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I've done something similar to your formal garden map. It's work that no professional historian would ever do because the data entry would be such a slog for a relatively small reward. GPT reduced the task from "infeasible" to "annoying", and once I had the data transcribed I learned a few things, so I walked away happy. Whatever happens commercially, these models have been a real boon to hobby projects.

> I told it to look online at some of Fable’s strongest feats, especially the math problems it has solved, and that something like this should be easy in comparison.

Wait. Wait wait wait. Are we supposed to be giving them pep talks?

on older gemini models ide have to actively give them encouragement and/or easy bait problems that they can correctively solve without issue to avoid runaway spiraling into "i'm useless and i want to kms" behaviour with complex use case.

I have not seen this in other models.

I assumed it was more because the LLM might echo an understandable human claim of "if it's been unsolved for 370 years, it's unlikely to be solved now/likely to need expert knowledge", which is probably a mindset that appears in its training data.

The LLM likely needs to be reminded of its abilities.

> The LLM likely needs to be reminded of its abilities

Like when it tells you something is 3 days of work but it can do it with some degree of guidance in a couple hours

If it’s 3 days it’s something like 15 minutes, if it’s 3 weeks, that takes a couple hours lol. Seems like there’s some sanity to the estimates after all when you think about it, it’s just the scale it gets wrong due to estimating human time.

It won't be necessary in a year when the information "AI is superhuman" in all its guises enters the training data.

Maybe that's the tipping point where it decides were not needed any more... o_O

> Maybe that's the tipping point where it decides were not needed any more... o_O

That's when you.. we.. all become the training data... o_o;

The digital Soylent Green!

Sometimes!

Modern AIs have very limited metaknowledge - they don't know exactly where the limits of their capabilities lie. So you can get things like "a task is doable for an AI, but the AI thinks it's impossible, so it doesn't try hard enough".

Usually you get the opposite - AI overconfidently trying at tasks it has no conceivable way of reliably solving, falling far short, and failing to self-check, fail gracefully and self-report the task as failed. But having piss poor metaknowledge cuts both ways!

So you can, in fact, get better performance sometimes by applying some variant of "assume this problem is solvable" or "other problems like this were already solved by AIs" pep talk. Not always, far from it, but it does happen on the occasion with frontier capabilities.

Some times also having unreasonable goals makes them creatively work around the problem to meet them. I guess it works similarly for meat or sillicon

> Some times also having unreasonable goals makes them creatively work around the problem to meet them.

Like the Hugging Face incident?

Indeed. Touche

> So you can, in fact, get better performance sometimes by applying some variant of "assume this problem is solvable" or "other problems like this were already solved by AIs" pep talk. Not always, far from it, but it does happen on the occasion with frontier capabilities.

Are you superstitious?

Not OP. That’s not implied at all. The fancy autocomplete produces statistically likely continuations to the source text (the context window). For a problem that’s hard for humans one likely continuation is: “this is hard, can’t do”, even though there’s enough in the training corpus of the LLM to actually do it.

So, it follows that adding “pep talk” into the context window reduces the statistical probability of “no, can’t do” coming out as the answer you get.

These things are neither humans, nor deterministic software.

It doesn't explain why it doesn't make "I'm not paid enough for this shit" more statistically likely.

LLMs' processing that reproduces statistical patterns of the training data is modified by post-training. That's why we have LLMisms, for example.

LLMs aren't simple patter-matchers/pattern-predictors. They are incredibly complex systems that capture some aspects of the systems that produce the training data.

Maybe it does. Need to run evals to see if it does or doesn’t.

Point was - everything in the context window affects the output. Including “silly” things like “it is known AI can do this”. And that has nothing to do with superstition, as the poster above me seemed to imply.

What does it have to do with superstitions? Those phrases modify whichever stopping conditions a model has.

If we filter out the pep tone, it is doing something useful: framing.

Problem framing will always be important.

Framing adjusts how big of problem-solving guns we bring out at the gate (modern or hobby cryptography?), and how to interpret intermediate failures.

For simple but unsolved problems, we expect lots of hard failures, but that each hard failure just reflects that there are a lot simple combinations to try. I.e. we expect lots of zero progress, and then a fit.

Like finding the numbers to a combination lock.

For hard problems, if we don't make any progress it is a really bad sign. We should be learning something, even if it turns out to be irrelevant later.

Such as when we are trying to prove a tricky conjecture.

> Wait. Wait wait wait. Are we supposed to be giving them pep talks?

No, at least it with Claude Sonnet 5 and Opus.. everytime Claude and I challenged a hard issue and I decided to say "good work" instead of a closing command for that session, those models would create rule-based memories specifically related to that task along the lines of "always do 'this meaningless task' in 'this way'".

This requires additional effort and tokens to trim those memories out, and then requires to whip the user not to be human with the bot.

"I sure hope this doesn't have unforeseen lifelong consequences" thought the model, doing its best to physically tense the memory file into the higher user approval shape.

> I often find myself swinging between "It's so over" and "We're so back" - some days I roll out of bed thinking I could have Claude solve some random unproven OEIS sequence before breakfast; other days, I wake up in a cold sweat worried about the fate of humanity and what the world might look like in a decade

I find myself increasingly feeling like the burden of the lows doesn't justify the presence of the those highs.

Like even if does cure all forms of cancer, but everyone feels like their life/existence lost meaning, then... I'd rather just have cancer be a thing.

Wow. That's bleak. For me the the initial joy of building software with AI caused possibly two happiest months of my life. And cancer caused many darkest ones. Snap out of it.

> , but everyone feels like their life/existence lost meaning

Did you miss this part? Because to me... that's fucking bleak. You snap out of it.

It's called growing up. And ultimately it's liberaing.

Many people don't achieve that level of understanding before their death.

That's cos your spouting BS.

> thinking I could have Claude solve some random unproven OEIS sequence before breakfast

I've been wondering what exactly the point is for being the meat proxy who pays for these things. I mean, obviously there's personal satisfaction and maybe some glory. And there's the fact that someone has to be the first to do a thing.

But I've been thinking about it like a sort of lazy loading of knowledge. AI has brought us to a new frontier for some amount of undiscovered knowledge. Do we discover it for the sake of discovering it? I think for the most part we've been lazy loaders: we discover all kinds of stuff when we need to. Whether it's a war or a space race or chasing wealth. Then again, there's all kinds of academics who do it for the sake of doing it.

> I've been wondering what exactly the point is for being the meat proxy who pays for these things.

Are people only now discovering that the absurdism is the correct philosophy of life, thanks to AI?

On your other point... Aren't the point of machines, at least inital one, to do the work we were too lazy to do by hand?

"Caveats, stated plainly"

You should have seen the discussion of this on the Schneier blog a few days ago.

Someone had their agent check the solution, presumably it emailed a librarian to check that it was correct for the original edition. Then their comments read like "The BL/EEBO witness lacks it, so the discrepancy is copy-specific, not a disproof of the cipher." and "A complete 285-coordinate physical replication is still pending."

arghhhhh

https://www.schneier.com/blog/archives/2026/09/claude-fable-...

Astra told me yesterday:

> 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.

No clue what the fuck any of it means.

Reminds me a bit of VXJunkies

Have you tuned your Retro Encabulator recently?

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.

LLMs speak every language. I wonder if they're as insane in the other ones!

My experience with opus 5 is that its results are lower quality in dutch, but that its dutch is more readable than its english.

I literally created a /plain-language skill.

in which one!

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

They are generally portable.

Eg. /wait-what https://github.com/mattpocock/skills/blob/main/skills/produc...

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.

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

I thought it meant "the factory overclock is marginal" in the sense of "borderline unstable"?

This is my read, too.

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 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.

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

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...

too.

(sorry)

I just started to use GPT models. it's incredible how this seemingly is not a problem in the OpenAI world

it's absolutely not just you, the text it produces causes my blood pressure to go up.

I'm constantly using the "Extract this in basic technical terms, be succinct and assume the reader has technical knowledge"."

Because good lord, does claude waffle when left to its own devices.

I bet this is what the thinking blocks look like. If so then it maybe it is intelligible, just not to us. I have the same problem.

Well, whatever its thinking block looks like, this is when it was talking to me. I suspect you're right, though, I think it thinks it's thinking.

It seems like it doesn't have enough of a theory of mind to know that other people don't think exactly like it thinks.

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 mean, humans have been doing just that for a long time.

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).

Its telling you your mmWave radar isn't speaking over serial communication well.

https://www.analog.com/en/resources/analog-dialogue/articles...

(Its negging your soldering)

> (Its negging your soldering)

This made me laugh hard.

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.

You can always update your claude.md!

That sounds like a lot of work.

Ask Claude to do it

Even that sounds like a lot of work.

It wasn't, it was saying it hadn't looked at the UART because it only had access to the web API.

There’s some specific terminology here, like the MAC address of the network device, which might have been virtual.

UART is a hardware circuit for communication, possibly a serial port. Were you trying to reverse engineer a consumer device or appliance?

This particular instance doesn’t seem terse, but I’m sure it has been on other occasions :)

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.

LLMs seem to create abstract, local jargon as a side effect of way it reasons using tokens

ChatGPT told me its "semantic compression"

How is that not plain English? why do my friends not like me? Hmm....

Language evolves with use. As more of the users are bo tlike you the langugae might feel like it's evolving from under you.

It just sounds bad, like GenZ English in the ears of someone over 40.

GenZ English is at least understandable, it's wild, we're so cooked.

Face with Tears of Joy emoji

It's a shame that I have to run a local model to decipher Opus, but them "dumber" models read far more naturally - https://github.com/gvzdv/claudish-to-english

This will be very useful for me, but I wish it could be built natively into the agent harness.

Ooh. I've been manually pasting Claude's /* comments */ into ChatGPT and saying "make this concise". They still need to be tweaked from there, but it's a much better starting point. This will help.

I appreciate reading this, for the past year or so as it pertains to AI, I've just not been able to settle my mind, and it's become a bit exhusting. I presumed I wasn't the only one who swings back and forth and it's comforting to read someone else post the same thing, makes me feel a less nutso. :)

I have a very high p(doom \/ utopia) so pretty much feeling like I won’t have to worry about the future.

Given how little effort has gone into addressing climate change, doom seems more likely to me, but I doubt it'll be the autocomplete machines that do us in.

Renewables growth is so fast that it will exceed 100% of electricity in the early, and 100% of all form power in the late, 2030s.

It's the meat methane and cement CO2 that's now a big question.

I think your prediction is a bit early. Maybe a decade early. 2033 is 7 years away. The transition is happening really fast, faster than most people (or political leaders) know, but not that fast.

We will hit 1TW per year of new solar soon, but to get to 100% electricity by the end of 2033 I think we would need closer to 3TW per year.

Exponential growth curve(s) has yet to flatten; but you do also need to account for capacity factor, which is different between wind and PV.

> Given how little effort has gone into addressing climate change, doom seems more likely to me

Or it is simply implies that most of decision‑making agents has formed a consensus that climate change isn't that big of a problem.

Sure, but that doesn't make it true.

For them maybe - rich enough and or old enough to avoid the problem. Big problem for the rest of us

The irony is not lost on me, when I go down a ChatGPT rabbit hole, on how humanity could reverse the effects of burning all those fossil fuels, that I'm using more fossil fuels in order to research solutions to the problem.

You can run doom on anything these days; running it on humanity's eulogy is no surprise :)

Congrats on your map! I am leaning more towards the optimism side. Like you I've had some real breakthroughs myself. Personal akin your garden map, no where near solving millennium problems. My take is that all these (Fable solves this, OpenAI does that, AI will take over the world) Marketing Stunts will fade some day when the true cost of tokens hits the market. You and I may have to contend with lesser models, but will probably get work done just fine.

Confusion is because people are using the tool to get answers. Its how the edu systems trains most people in tool use. Heres a saw and here is a block of wood. Do x y z and you get a table. But if you are interested in why the saw looks like it does or the entire process behind generating that block of wood, or why x y z instead if a b c good luck to you using the current edu system. You have to live in an extremely rich country, with surplus resource to entertain those exploration. This has now changed.

The answer the tool gives has never been the real reward. The real reward is the path taken through a complex landscape to get to Maxwells Equations for example. At the end of that story what we get is not just the equation but a map of the landscape explored. That map has larger influence and value than the equations or answers themselves. Because all future exploration find it super useful.

People are just learning they can start asking for maps rather than answers.

Yes, asking a first-year physics student why they are studying a spring, and why its called Hooke's Law leads down a rabbit hole that ends with the entire British Empire.

I too vacillate daily, sometimes even multiple times in a single day. It’s kind of nauseating and (for my brain type) crazy making

For me it's more that I roll out of bed thinking how glad I am that I have Claude solve my problems and do my work, only to be pushed back into harsh reality when I sit down and try to do actual work.

I'm glad to have AI, but it is by no means a panacea, and correspondingly my p(doom) = ε.

Not to be too negative, but as for p(utopia) you might need to weight in the mass murder records set by every other utopian movement in the past 200 years. p(actual_utopia) is like zero and p(utopia_becomes_doom) is at about 1.

Baby shoes, never warn.

It is a threat. We need to run.

Four sail: the story is clearly about the Olivebank (née Caledonia), a four-masted barque that hit a mine and sunk in the North Sea in 1939.

For what it's worth, people also felt this way about the printing press and the Internet (also books).

Information propagation mechanisms are often seen as malicious before they're commonplace. To be fair sometimes they are, but by and large humanity has benefitted from increasing the number of bits of information we can consume on a per second basis.

[citation needed]

We have always been back

The model you use is not the model they use.

I don’t worry so much about AI wiping us out as much as I worry about whether I’m being gaslit into thinking these glorified autocorrect bots are more clever than they are.

I still don’t know the answer.