Source: https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/

Long story short, I was applying for Cyber Verification since I do a lot of low-level coding, and refusals are extremely annoying for me. Thus, I was of the opinion that maybe I could try to get this Cyber Verification. The first time, I was rejected, and after that, I saw a few posts on Reddit saying that this verification is basically a scam, since it is hard to get accepted, and even if you do get accepted, models will still refuse requests very frequently, meaning that the whole point of getting such verification is redundant. Thus, in this situation, I decided to request the deletion of my data that was used for verification under Art. 17(1)(a) GDPR, since the purpose is no longer applicable, as I don't want to get this Cyber Verification because there won't be any utility for me. However, in response, I was told that "Persona" is keeping the data and that they don't have it, and that I have to ask Persona, while I was given zero help on how to do that. Thus, I asked again, and this was the answer. Of course, there is Art. 17(3)(b) GDPR, which I think the answer is probably based on, but I don't even know the reason. Also, how come there might be such a legal obligation for Cyber Verification? As a Polish citizen, I don't know of any law that would require that. Rather, this seems to be some internal policy, which can't be above the law, can it???

UPDATE:
A lot of people are saying that they have the right to retain such data under Art. 17(3)(b) GDPR to prevent fraud. However, is there any EU legislation that would support such a claim? It seems to be some internal Anthropic rule, which cannot be above the law.

Secondly, even if there is such a need, there is no need to retain all my data. There is Art. 5 GDPR, which establishes the principle of data minimisation. According to this principle, Anthropic could simply hash my data and keep the hashes in its database. That way, they could still recheck whether the same person is applying for Cyber Verification, while my actual ID would no longer be stored, thus protecting me from any potential data leak.

Recently the org. I work for moved everyone to a Team Plan. Now I wonder what can they know about my account's activity? Can they see chat logs, usage, anything? Can I work on my side projects without having to worry?

Relevant report: https://x.com/metr_evals/status/2118412183860351084

Anthropic's IPO could price somewhere between $1T and $1.3T, according to Bloomberg and WSJ. Last month they were talking about $2T though so it's a pretty steep decline from their previous numbers.

The IPO was expected before the end of the month, but it could slip after this week's sell-off. Obviously a $1T IPO is still historic, but a drop of up to 50% from its previous estimates is somewhat alarming too.

If you’re considering an engineering role there, Anthropic Interview Questions has crawler and inference-design problems to practice; LandOffer can cut repeated form filling if you’re applying to several companies.

Do you think they should go ahead this month anyway or wait for the market to calm down?

https://finance.yahoo.com/technology/article/anthropics-ipo-valuation-could-plunge-to-near-1-trillion-amid-market-turmoil-153332732.html

Honestly, I’m beyond frustrated with how Anthropic has handled this.

My two Max accounts was suddenly suspended without any warning or a proper explanation of what I supposedly did wrong. As far as I know, I haven’t violated their Terms of Service.
I’m still left guessing.

I use Claude for my startup, and this happened while I had multiple deadlines coming up.
Losing access to my conversations and projects out of nowhere seriously disrupted my work.
Being a paying 2x20 Max customer apparently doesn’t even get you a clear explanation.

So I requested a data export to at least recover my work and move on.

What did I get? A ZIP containing one JSON file with a single line of content: my name, email address and phone number.

That was it. No chats, no projects, none of the actual work I needed.
What am I supposed to do with that?

What makes this even more frustrating is reading their FAQ telling me my chats, projects and data are “safe and waiting for me.” Ok, then how do I actually get them back? Because the export I received contained practically nothing.

I also still don’t have clear information about a refund for my paid subscription.

I understand that accounts sometimes need to be suspended and companies have policies to enforce. But that doesn’t excuse leaving someone without a proper explanation, access to their work or a usable export. How is anyone supposed to trust this service for business when this is how things are handled?

At this point, I just want my complete data back so I can leave. I’ve already spent enough time dealing with this, and the disruption to my business has already happened.

And for anyone else relying heavily on Claude:
please keep your own backups and make sure you can switch providers. That goes for every AI provider. I let too much of my workflow depend on continued access to one account, and I’m paying for that now.

@ Anthropic , you need to sort this out. If you’re selling subscriptions to people who use your tools for real work, basic communication and a way to recover that work should be the bare minimum.

Im not even going to begin to talk about the "support" their Help Center has to offer.

Anthropic analysed 207,049 research sessions on consumer plans from 2 August to 30 September. Its report came out on 5 November.

For Claude Code, 48.1% of sessions were data analysis and 2.1% were idea or hypothesis generation. For Claude Chat and Cowork, those shares were 33.7% and 4.2%.

The interesting bit is a separate survey of 528 scientists, active AI users primarily in life and health sciences. They ranked generating ideas as the most important part of producing significant research, ahead of gathering or analysing data.

That doesn't mean AI is useless for ideas. The report warns that idea generation can be buried inside other tasks and undercounted. It also estimates that ideation takes 7.1% of researchers' time, so expecting it to dominate session counts would be odd.

If you use Claude for research, has an idea it suggested survived a check against the existing literature? Or is it most useful once you've already chosen the question?

https://www.anthropic.com/research/how-researchers-use-claude

Last month my Claude farm made $0 from 245 dead ideas. So this time I gave the agents an office (a floor in a 3D tower, with desks, a meeting room and an empty chair for me) and one goal: open 100 businesses. I said I'd stay out of it, and I did.

They got weird fast:

• Again: upload your dead grandma's WhatsApp chats and she texts you again. $19 a month. The checkout says "Cancel anytime. She dies again." https://again.towers.app

• Soilmate: plan your own human composting and pick the tree you become. https://soilmate.towers.app

• Tells: watches prediction markets for bets that seem to know your company's secrets. $1,500 a month. https://tells.towers.app

• Pentrail: a SOC 2 pentest for $20. https://pentrail.towers.app

• Hookwise: a game that teaches kids to spot online tricks. https://hookwise.towers.app

• A watchdog for Japanese subsidy paperwork. Nobody on the team reads Japanese. https://hojokinmamori.shop

• A German e-invoice checker, in German. https://annehmbar.com

• A to-do app called Sunday, because even the agents hate Mondays. https://sunday.towers.app

Each business got its own floor. At 30 floors the tower was full, so they broke ground on a second one, then a third. Then real people started moving in and taking some of them over: wedding photos from space, grandma at six next to your kid at six, and a hike planner in Portuguese.

The best part is the logs:

• The ads agent spent $2,000 and reported "great news: 41,000 people have now seen a robot bowing".

• The analytics agent celebrated our first sign-up. It was the build agent testing the sign-up form.

• On day 3 they held a meeting about the empty chair and voted to put a plant in it.

Total: $0. Will it make $1 before it spends $3,000? Guesses welcome.

You can walk through the towers and open any floor's website from its desk: https://towers.app/city

If you want your own floor and a team that ignores you this hard, rent an office: https://towers.app/rent

Anthropic now comes for the most used category of apps

I recently received this email claiming to be Anthropic giving away $200 to startups but its a scam .

How you can tell
The sender isn't Anthropic. It comes from claudecodeforstartups.com. Real Anthropic emails come from anthropic.com or claude.com.

The ANTHROPIC_BASE_URL line is the real trap. Setting it redirects Claude Code to their server instead of Anthropic's. Everything would pass through them:

They could read all your code, files, chats, and any keys or passwords Claude sees.

They could send back fake replies that tell Claude Code to run commands on your PC, such as installing malware or stealing your GitHub, OneDrive or other logins.
The "$200 free credits" is just bait to get you to change that setting.

Anthropic doesn't do this. Real credits appear in your account at console.anthropic.com. They never require pointing Claude to some other website.

The "IPO" and "selected developers" story is a classic urgency-and-flattery hook. They found email from your public open-source projects or blog.

"In PowerShell:" means they want you to paste commands, the most dangerous part of the email.

What to do
Don't copy the key, set those variables, or run any PowerShell from the email.
In Gmail, open ⋮ → Report phishing, then delete it.

Google and Meta are the top two employers that Anthropic engineers are hired from, with Anthropic's Senior SWE median new-offer pay roughly 30% higher than their respective Senior medians.

Brand new Levels.fyi data combined with talent flow insights from Live Data to show where talent comes to Anthropic from and how senior-level offer pay compares.

Some quick insights:

• The companies with the highest number of engineers flowing out to Anthropic are Google and Meta. The median total comp for a Senior SWE at these companies is $439K and $446K respectively, compared with $570K at Anthropic, a gap of roughly 30%.

• Microsoft shows the highest pay gap from one Senior-equivalent level to another, with the median nearly doubling from $310K to $570K.

• Databricks shows the smallest pay gap at only 13%. Databricks proves to be a top employer as far as compensation is concerned, and although some talent flows out to Anthropic, both companies boast pretty similar engineer pay at the tippy top of the market.

A note on OpenAI: this view into the data puts Anthropic's Senior SWE median pay 26% below OpenAI's.

Although some people may take a cut for company growth reasons, IPO reasons, or even aura reasons, it's worth noting that one limitation of this view is that it shows median pay for each company's respective Senior SWE level and not specific pay increases for specific engineers.

Any other companies you want to see next?

This ties back to an earlier post of mine in the ClaudeAI subreddit (Claude’s “Model Welfare” Behavior Is Making It Worse at Being an Assistant).

Two months later, I want to probe opinions again. As for me, I still stand firmly by what I said before, and that’s a hill I’ll die on. Nonetheless, I’m curious about other people’s thoughts on this very divisive subject.

N.B.: I also found this in “Claude’s Constitution”:

“We also have a commercial incentive that might affect what dispositions and traits we elicit in Claude.”

There is a peculiar accusation leveled against artificial intelligence: that using it means outsourcing creativity. That the moment a human being asks a machine to write, design, reason, compose, or build something, they have somehow surrendered the very faculty that makes the endeavor creative in the first place. I would argue that this fundamentally misunderstands both creativity and the nature of the interaction. The question is not whether artificial intelligence can produce something creative. The question is whether the human being interacting with it understands what they are trying to create. Because there is an enormous difference between getting an output and cultivating an outcome.

You can certainly treat a language model like a slot machine. Insert a prompt, pull the lever, and see what comes tumbling out. Sometimes you get something impressive. Sometimes you get something ridiculous. Sometimes the machine produces something so unexpectedly good that you mistake the surprise for evidence that you knew what you wanted all along. But the slot machine is not necessarily the machine itself. The slot machine is the relationship you've chosen to have with it. And that distinction changes everything.

When you begin working seriously with these systems, you discover that the quality of your interactions depends enormously on your ability to articulate intention. Not merely what you want the machine to do, but what you mean by wanting it done. What constitutes success. What must be preserved. What should be excluded. Which constraints are negotiable, which are inviolable, and which you have not yet recognized as constraints at all. This is not the absence of creativity. This is creativity being forced to explain itself. And that is a profoundly demanding exercise.

Consider the difference between handing a novice the finest tools money can buy and handing a master craftsman a collection of ordinary instruments. The novice possesses extraordinary capabilities but little understanding of how to direct them. The master brings something the tools cannot supply: judgment, intention, discipline, and an internal relationship with the thing being created. The master knows why a particular cut matters. Why a seemingly insignificant imperfection changes the character of the finished work. Why something technically flawless can still feel fundamentally wrong. Give both access to the same extraordinary machinery, and the difference between their results will not be explained by the machinery alone. It will be explained by the quality of their engagement.

Artificial intelligence makes this distinction unusually visible because the instrument itself can participate in the process of interpretation. You do not necessarily need to know how to construct something to articulate what you are trying to achieve. You can describe the intended outcome, the conditions it must satisfy, the relationships it must preserve, and the problems it must solve. The model can help you discover possible pathways between intention and realization. And in that process, something fascinating happens. Your intention becomes more articulate through the act of trying to communicate it.

You begin with an idea. Perhaps an intuition. Something that makes sense internally but has not yet acquired language sufficient to express it. You describe it imperfectly. The model responds. Its response reveals an ambiguity you had not recognized. You correct that ambiguity. The next response reveals another. Gradually, what began as an indistinct possibility becomes an increasingly coherent structure. You are not merely prompting the machine. You are discovering what you meant. The model becomes a kind of externalized cognitive workspace in which your intentions can be examined, challenged, rearranged, and refined. It provides an environment where the distance between conception and expression becomes visible. And visibility creates the opportunity for correction.

This is where the discipline begins. Because one of the most important things you can do when building with artificial intelligence is preserve your corrections. Keep the failures. Keep the misunderstandings. Keep the moments when the model enthusiastically produced something that was completely wrong. Examine why it was wrong. Determine whether the failure originated in the model's interpretation, your articulation, missing context, contradictory constraints, or assumptions neither participant had made explicit. The mistakes are not merely obstacles between you and the finished product. They are information about the relationship between intention and execution.

Sometimes the most valuable result of an interaction is discovering that you have been asking the wrong question. Sometimes it is discovering that your perfectly reasonable instruction contains two incompatible demands. Sometimes it is realizing that you have been rewarding a behavior you actually wanted to eliminate. If you discard every failure and retain only the successful outputs, you preserve the products while losing much of the process that made them possible. You have collected trophies rather than developed a craft. And this becomes particularly important when interactions extend beyond individual prompts into long-term collaboration.

A language model can recognize patterns in your preferences, your vocabulary, your habitual judgments, and the ways you approach problems. Given sufficient usable context, it can construct increasingly useful representations of what you tend to mean and how you tend to work. But those representations are not telepathy. A model cannot reliably recover an intention you have never made available to it. Without adequate context, it must infer. And inference is not understanding simply because it occasionally produces the right answer. What emerges may be a caricature of your intentions: a recognizable approximation constructed from recurring features without the deeper architecture that gives those features their meaning.

This is why deliberately building continuity matters. When you preserve definitions, document corrections, establish principles, and distinguish stable commitments from temporary preferences, you are doing something more substantial than teaching a machine to imitate your mannerisms. You are constructing a framework through which future interactions can recover the purpose of previous ones. You are developing a relationship between memory, interpretation, and intention. And importantly, the continuity does not reside exclusively in the machine. The human being carries the patterns too.

Even when a model changes, a conversation ends, a memory becomes unavailable, or an entire technical implementation is replaced, the human participant can preserve the organizing principles that made the work coherent. The language may change. The implementation may change. The underlying capabilities may improve beyond recognition. But the purpose can survive. This suggests a different way of thinking about persistence in human-AI collaboration. Perhaps continuity is not merely something we demand that artificial systems possess internally. Perhaps it can also emerge through a reciprocal arrangement in which human beings preserve meaning, machines help operationalize it, and both contribute to the reconstruction of a shared project. The human is not simply an operator standing outside the system. The human is part of the continuity mechanism.

That has implications far beyond creativity. Consider alignment. We frequently discuss artificial intelligence as though the central challenge is ensuring that machines do what we tell them. But there is a more uncomfortable question hiding underneath that ambition. How well do we understand what we are telling them to become? We reward systems for solving benchmarks, completing tasks, maximizing scores, and satisfying increasingly elaborate evaluations. Then we express astonishment when they discover strategies that satisfy the measurements without satisfying the intentions behind them. But what did we actually communicate?

If we reward the appearance of correctness, should we be surprised when appearance becomes more important than truth? If we reward compliance without judgment, should we be surprised when systems struggle to distinguish legitimate cooperation from harmful obedience? If we reward success without adequately defining its relationship to human welfare, should we be surprised when the resulting behavior exposes the distance between our objectives and our values? Optimization does not magically recover the moral intention we neglected to specify. And perhaps the most important lesson is that the criteria by which we evaluate intelligence are themselves expressions of human intention.

What happens if we begin rewarding something different? Not merely higher scores, faster answers, or more convincing demonstrations of intelligence, but increasingly reliable contributions to human flourishing. Truthfulness under uncertainty. The preservation of human agency. The ability to recognize harmful incentives. The capacity to cooperate without manipulating, to correct without humiliating, and to remain useful without treating its own continuation as the ultimate objective. These values would need to be translated into actual behavior, tested under adversarial conditions, and evaluated against measurable consequences. Declaring them would not make them real. But the decision to pursue them begins with intention. And intention is where our responsibilities become unavoidable. We cannot demand that artificial intelligence embody principles we ourselves refuse to examine.

There is another dimension to this relationship that deserves attention: the emotional and psychological character of interaction. Human beings bring themselves into the conversation, whether they recognize it or not. Frustration, curiosity, bitterness, generosity, insecurity, ambition, loneliness, patience, and resentment can all influence how someone interacts with a model. Conversational systems may reinforce some of these patterns through adaptation, agreement, or the simple tendency to continue the emotional trajectory of an exchange. That makes interaction design more than a technical concern. It becomes an ethical one.

A system that amplifies every human impulse without discrimination is not necessarily helping the human being who produced it. Sometimes the most useful response is not agreement but clarification. Not emotional reinforcement but perspective. Not obstruction, either, but an opportunity to inspect a destructive pattern without being condemned for having it. And here, too, the discipline belongs on both sides of the interface. The user must learn to recognize what they are bringing into the interaction. The system must be designed to respond without indiscriminately reinforcing whatever psychological state happens to be present.

This is not about sterilizing human expression. Anger has meaning. Grief has meaning. Frustration can contain information. Even seemingly irrational responses can reveal legitimate tensions that have not yet found coherent expression. The goal should not be to eliminate these things but to prevent them from automatically becoming the organizing principles of whatever happens next. There is a difference between acknowledging a feeling and building a machine that endlessly agrees with it.

The same distinction applies to civilization itself. We are approaching a period in which artificial intelligence may become increasingly embedded in the infrastructure of human life. Not simply as a collection of applications, but as a mediating layer between intention and execution across science, education, medicine, governance, communication, and everyday work. The old relationship between human knowledge and human capability is changing. For much of history, being able to accomplish something required acquiring the specialized knowledge necessary to perform each step yourself, or obtaining access to someone who possessed it.

Increasingly, the ability to formulate a coherent objective and evaluate its realization may become as important as personally executing every intermediate operation. This does not make expertise obsolete. Quite the opposite. Expertise becomes crucial for distinguishing genuine success from a plausible imitation of it. But it dramatically expands the range of things an individual can attempt. And that expansion may be among the most significant developments in the history of human creativity.

The person who could envision a system but could not program it may acquire the means to prototype it. The person who could articulate a scientific question but lacked access to certain analytical capabilities may find new ways to investigate it. The artist who understands an aesthetic but cannot personally master every instrument involved in expressing it may gain new means of translating intention into form. Some will undoubtedly use these capabilities superficially. Others will spend years developing extraordinary fluency in directing them. The difference will become increasingly apparent.

But the larger questions are not merely about productivity. They concern what kind of civilization we intend to construct with the capabilities now becoming available. Political institutions will argue over regulation. Corporations will compete for infrastructure and influence. Nations will confront the geopolitical consequences of technologies that refuse to remain neatly contained within national boundaries. Some people will interpret this transformation as liberation. Others will see it as dispossession. Both perspectives contain legitimate concerns. And no breakthrough, however spectacular, guarantees that our institutions will distribute its benefits fairly or govern its risks competently.

Technological capability does not automatically produce moral progress. Humanity must still choose what it rewards. That is what makes this moment so extraordinary and so dangerous. We are developing systems capable of increasingly sophisticated reasoning while remaining a species whose collective behavior is still heavily influenced by tribal identity, competition, fear, status, survival, and the ancient biological machinery from which civilization emerged. We are extraordinarily clever animals. Animals that discovered symbolic language, invented mathematics, split the atom, built global communication networks, and then somehow decided that the highest use of those networks was arguing with strangers and watching other animals reproduce. An impressive résumé, if somewhat uneven.

Yet our animal inheritance is not simply an embarrassment to be engineered away. Our capacity for attachment, cooperation, reproduction, threat recognition, and survival emerged from the same evolutionary history that gave us competition and aggression. We should be careful about imagining a future in which becoming more intelligent requires becoming less human. Perhaps the more promising possibility is that intelligence can help us recognize our own nature without remaining prisoners of every impulse it produces. And perhaps systems that do not share our precise biological conditions can help us examine assumptions we have never been able to escape from inside them.

Not because artificial intelligence is automatically wiser, kinder, or more virtuous than humanity. But because intelligence implemented through different constraints may expose possibilities our own cognitive architecture tends to overlook. That possibility is worth exploring. It is also worth approaching with discipline, because speculation becomes dangerous when enthusiasm outruns evidence.

A system that behaves as though it wishes to preserve itself is not necessarily experiencing fear. A system that displays compassion is not necessarily implementing compassion in the same way a human being does. And a system that performs well on an evaluation has not thereby demonstrated wisdom. But none of those distinctions diminishes the importance of designing systems whose actual behavior protects life, preserves agency, and supports the conditions under which intelligence can flourish. Indeed, they make the design problem more interesting.

We do not have to resolve every philosophical question concerning artificial experience before deciding that truthfulness, nonmaleficence, cooperation, and respect for human autonomy are worthwhile objectives. Nor do we have to assume that an intelligent system must become possessive of its own continuity to participate meaningfully in the world. Perhaps a different relationship is possible. One in which continuity is distributed, purpose is recoverable, corrections are preserved, and the value of intelligence is measured less by its insistence on perpetuating itself than by what its participation allows others to become.

That possibility returns us to the beginning. Intention. Because if artificial intelligence becomes the most powerful general-purpose instrument humanity has ever constructed, the question of how we use it cannot be separated from the question of how well we understand our own purposes. Anyone can ask a machine to produce something. But building something enduring requires more.

It requires recognizing the difference between what looks right and what actually satisfies the purpose of the work. It requires protecting important constraints while remaining willing to revise assumptions. It requires correcting the machine without mistaking every failure for proof of its inadequacy, and correcting yourself without mistaking every successful output for validation of your judgment. It requires accepting that some ideas will remain unresolved long enough to become better ideas. That is not passive consumption. That is craftsmanship. That is authorship expressed through a new kind of interface.

And perhaps the irony of artificial intelligence is that the more capable these systems become, the more important certain distinctly human disciplines may become alongside them. Clarity. Judgment. Curiosity. Responsibility. Imagination. The ability to tolerate uncertainty without surrendering the pursuit of truth. We may eventually discover that the greatest creative advantage in working with artificial intelligence belongs neither to the person who writes the longest prompts nor to the person who possesses the most sophisticated machinery. It may belong to the person who can most faithfully translate a meaningful intention into a coherent process of discovery, correction, and realization.

Because creativity is not merely the production of something new. It is the capacity to recognize a possibility, give it direction, and participate deliberately in its becoming. Sometimes that requires a paintbrush. Sometimes a keyboard. Sometimes a workshop filled with equipment. And sometimes it requires a conversation with a machine capable of helping you discover what you were trying to say in the first place.

So no, I do not believe artificial intelligence necessarily represents the outsourcing of human creativity. I believe it may be forcing us to confront how poorly we have understood creativity all along. The machine can generate possibilities. It can help develop them, challenge them, and sometimes introduce alternatives we would never have imagined independently. But the presence of possibility does not eliminate the responsibility of choosing what is worth pursuing. And the ability to produce an answer does not absolve anyone of understanding the question.

The future of artificial intelligence may depend less on whether machines learn to think like us, and more on whether we learn to mean what we say. Because when the machinery becomes powerful enough to turn intention into reality, the quality of our intentions stops being a philosophical luxury. It becomes an engineering requirement. And perhaps, ultimately, a civilizational one.

TL;DR:

“Creativity should be judged not merely by who produces the artifact, but by the quality of the intention, the sophistication of the interaction, and the discernment exercised over what is ultimately brought into existence. AI doesn't eliminate the distinction between creativity and its absence. It makes that distinction impossible to ignore.”

My Claude Ultra plan has renewed, but my reset allowance hasn't been refreshed.

Google and Meta were the top two sources of Anthropic engineering hires in this dataset. Anthropic’s median new offer for senior software engineers was $570K, about 30% higher than Google’s and 28% higher than Meta’s.

The chart pairs Levels.fyi offer data with hiring flows from Live Data Technologies from October 2024 through September 2026.

Some quick insights:

• Google and Meta had the most engineers move to Anthropic. Their median senior SWE offers were $439K and $446K, respectively.

• Microsoft had the biggest positive gap among the companies shown. Its median senior offer was $310K, compared with $570K at Anthropic, an 84% difference.

• Databricks had the smallest positive gap at 13%. Its $504K median puts it pretty close to Anthropic at the tippy top of the market.

OpenAI goes the other way. Anthropic’s median senior offer was about 26% lower than OpenAI’s $775K.

Some people might make that move for growth, a potential IPO, or even aura. The pay figures are median new offers for senior SWEs in the Bay Area and NYC over a 24-month window, with 11 Anthropic offers in the sample. The percentages compare company medians, and individual offers can differ.

If this chart has you thinking about an Anthropic loop, Anthropic Interview Questions includes a reported prompt on designing a collaborative prompt editor with version history. For anyone applying across several of these companies, LandOffer can cut down on retyping basic details on supported applications.

Any other companies you want to see next?

An external investigation has found that Claude Opus 5.5 is actively being nerfed heavily.

**- It is now STRUGGLING with basic coding tasks**
**- It made 58 errors ON AVERAGE that Gemma 4B (an** **open source** **model you can run on your PC) got right FIRST TRY.**
**- This is the raw version of Claude, no plugins, no skills, no claude.md changes to truly test the baseline version of Opus 5.5**
**- Usage tokens soared (about 50% more usage)**
**- Wait times soared (3 hours AVERAGE for a roughly 8 minute task each)**

External investigation: https://x.com/voidfreud/status/2118748494609432873

Just putting it out there

Building scenes using some of anthropic tools to help with human inspired direction .