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The thing to be remember about those companies you listed, none of them are software companies like Anthropic is.

* Amazon is a logistics company * Walmart is a logistics and retail company * Apple is a hardware company (that uses software to set their hardware apart) * NVidia is a hardware company

The closest you get is Microsoft, at 281B in revenue. Some might argue Google/Alphabet, which is in the same area, but they sell Ads, not software.

Anthropic's expectations is that they will be able to fast-track their revenue generation to almost twice what Microsoft does today. In less than a few years.

I agree, not a huge difference here. They eyes and ... hat? on high are out of place so I'd argue that's the worst one, but it takes xhigh before we get legs and bike ordering correct.

It's less than OpenAI did for Astra, but that was my first encounter opening it and my first thought was that they decided they liked Astra's hero/scrolling animation. I'm pleased to see they didn't make the entire page that like OpenAI did, but I'm expecting to encounter this pattern more often on these announcements now.

"Oh, usually my bank just logs me in, that's strange. Let me just go grab my username and password and type it into this site that looks like my bank."

Same thing is going to happen with passkeys for non-technical users for exactly the same reason you stated. People will think the integration is busted and manually copy/paste the non-passkey credentials in. In that way, I would argue that passkey is not stronger protection against phishing attacks unless its the only way to login. It is, at best, a convenience for users.

Oh look, the answer is the same when I looked a couple months ago. Interesting

I've been seeing links to it for the past hour+, and I did catch it live when this post came up, but is now once again a 404 and this post is flagged. Several other outlets are reporting on its release. Clearly we're getting a new GPT today, the question is when are they going to commit to the announcement.

Ever since they made auto-mode default I swear claude has tuned to use python commands instead of the Edit Tool to frustrate the ~security conscience~ luddites into using auto-mode.

Absolutely. I was focused on the burden CVEs place on everyone downstream of teams that don't take a nuance view, but even when teams do look at all the CVEs reported in scans, the proliferation of CVEs just adds workload to determine if they are affected. Unless teams say they will not look at lows (or lower-than-lows if the category existed) then what amounts to busy-work just piles up.

> Every CVE thus has this huge cost tied to it. A cost that does not land on us and we don’t really see or feel it, but a cost on the ecosystem I believe we should not ignore.

I really appreciate this attitude towards this because it recognizes that there are a lot of security teams out there that don't take a nuance view of CVEs. For instance, one time we had a security team that required us to patch a vmware support package that was installed by default on ubuntu, but the CVE required being ran on vmware when we were running on EC2. Arguing with them was pointless because they were not interested in determining if the CVE applied to us, only that it needed fixed.

Lots of teams that are supposed to be in charge of security don't ask "does this CVE affect us", but simply shift the burden of patching downward and outward. In some cases, like in the case of easy to update and centrally deploy SaaS products, that burden is more annoying and frustrating than difficult. In some cases, like when you have complicated deploy or have customer-controlled updates, those mandates cause a huge burden on teams not producing the decision to patch every low CVE.

About 8 years ago, we used mturk for reading data out of public PDFs generated by a huge range of producers. I am not sure that LLMs would have been able to consistently extract this data until recently as a good number of these PDFs were scans, sometimes a scan of scan.

We had pretty good luck in the end, but getting there produced a somewhat large app on our side that would manage the whole process, including but not limited to asking for multiple responses, comparing them to each other, finding consensus, and determining which users would consistently produce bad responses and stop them from responding. We got to a confidence that about 85-95% of the data was correct, which was good enough for the company.

Through that process, I learned a couple things about managing mturk, primarily about how changes to the cost-per-task would change the process. Initially, we thought that price would be a quality knob, but quickly learned that price was a speed know. The higher the price the faster the tasks would be taken and completed. Quality did not change significantly as the price went up or down.

Overall, I still have fondness to mturk, but it was really bare-bones experience that needed a lot of work to get working effectively.

I've yet to understand why they call a 190 page PDF a "card". Calling something a card invokes a small, quick rundown of pertinent details, not every single possible detail.

Co-worker and I were speculating that a) They'd extend the preview period when OpenAI announced Sol (which they did, maybe not because of Sol though) and b) That once the preview period it was going to get included by default on plans in a couple weeks so they could gauge usage of it being included versus having to pay for it all the time. Noticed that Fable was included when I launched claude code this morning, so I'm guessing they have all the data they needed to make a decision.

My first instinct was Sol > Luna > Terra, since Sol is the farthest away, then Luna, and Terra is the closest. Size was not my first instinct. Or should Terra be the best model because its closest to people, then Luna because there have been people on it, then Sol be the worst because no human has been there?

Thanks. What you described is much more what I'd expect a data warehouse process to look like. Which is driving me mad because I don't understand why there are so many steps with so many tools.

I've not gotten a straight answer. I assume it is a pet project kind of situation, or trying to justify the data warehouse project as a whole, but I really don't know the real driver to do this.