Hasn't this always been the case, what is different right now is that the tech enables to do this at scale, at much higher frequency that makes them more audacious, since no regular person can keep up with it. The over saturation of lies/fake news has lead to numbness and the hyper-normalisation. So, unless something directly is affecting us currently, we won't care
No. The modern Republicans want you to believe that because it’s an easy path to despair and inaction, which means they win, but the magnitude and degree have varied significantly in the past. Where we are now is something living Americans don’t have experience with unless they escaped somewhere like the Balkans in the 90s.
Corporations are people under the current SCOTUS interpretation. Which means they have the same rights under the US Constitution, so this should be struck down in no time in the courts. Another nothing burger likely to end up just manipulating the equity market and the derivative markets in the short term for large investors to capitalize.
For anyone looking for basic information of financial statements in business, the assumptions and estimates that go into it, I recommend Financial Intelligence by Joe Knight and Karen Berman. It helped me understand how much fuzziness happens in financial statements and how they can affect a business operation
The fundamental change we need is to make corporations pay taxes on revenue (top line) and not on profits (bottom line). It's simple, easy to track and in par with income tax of regular people.
Can someone explain what's technically better in the recent embedding models. Has there been a big change in their architecture or is it lighter on memory or can handle longer context because of improved training?
Care to explain more? I understand the prompt might not be used for training, but how about sanitizing the PII from tracking or logging or memory bugs in these serverless functions
My point is there are plenty of cases where you would send the same PII through a server-less function or internal API (IE PATCH /user/profile). The concerns about logging or bugs are the same in both instances. You could make a case that using a masking tool like this would make it easier to share full production logs, but there are plenty of other ways to secure logs that don't involve modifying the runtime behavior.
The more I look at it, the more I realise the reason for cognitive overload I feel when using LLMs for coding. Same prompt to same model for a pretty straight forward task produces such wildly different outputs. Now, imagine how wildly different the code outputs when trying to generate two different logical functions. The casings are different, commenting is different, no semantic continuity. Now maybe if I give detailed prompts and ask it to follow, it might follow, but from my experience prompt adherence is not so great as well. I am at the stage where I just use LLMs as auto correct, rather than using it for any generation.
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