xpe

To my eye, gaslighting is a serious accusation. Wikipedia's first line matches how I think of it: "Gaslighting is the manipulation of someone into questioning their perception of reality."

Did I miss something? I'm only looking at primary sources to start. Not Reddit. Not The Register. Official company communications.

Did Anthropic tell users i.e. "you are wrong, your experience is not worse."? If so, that would reach the bar of gaslighting, as I understand it (and I'm not alone). If you have a different understanding, please share what it is so I understand what you mean.

> All of this points to their priorities not being aligned with their users’.

Framing this as "aligned" or "not aligned" ignores the interesting reality in the middle. It is banal to say an organization isn't perfectly aligned with its customers.

I'm not disagreeing with the commenter's frustration. But I think it can help to try something out: take say the top three companies whose product you interact with on a regular basis. Take stock of (1) how fast that technology is moving; (2) how often things break from your POV; (3) how soon the company acknowledges it; (4) how long it takes for a fix. Then ask "if a friend of yours (competent and hard working) was working there, would I give the company more credit?"

My overall feel is that people underestimate the complexity of the systems at Anthropic and the chaos of the growth.

These kind of conversations are a sort of window into people's expectations and their ability to envision the possible explanations of what is happening at Anthropic.

> You can confirm that the people who say things are in a position to know.

What is the above commenter's sense of how well one can 'confirm' such a thing?

Looking at an HN account and its comment history provides some signal, but this doesn't satisfy me, given the incentives at play here. We're talking about OpenAI, a ~$800B company. Reputation matters a lot. The stakes are higher than e.g. "does so and on really work at e.g. Mozilla and know about the details of a messy Rust governance issue?" (to pick a deliberately lower stakes example).

When we decide what to let into our brains around OpenAI, Anthropic, etc, the bar needs to be higher than i.e. "does a HN account seem to be consistent with someone who works at OpenAI?". (I'm not sure if this is the above commenter's position or close to it?)

We need to be able to have stronger proofs, preferably ones with cryptography and credibility rooted in a legitimate trust model. In 2026, this is certainly possible technically, if a platform made this a priority. The barriers are largely social, cultural, and economic.

HN does not make real-world identity a priority. There might be some workarounds for posting information in one's profile, but practically speaking, I'm not seeing how this would work and what levels of identity it would bolster. Am I missing something?

If I start hand-waving I might dream up something like the following ... Maybe someone could stitch something together with a trusted content time-stamping server and prove they control an OpenAI email address and also provide that cryptographic evidence on their HN profile. It sounds ... practically unappealing at best. I haven't seen this done. Maybe I'm overlooking a good way. I'm all ears. We're going to need better solutions.

Relevant I think: https://www.anthropic.com/engineering/april-23-postmortem

I ask everyone to be a bit more careful about the "assume LLM/bot" thing. That hair-trigger is often counterproductive.

Anyhow, the Claude research took 36 minutes to run, so I put it to the side and didn't link it originally. I'm still thinking through it -- there is a lot to cover : https://gist.github.com/xpe/654af2731d40a145e1d0b8b694fe8fd3

I appreciate the "please", but this comes across as presumptive. First, you don't know the effort level I put in. Second, you haven't seen the end result. Third, why do you think I would "blindly paste" from an LLM? If you take a look at my profile or other comments, I hope that is clear.

I appreciate feedback in general, and I am glad when people care about making HN a nice place for discussion and community. Sometimes a well-meaning person goes a little too far, and I think it happened above. That's my charitable interpretation. It is also possible that in this age of AI, people are understandably pissed and sending that frustration out into the world. When that happens, just remember the people reading it matter too.

About me: I would not share something unless I think it has value to at least one other person on HN. I've done a lot of work about data and privacy in general (having worked at a differential privacy startup in the past), but I'm much newer to the idea of digging into ways of making telemetry gathering more transparent. I haven't found great resources on the Web about this yet, which is why I started doing the research. And I'm going to share it for others to read, criticize, build on top of, etc.

This was also my first impression. But it seems to me the changes are mostly about swapping what panels dock where (left or right) and maybe some additions/tweaks around the AI panels. On macOS these are still the same:

    ⌘B : toggle the left dock
    ⌘R : toggle the right dock

If you opt-in to the new layout, the panels that used to sit in the left dock are now in the right dock. I will give it a try even for classic coding. One can change what panels get docked where from the settings window.

I'll bet if you point out the issues where this is described and measured, you'll get some eyeballs.

Being a good regulator is about solving a nearly impossible satisficing problem. You have to follow the law and achieve achieve results with a limited budget and political constraints. Given the priorities of say the FTC or state AGs or the SEC, I don't think GitHub is even a blip on their radar. Of any of the regulators I would hazard to guess that maybe the California Privacy Protection Agency is the most likely to prioritize a look, but I still doubt it.

I know lots of idealists -- I went to a public policy school. And in some areas, I am one myself. We need them; they can push for their causes.

But if you ever find yourself working as a regulator, you'll find the world is complicated and messy. Regulators that overreach often make things worse for their very causes they support.

If you haven't yet, go find some regulators that have to take companies all the way to court and win. I have know some in certain fields. Learn from them. Some would probably really enjoy getting to talk to a disinterested third-party to learn the domain. There are even ways to get involved as a sort of citizen journalist if you want.

But these sort of blanket calls for "make an example of GitHub" are probably a waste of time. I think a broader view is needed here. Think about the causal chain of problems and find a link where you have leverage. Then focus your effort on that link.

I live in the DC area, where ignorance of how the government works leads to people walking away and not taking you seriously. When tech people put comparable effort into understanding the machinery of government that they do into technology, that is awesome. There are some amazing examples of this if you look around.

There are no excuses. Tech people readily accept that they have to work around the warts of their infrastructure. (We are often lucky because we get to rebuild so much software ourselves.) But we forget what it's like to work with systems that have to resist change because they are coordination points between multiple stakeholders. The conflict is by design!

Anyhow, we have no excuse to blame the warts in our governmental system. You either fix them or work around them or both.

The world is a big broken machine. Almost no individual person is to blame. You just have to understand where to turn the wrench.

Thinking out loud: what are the best practices to vet a tools' telemetry details? The devil is in the details.

A quick summary of my Claude-assisted research at the Gist below. Top of mind is some kind of trusted intermediary service with a vested interest in striking a definable middle ground that is good enough for both sides (users and product-builders)

Gist: WIP 31 minutes in still cookin'

/facepalm If we're going to debate norms and ethics, sending one liners into cyberspace won't get far. There are better ways. Invest in your conversational skills and listening skills, please. Otherwise you are a moth and HN is a streetlamp.

In general, this can feel like reading tea leaves. We might have some guesses, but it is tricky to get much confidence in them. Bottom line: it can be really hard to extract coherent signal from downvotes. A relatively low number given the overall topic interest can often mean noise or particular strong reactions. A relatively high number might serve as some useful feedback. This general ambiguity imo is a downside of the "up" or "down" systems that HN uses; it is part of the overall "design package". If you want more granularity, one step in the right direction is Lobsters. If you want many steps forward, requiring more community norms and background reading, you could try LessWrong. If you have other forums that you think do this well or at least differently, please let me know.

My takes on some of the comment above:

> A surprisingly high number of people are already being tricked into supporting things that clearly threaten their ability to survive in this economy, and even their ability to survive period.

I agree. Yes, I know that I'm far from a neutral judge of other people's core values, but I can say with high confidence that I've lived and worked among more US cultures than the typical tech demographics, by far. Born in Texas, worked in Colorado, San Diego, San Francisco, Berkeley, DC (the District itself), Northern Virginia. My industries range the for-profit, not-for-profit, tech-heavy. Other variations include idealist-heavy, good-execution, poor-execution, heavy on the dogma versus heavy on the pragmatism, pro-military, pro-patriotism, and the opposite.

I can say personally in an important sense, I fit nowhere. When I lived in Berkeley, I didn't fit the mold. When at SF startups, the culture felt shallow. I'm sort of "post culture" in a way. In DC, people seem rather blinkered. I see it, but I rarely feel like I'm truly part of any one culture. The only culture that I feel fits me is the "question everything -- but not like a conspiracy nut -- and then try to build something that matters". [1]

So, by objective standards -- counting places I've lived, different industries I've worked in, friends from different backgrounds, socioeconomic conditions, I think I'm easily in something like a weird 0.1% that actually deeply has experienced Trumpland and many variations of classic liberalism. If I didn't get out of my hometown, there is at least 20% chance I would now support Trump, if only due to tribalism and trusting of one's neighbors. So I know many Trump supporters.

All in all, I can't think of more than a handful that support him for reasons that I find even to serve their own long-term self-interest. There are special cases in a sense, like "techno-libertarians" or single-issue "pro-Israeli" voters or even smart people who feel like"f--k the democrats" who use these rationales to justify voting for Trump.

Saying I'm in the top 0.1% may sound presumptuous, I get it. But I don't like how some people water down their statements in the hope of sounding modest. See [2]. This is a kind of "false modesty" imo. I don't take any credit for my life circumstances. I was born into this position, and here I am. So I'm acting as a mirror to share this back. I've spent easily dozens of hours in one-on-one conversations on these topics, so I'm not coming at this with any particular dogma. This is my hundreds or thousands of hours of experience distilled. I really feel for Trump supporters on one level, even as their close mindedness and enable-ism wreck the economic and core principles of the US.

I welcome charitable and constructive disagreement on anything I've said. But more importantly, I ask for curious conversation.

[1]: Actually, there are some cultures I do fit into. Bayesian thinking being a key one. I admit I'm more than intellectually opposed to frequentist statistics -- it actively pisses me off in some ways. I'm pro-causality and pro-modeling. If people don't state their models, I don't take their predictions seriously. These are my strongest meta-opinions I think. People like Max Tegmark, Stuart Russell, Nate Soares, Yudkowsky -- these folks seem to think in ways that seem enlightened to me. The best "mainstream" thinkers I've seen probably are ... hmmm ... Ezra Klein, though he is more political than intellectual in my opinion.

[2]: "Against Modest Epistemology" by Eliezer Yudkowsky. November 14, 2017. https://www.lesswrong.com/posts/svoD5KLKHyAKEdwPo/against-mo...

If you are serious, I'm not quite sure how to infer what your definition of cringe is. I have no shame... I even checked urban dictionary.

Re: LW: yeah, you nailed the stereotype. I've probably said something similar myself (this is not a compliment). I'll bet many people on HN could find many interesting & useful discussions there. Here are some more takes with my commentary.

1. "LW has such a insider set of lingo and ideas and tenets". True imo, but these aren't hidden: they are easy to find.

2. The LessWrong "canon" is often weird and/or useless. I can confirm the feeling. Each new weird idea takes time to weigh. Take Newcomb's Paradox. Why the obsession with it? Until it clicks, it feels like you've walked into some kind of die-hard tabletop gaming session. So most normal people find ways to leave. Don't. Stay. What better do you have to? I'm seriously asking. If it matters, go do that instead. But if you have free time, you could do worse. How often do you really get your brain challenged?

3. The articles are too long. Yep, sometimes they are. One needs to like reading. A lot. So people with short attention spans might have trouble. This is my top criticism.

4. Intellectual depth and openness is off the charts. I put LW at the "best I've seen anywhere online, ever" level. Not the kind of permanent openness to anything. Openness to new ideas is a great starting posture. But the goal is to be able to get somewhere with them. As a particular idea loses plausibility, don't expect it to get much airtime. Bad ideas don't deserve as much consideration until/unless something changes to prompt a reconsideration.

> If we're going to have AI overlords, it'd be great if they spoke with proper grammar.

People like blurring the lines and lots of people want AIs to bang out keystrokes in an order that is roughly human-like, so here we are. (Personally, I would love if Claude said "honestly" or "real" or "landed" less. Or pretty much never! I've tried banning the words. I've tried providing a list of alternatives, and I'm enjoying nothing like Great Success.)

On the topic of "correct usage", until maybe ~3 years ago I had a pretty bog-standard understanding of what dictionaries do. They are authorities, right? Or at least experts in correct usage? That all changed when I read "Dictionary editors are historians of usage, not legislators of language." in "Disputing Definitions" by Yudkowsky : https://www.lesswrong.com/posts/7X2j8HAkWdmMoS8PE/disputing-...

See also: "Finding the forgotten creators of the Oxford English Dictionary" on 1A, WAMU 88.5, March 26, 2026:

https://wamu.org/story/26/03/26/finding-the-forgotten-creato...

https://the1a.org/segments/finding-the-forgotten-creators-of... (has transcript)

I get that metaphor. Let's try on another metaphor too, since the goal is insight, not judgment*... This reminds me of economic development policies of China and the USA ... both often pitch a developing country who needs something. Where "something" means i.e. getting out of poverty and/or having fewer people starve. The investor offer certain benefits with strings attached. These benefits are hard to pass up but involve major reforms. Painful reforms. On top of that, one country often says "if you don't work with us, you're stuck with [other country]" and vice versa. Try on this metaphor and see if it sheds some light on the impossible situation Dario is in.

* If you are trying to judge Dario, we're not having the same conversation. How many people on earth can grasp even ~1% of the situation he's in? How many have the intellectual tools and ability to reason through it? Maybe 0.1%, tops.

> You're just picking random problems with tech and blaming them on AI.

This comment is uncharitable, uncurious, and dismissive.

Reality is multifaceted. It is worth trying to synthesize and reconcile different views first. To do that, it really helps to ask some questions first. Genuine questions, not gotchas. Even better is to say e.g. "Ok, but I prefer this model instead [...]: how does it compare to yours?".

Does the above commenter find the expression of anger itself cringe-worthy? I hope not.

Categorically dismissing anger as "cringe" seems like a path to disconnecting from reality and morality.

> they just hear that this guy, whatever his intentions, is threatening their ability to survive in this economy.

Yep, Dario is straddling this sort of impossible line: he's the least-scary harbinger who is try to be one of the more transparent people to sound the alarm. But the funny thing about saying "don't shoot the messenger" is that it usually gets uttered well after the messenger has taken a bullet.

> You're overthinking the parent comment, I think.

Luckily, the phrase overthinking is on the way out. We really don't want any more Idiocracy Part II. In this day, we need all the thinking we can get. We often need (1) better thinking and (2) the ability to redirect our thinking towards other directions.

In my experience, 2026 is the year where almost all stigma about "talking AI" is out the window. I am nearly at the point where I say whatever I think needs to be said, even if I'm not sure if people will think I be crazy. So if Typical Q. Person asks me, I tell them whatever I think will fit into their brain at the time -- how AI works, why Dario is awkward, why superintelligence is no bueno, etc.

> The easiest way to grow AI resistance is to get Dario Amodei and Sam Altman on TV and let them talk.

Tell me more? I'm guessing you might say: neither connects with everyday people, they have misaligned incentives*, they (like most corporate leaders) don't speak directly, they have more power than almost any elected leader in the world, ... Did I miss anything?

My take: when it comes to character and goals and therefore predicting what they will do: please don't lump Amodei with Altman. In brief: Altman is polished, effective, and therefore rather unsettling. In short, Altman feels amoral. It feels like people follow him rather than his ideas. Amodei is different. He inspires by his character and ideals. Amodei is a well-meaning geek, and I sometimes marvel (in a good way) how he leads a top AI lab. His media chops are middling and awkward, but frankly, I'm ok with it. I get the sense he is communicating (more-or-less) as himself.

Let me know if anyone here has evidence to suggest any claim I'm making is off-base. I'm no oracle.

I could easily pile on more criticisms of both. Here's a few: to my eye, Dario doesn't go far enough with his concerns about AI futures, but I can't tell how much of this is his PR stance as head of A\ versus his core beliefs. Altman is a harder nut to crack: my first approximation of him is "brilliant, capable, and manipulative". As much as I worry about OpenAI and dislike Altman's power-grab, I probably grant that he's, like most people, fundamentally trying to do the right thing. I don't think he's quite as deranged as say Thiel. But I could be wrong. If I had that kind of money, intellect, and network, maybe I would also be using it aggressively and in ways that could come across as cunning. Maybe Altman and Thiel have good intentions and decent plans -- but the fact remains the concentration of power is corrupting, and they seem to have limited guardrails given their immense influence.

* Here's my claim, and I invite serious debate on it: Dario, more than any corporate leader, takes alignment seriously. He actually funds work on it. He knows how it works. He cares. He actually does some of the work, or at least used to. How many CEOs of the companies they run actually have the skills to DO the rank-and-file work? Even the most pessimistic people probably probably can grant this.

I strive to be decently Bayesian and embrace uncertainty. I'm sharing my probability estimates because it helps me to stop and think ("is this roughly what I think?" and "let spend a minute making sure before I say so"). But yeah, of course, they are my priors and fuzzy. Hopefully I can reflect I figure them out +/- 15% or so. But at least you can see how my takes compare with each other. And down the road I can see how I did.

Thanks for getting into some of the details ...

>> (1) One possibility is they are having capacity and/or infrastructure problems so the model performance is degraded.

> As far as I understand it, scaling issues would result in increased latency or requests being dropped, not model quality being lower.

Yes, many scaling issues would manifest in that way -- but not all. It seems plausible for Anthropic to have other ways to degrade model performance that don't show up in the latency or reliability metrics. I need to research more... (I'll try to think more on your other points later).

Hi. I read your message, and I considered it. I've also read some of your previous HN comments. Briefly, I'll just say I've argued at length against many of the claims you make (you certainly aren't alone in making them). I don't feel it would be useful to repeat these again here, but I'll reference a few, below, just to show that I do care about the subject matter and am happy to dig deeper ...

... but only with certain conversational norms. I say this because I predict we aren't (yet) matched up in a way such that we would have a conversation useful to us. The main reason (I guess) isn't about our particular viewpoints nor about i.e. "if we're both critical thinkers". We're both demonstrating that frame, at least in our language. Instead, I think it is about the way we engage and what we want to get out of a conversation. Just to pick one particular guide star, I strive to follow Rapoport's Rules [1]. FWIW, HN Guidelines are not all that different, so simply by commenting here, one is explicitly joining a sort of social contract that point in their direction already.

Anatol Rapoport or Daniel Dennett were not only brilliant in their areas of specialty but also in teaching us how to criticize constructively in general. I offer the link at [1] just in case you want to read them and give them a try, here. We can start the conversation over (if you want).

---

In response to your comments about consciousness, intelligence, etc, here are some examples of what I mean by intelligence and why:

- intelligence: https://news.ycombinator.com/item?id=43236444

- general intelligence: https://news.ycombinator.com/item?id=43223521

- pressure towards AGI: https://news.ycombinator.com/item?id=41707643

- intelligence as "what machines cannot do" / no physics-based constraints to surpass human intelligence: https://news.ycombinator.com/item?id=44974963

---

[1]: https://assets.edge.bigthink.com/uploads/attachment/file/151...

This is just "classic" (but avoidable) miscommunication. I don't even disagree with you on your point! I'm only saying you are not reading my point in the context it was offered.

For more background, please read Rapoport's Rules : https://themindcollection.com/rapoports-rules/

> Rapoport’s Rules, also known as Dennett’s Rules, is a list of four guidelines that detail how to interpret arguments charitably and criticise constructively. The concept was coined by philosopher Daniel C. Dennett in his book Intuition Pumps. Dennett acknowledged our proclivity to misinterpret and attack a counterpart’s argument instead of engaging meaningfully with what was actually said.

Words hint at concept space, which is messy and interconnected. I think a charitable reading can understand the difference between "powerful search, kind of like Google as of 2020, or Lexus-Nexus" and LLM-AI chatbot interfaces... I would hope. But I've been developing software since the 1980s so I can't speak for the newer generations who might not have a quadruple decade view. I've been in meetups in San Francisco around 2018, where people were excited to find multimodal reasoning in early days proto-language models. There have been qualitatively noticeable historical shifts. We don't have to agree on the exact labels used, but what LLM's enable is different enough from e.g. ElasticSearch of 2020 to call out.

I was attempting to give an example to say that AI-LLM technology is more than "fancy search" which to me sounds like "search engine". / I realize now that ChatGPT was released in late 2022, more than 3 years ago. Time flies.

> But it feels somewhat hype-y to say that goal posts are being moved "monthly"...

Here's what I mean. What you see if you kept a journal once a day and wrote down:

1. what impressed you about AI that day;

2. what did you do with it that day that you pretty much took for granted ("just SoTA")

Then compare today against 30 days ago. A lot changes! My point is that it is getting harder to impress us: our standard for what we expect seems to be changing significantly on a ~monthly basis. What does this rate of change where you "just expect something to work as table stakes" feel like to you? Certainly faster than annually, right? 6 months? 3? 2? 1?

For me, a lot of this isn't just the raw technology but also socialization of what the tools can do and the personal experience of doing it yourself.

I don't claim using AI is the same as doing it yourself. My point is that AI capabilities are much more extensive than "fancy search". By giving a metric and an example I hoped to make that point without getting into hair-splitting.

I don't mean writing a literature review. I mean reviewing the literature to find what I need. My point is that this was not practical with "fancy search" three years ago by which I mean Google-like search engines.

My example: I wanted to get a sense for the feasibility of doing a project that blends Gaussian Processes, active learning, and pairwise comparisons. So I want to dig into the literature to find out what is out there. This was around 5 minutes with Claude. In this case, I don't think I could have found what I wanted in 10 hours of searching and reading. This is the kind of thing that great LLMs unlock.

> Those tools seem mostly useful for a Google alternative, scaffolding tedious things, code reviewing, and acting as a fancy search.

Just to get a sense for the rate of change, imagine if you took a survey. Compare what people said about AI tools... 3 years ago, 2 years ago, 1 year ago, 6 months ago. Then think about what is plausible that people will be saying in 3 months, 6 months, 9 months ...

Moving the goalposts has always happened, but it is happening faster than I've ever seen it. Many people seem to redefine their expectations on a monthly basis now. Worse, they seem to be unaware they are doing it.

Fancy search? Ok, I'll bite. Compare today's "fancy search" to what we had ~3 years ago according to your choice of metric. Here's one: minutes spent relative to information found. Today, in ~5 minutes I can do a literature review that would have taken me easily 10+ hours five years ago. We don't need to argue phrasing when we can pick some prototypical tasks and compare them.

We're going to have different takes about where various AI technologies will be in these future timelines. It is much better to run to where the ball is likely to be, even if we have different ideas of where that is.

The human brain, at best, struggles to grasp even linear change. But linear change is not a good way to predict compounding technological change.

>>> ... but it seems like Anthropic is going for the Tinder/casino intermittent reinforcement strategy: optimized to keep you spending tokens instead of achieving results.

>> This part of the above comment strikes me as uncharitable and overconfident. And, to be blunt, presumptuous. To claim to know a company's strategy as an outsider is messy stuff.

> I said "it seems like".

Sorry. I take back the "presumptuous" part. But part of my concern remains: of all the things you chose to wrote, you only mentioned "the Tinder/casino intermittent reinforcement strategy". That phrase is going to draw eyeballs, and you got mine at least. As a reader, it conveys you think it is the most likely explanation. I'm trying to see if there is something there that I'm missing. How likely do you think is? Do you think it is more likely than the other three I mentioned? If so, it seems like your thinking hinges on this:

> I am claiming that there is no incentive for Anthropic to address this issue because of their business model (maximize the amount of tokens spent and price per token).

No incentive? Hardly. First, Anthropic is not a typical profit-maximizing entity, it a Public Benefit Corporation [1] [2]. Yes, profits matter still, but there are other factors to consider if we want to accurately predict their actions.

Second, even if profit maximization is the only incentive in play, profit-maximizing entities can plan across different time horizons. Like I mentioned in my above comment, it would be rather myopic to damage their reputation with a strategy that I summarize as a short-term customer-squeeze strategy.

Third, like many people here on HN, I've lived in the Bay Area, and I have first-degree connections that give me high confidence (P>80%) that key leaders at Anthropic have motivations that go much beyond mere profit maximization.

A\'s AI safety mission is a huge factor and not the PR veneer that pessimists tend to claim. Most people who know me would view me as somewhat pessimistic and anti-corporate and P(doomy). I say this to emphasize I'm not just casting stones at people for "being negative". IMO, failing to recognize and account for Anthropic's AI safety stance isn't "informed hard-hitting pessimism" so much as "limited awareness and/or poor analysis".

I'm not naive. That safety mission collides in a complicated way with FU money potential. Still, I'm confident (P>60%) that a significant number (>20%) of people at Anthropic have recently "cerebrated bad times" [3] i.e. cogitated futures where most humans die or lose control due to AI within ~10 to ~20 years. Being filthy rich doesn't matter much when dead or dehumanized.

[1]: https://law.justia.com/codes/delaware/title-8/chapter-1/subc...

[2]: https://time.com/6983420/anthropic-structure-openai-incentiv...

[3]: Weird Al: please make "Cerebration" for us.

> ... but it seems like Anthropic is going for the Tinder/casino intermittent reinforcement strategy: optimized to keep you spending tokens instead of achieving results.

This part of the above comment strikes me as uncharitable and overconfident. And, to be blunt, presumptuous. To claim to know a company's strategy as an outsider is messy stuff.

My prior: it is 10X to 20X more likely Anthropic has done something other than shift to a short-term squeeze their customers strategy (which I think is only around ~5%)

What do I mean by "something other"? (1) One possibility is they are having capacity and/or infrastructure problems so the model performance is degraded. (2) Another possibility is that they are not as tuned to to what customers want relative to what their engineers want. (3) It is also possible they have slowed down their models down due to safety concerns. To be more specific, they are erring on the side of caution (which would be consistent with their press releases about safety concerns of Mythos). Also, the above three possibilities are not mutually exclusive.

I don't expect us (readers here) to agree on the probabilities down to the ±5% level, but I would think a large chunk of informed and reasonable people can probably converge to something close to ±20%. At the very least, can we agree all of these factors are strong contenders: each covers maybe at least 10% to 30% of the probability space?

How short-sighted, dumb, or back-against-the-wall would Anthropic have to be to shift to a "let's make our new models intentionally _worse_ than our previous ones?" strategy? Think on this. I'm not necessarily "pro" Anthropic. They could lose standing with me over time, for sure. I'm willing to think it through. What would the world have to look like for this to be the case.

There are other factors that push back against claims of a "short-term greedy strategy" argument. Most importantly, they aren't stupid; they know customers care about quality. They are playing a longer game than that.

Yes, I understand that Opus 4.7 is not impressing people or worse. I feel similarly based on my "feels", but I also know I haven't run benchmarks nor have I used it very long.

I think most people viewed Opus 4.6 as a big step forward. People are somewhat conditioned to expect a newer model to be better, and Opus 4.7 doesn't match that expectation. I also know that I've been asking Claude to help me with Bayesian probabilistic modeling techniques that are well outside what I was doing a few weeks ago (detailed research and systems / software development), so it is just as likely that I'm pushing it outside its expertise.

Explore help in all the forms you can find. Feel free to contact me if you like. I'm happy to meet people and do a phone call or video call. I'm not hard to find with a little digging.

I've been through a fair amount of situations in my life, and I don't think have any illusions about places AI could go. I'm definitely not an optimist -- and I don't think being naively optimistic is something we want in everyone! -- but I'm still fighting. I think it takes a lot more strength to say "the world is looking pretty messed up and not getting better if I just sit here, so here we go..." Here are some things I suggest:

- Seek connection and community. This depends where you live, but get out there. Coffee shops, volunteering, just saying hello to people.

- Fill your brain with interesting thoughts. If you are feeling rough or depressed, you naturally may want to feel lifted up most of the time. Everybody needs a break, a laugh, some levity, or at least a change.

- But not everyone, at least not all the time, truly wants a fake sense of "everything is going to be fine". Sometimes we need to find people that are fully engaged in reality who say "yes, this is unacceptable and not getting better anytime soon but we're not giving up". And they find ways to still move forward.

- Back to the personal connections again!: It helps me to know people who have come to the US from other places in worse conditions. It helps to know that people can move forward even when many things are terribly broken. From this point of view, humanity really can be impressive. (To me, sometimes I'm most critical of people who get complacent when things seem good.)

- So, to me, and many others (Stoics especially), pessimism has a huge role to play. Things could go very badly. There are no guarantees. So get prepared -- getting prepared for tough times is a concrete activity that has meaning.

- More sunshine for you: You might benefit most by reading some really hard-hitting authors. Read about how f--ked up wars can be, how precarious the Cold War was. But somehow we made it through. There are no do-overs. Hopefully people realize the best time to do something was yesterday, but today is pretty good too!

- Train your mind. It feels good to invest in your own thinking. I recommend finding the most substantive and engaging material you can find about understanding how your brain works. For many people, this opens up a whole new set of tools.

- Find sources of inspiration. Personally, I'm a secular humanist. I've found great wisdom in the book Replacing Guilt by Nate Soares. You might too. https://replacingguilt.com/toc/ Some of my favorite sections are:

See the dark world : https://mindingourway.com/see-the-dark-world/

Detach the grim-o-meter : https://mindingourway.com/detach-the-grim-o-meter/

Dark, Not Colorless : https://mindingourway.com/dark-not-colorless/

> The last arc of posts has been about how to handle a dour universe. Become unable to despair, learn to see the darkness rather than flinching from it, learn to choose between bad and worse without suffering. Learn to live in a grim world without becoming grim yourself, learn to hear bad news without suffering, and stop needing to know your actions were acceptable. Come to terms with the fact you may lose, use the darkness as a source of fuel, and let go of dreams of total victory. These are the tools I use to tap into intrinsic motivation, in a precarious world where the problems are larger than I am.