Could be any number of things:

• A law firm might have offered to give legal advice for what to do in response to the cease and desist, but won’t agree to represent them in a lawsuit (at least at rates that Nitter can afford).
• A law firm might have given legal advice about one particular issue, but it didn’t comprehensively cover all the legal issues raised by the cease and desist (maybe they gave a legal opinion on the federal law issue but not the Texas law issue).
• A law firm may have agreed to represent them for a particular price but there was a misunderstanding about how to read the pricing sheet and Nitter can’t actually afford to retain that firm.
• Someone may have lied about being a licensed lawyer and law firm.
• A law firm may have given advice and then rescinded that advice after the second cease and desist letter.
• Law firm may have given advice based on erroneous understanding of the facts, and either backed out of representation or modified its advice upon learning the new facts.

Does it?

Reuters is reporting that Anthropic’s Investor Day event a few days ago, where they showed their audited Q3 financials to investors, caused institutional investors to shift their sentiment and that they are being pushed to lower their IPO valuation target down from $2 trillion to $1.5 trillion.

Coreweave is down almost 20% this week. Nebius is down more than 15%.

Investor sentiment is shakier than it was a month ago. And for hype-fueled valuations, investor sentiment is a two-way street.

What passkeys can’t overcome is people dismissing the popups asking if they want to switch to passkeys or create them.

I think it’d actually be pretty easy. Just ask:

Trust this device, with biometrics?

Most people will say yes on their Apple/Samsung/Google devices.

It’s been like 25 years since the security world basically moved on from assuming that users could be trusted to act securely. We gotta design processes and protocols that keep things secure even with people who have bad habits.

I can secure my email, but every email I send or receive touches at least one other mailbox I don’t control, of some other user. Same with text messages, social media posts, etc. The digital world we use has a billion overlapping people, some with escalated privileges as employees or admins or mods or whatever. We can’t make them perfect, so we should design systems that work well even with imperfect people.

I think you’re mixing up compromise of an account with compromise of the data associated with that account.

If a particular piece of data is accessible to people of a dozen accounts, compromise of any of those accounts potentially compromises the data. That’s a legitimate concern for people who want certain data secured, whether other accounts with access are also secure. In some cases, it may be best to have an authentication policy that provides a baseline level of protection even for the most clueless users, rather than a system where you’re able to secure your own account.

They don’t need to. It just works to log them in when they’re using one of their own trusted devices. Being able to unlock their phone gives them full access to the services (apps, websites) that are authenticated to that device.

TOTP tokens are often distributed as QR codes, where you can store in more than one place, store/copy/transfer in plaintext between people or places. Compromise of that token gives permanent access to all TOTP codes generated.

I’m not a fan of how most TOTP apps and implementations actually work right now.

I have a secure password manager, offline, nowhere shared, even the passwords are encrypted.

How can the service provider tell, though?

The point of passkeys is to create an authentication flow that is easier for users to keep secure. The question, then, becomes whether a service provider should make user authentication decisions based on the worst case scenario, best case scenario, or average case scenario. Passkeys are a reasonable way to make sure almost all of your users have secure login credentials, even if the best of the best users would have just as secure login credentials with traditional passphrases.

Apple mainstreamed tech tribalism

I assure you, from car brands to shoe brands to soda brands, brand tribalism by consumers has always been with us. On the tech side, we saw it with console wars of every generation, PC/accessory brand tribalism, even OS/software tribalism. TVs, radios, portable music players, AV equipment of all types, etc. When cell phones came on the scene, you had significant brand rivalries there, even before smartphones. (You see remnants in this when people get nostalgic about Nokias or Blackberries or Razrs).

plagiarism

outright theft

It frustrates me to see that people are suddenly protective of some kind of intellectual property right of people to control creations after they’ve been created.

I’m pretty skeptical of AI and have my doubts that it will be a net good, but I’m definitely not gonna suddenly be pro-copyright as part of that fight. Copyright has traditionally been used to entrench corporate interests, and strengthening those protections isn’t going to suddenly win a fight against some kind of AI dystopia.

Chip industry, both potato and RAM

That’s basically what AI is best at: incorporating lots and lots of data points (every single pixel on an image, many trillions of pixels on previous images used to train the model) to determine the best answer to a question with clear definitions of what is better or worse on a single-dimensional quality axis that most people would agree on. In this case, whether a de-noised photo looks good and accurately represents the image that could have been captured of the exact same scene using more light or a better sensor or whatever.

It’s generative AI where things fall apart. The analogy I always use is that these math techniques are great for inferring what is between pixels in a low res image, but starts going off the rails when it starts guessing what’s beyond the edges of the image. These AI algorithms are just math, and math with this many data points needs to start considering the limitations described by chaos theory (in the mathematical sense).

Once there’s a public expectation for profit from the two biggest AI labs

I don’t think that it’s necessarily the order things will go.

Anthropic and OpenAI have bills to pay, including obligations to pay a ton of money to partners/vendors in the coming years. They need cash to do that. And the cash can come from 3 main places: revenue from customers, loans from lenders (usually in the form of issuing bonds), and issuing new stock/equity to raise funds.

The IPOs are basically their last chance to raise funds by issuing new equity. Even if each of them raise $100 billion, that’s probably not enough to hit self sustaining profitability. As lenders start to get more skeptical, in a macroeconomic environment with high interest rates and high energy prices, they start to run into a very real possibility that they hit illiquidity issues, even if their core business model actually works by then (and I don’t think it will). There’s just not enough runway for them, even in the most optimistic long term projections.

I want a convertible, a laptop that folds all the way back and gives a tablet/stylus interface, but has stiff enough hinges to hold the screen like a laptop at any angle with a keyboard, and that closes and covers the screen like a laptop. They played around with some form factors like that 10 years ago but they never really took off. Plus they tended to be Windows only, with poor Linux support on the hardware.

Whatever the reason, it’s basically become true.

Annualized Revenue rate is a made up number

It is fundamentally a projection about the future using recent results, and transparent in its methodology. It’s shit methodology, but it’s not hiding anything. So I don’t know that I’d classify it as fraud.

Playing games with “revenue” is another problem, but because that’s misleading about the past, and making an active decision on whether to classify money that was never gonna end up in their pockets as revenue share to partners or cost of revenue. Companies sometimes engage in shenanigans where they give discounts but classify the discounts as marketing/sales expenses rather than foregone revenue. That kind of stuff strikes me as more squarely in the category of accounting shenanigans.

Financial Times smells blood in the water. They’ve been reporting on leaked OpenAI documents for a bit the last few weeks.

If the major financial outlets start reporting on the negative, there may be a sentiment shift that does actually destroy the companies propped up by hype rather than fundamentals.

The number they used for annualized revenue for these rapidly growing companies is taking the most recent month’s revenue and multiplying it by 12. It’s not a guarantee about the future, but it is misleading.

The extreme version is taking a good week’s revenue and multiplying it by 52, or even worse, multiplying a day by 365. Those are outright dishonest. But not accounting fraud, because it’s not accounting at all.

The tablet one eventually came true. Desktops and laptops aren’t anywhere near as popular for personal use by non-technical people.

In the U.S., copyright protections don’t extend to functionality, though. So copying even copyrighted code in a manner to replicate functionality is fair use when there’s no other way to accomplish the same thing.

For example, if you have a piece of equipment that checks the software on a cartridge for a string of text that says “Produced by or under license from Sega Enterprises Ltd.” before running that software, then it’s fair use to copy that exact text so that your software can run on that equipment. And it’s fair use to reverse engineer and decompile licensed cartridges to see what the bare minimum necessary to make it work.

One way to prove that you didn’t copy the software any more than is strictly necessary for functionality is to fully document the functionality, and then have a skilled programmer take the documentation and write new software from scratch, without ever having seen the original software whose function is being copied. That’s a “cleanroom implementation.” Compaq and other IBM clones built their own BIOS software to implement the exact same functionality of copyrighted IBM code, and created an entire industry of IBM compatible PCs that weren’t actually licensed from IBM. Similarly, Google moved Android off of Sun-licensed Java using a cleanroom implementation (when Oracle bought Sun and Google wanted to get away from Larry Ellison’s abusive pricing practices).

Ok, so if it’s permissible to reverse engineer the code to create documentation of how it works, and then have someone else take that documentation and implement the functionality using new code, how do AI/LLMs fit into this? Can it be said that it’s truly a “cleanroom” when the reverse engineering and decompilation functions are done by the same software that converts the decompiled code into documentation in human language, and then is the same software that converts the documentation into newly implemented code? Doesn’t quite hit the same way, and I’m not sure the courts would see it the same way.

All of this is a gray area, and people shouldn’t confidently predict what the courts will decide in specific nuanced examples. A lot will depend on the specific details, so there isn’t going to be much room for sweeping generalizations.