect this happens due to optimising for reasoning... if you insert a few words, it will suddenly start to make more sense.
"If the crashes stop, (that means) the factory overclock is marginal; (so) run a small negative offset. (to confirm this hypothesis)"
The core thought is basically avoid crashes -> caused by marginal overclock -> apply small -offset to test.
Which is exactly the order the sentence is in :P
I wonder if this is a result of them trying to cut token consumption by summarizing their RL training data, or maybe it's from how they anonymize user data for training.
Marginal - definition 2a: of, relating to, or situated at a margin or border.
Succinct and precise; a well crafted sentence. A marginal OC results in unpredictable crashes and can be corrected with a small offset; marginality describes the behavior and explains the solution.
Inscrutable clues casually conveyed can now be readily explained, at least, unlike the training data of [silence]. Brevity is the soul of wit, but perhaps also exasperated confusion.
This is actually a new skill I've been working on. Learning how to elicit concise and simple speech from models (and from people to!).
Whenever I come to a wall of complicated text I kick into gear and think through getting it to distill this into the high-level useful bits that I actually need to know.
I guess I could create an actual agent skill for this :) And next-gen models might eventually be trained to simplify their output themselves...
The most surprising part, however, is that when one model slops this into a plan, another model somehow is able to interpret it correctly enough to produce code to spec.
I have shared this dismay. I’ll have opus create a plan, I read it doubtfully. And then sonnet implements it. I am surprised it went so well. I theorize the redundant verbosity effectively builds rails that help keep llm focused. I will experiment with such rails myself.
I suspect it's because the different models co-evolve? The labs train on one model implementing the plans of another model, especially in the same family of models (like Fable to Sonnet).
It was saying it can't tie the calibration results to a device, because it doesn't know the MAC address (I never asked it to look at the MAC address, it way overengineered things).
It also couldn't see the UART communication and could only see the web API endpoint, hence the rest of the slop.
I'm also worried about that these days. They posted an article a while back that Signal costs $50m per year to run, and that they make that money from "things". Together with how low a profile they keep, I'm not convinced they aren't a honeypot.
I love Signal and it's still my preferred messenger, but if it came out that they're backed by some government agency, I wouldn't be extremely surprised.
I and not necessarily many, but at least some people I know donate (and, somewhat irregularly, will continue to do so) small-ish amounts to the foundation. [1]
You’d imagine that ought to be sufficient to cover running costs. However, it might actually not be enough with hardware prices these days? Not sure.
They’ve also recently (edit: “recently-ish”, it’s actually been a year!) introduced paid storage for backups, which I’d imagine comes with some amount of profit margin, too. [2]
50 million a year is about 50 cents per user as they are said to have 100M active users. So it's not that bad really. Nothing that would require a 10$/month subscription.
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