While I wish Musk wasn’t a Canadian citizen, I don’t think we should set a precedent for stripping citizenship from Canadians
Ugh, I was hopeful that this wasn’t true but unfortunately you are correct. Thanks for the link.
Oh yes, you are correct. “ReTruths” should have given it away 🤦♂️
What are you hosting and who are your users? Do you receive any legitimate traffic from AWS or other cloud provider IP addresses? There will always be edge cases like people hosting VPN exit nodes on a VPS etc, but if its a tiny portion of your legitimate traffic I would consider blocking all incoming traffic from cloud providers and then whitelisting any that make sense like search engine crawlers if necessary.
Kash Patel’s twitter handle is Kash_Patel.
Focus on the wisdom instead of the semantics
How did you meet? And if you went on a honeymoon, where did you go?
except genAI has proven no purpose
Generative AI has spawned an awful amount of AI slop and companies are forcing incomplete products on users. But don't judge the technology by shitty implementations. There are loads of use cases where when used correctly, generative AI brings value. For example, in document discovery in legal proceedings.
100% and like any tool, it can be used poorly resulting in AI bit rot, bugs, unmaintainable code, etc. But when used well, given appropriate context, by users that know what good solutions looks like, it can increase developer efficiency.
The author seems to think that OpenAI having an unsustainable business model means generative AI is a con. Generative AI doesn’t mean OpenAI 🤦♂️ There is a good chance that the VC funds invested in OpenAI will have evaporated in 5 years. But generative AI will exist in 5 years, it will be orders of magnitude more useful, and it will help solve many problems.
It is the best option for certain use cases. OpenAI, Anthropic, etc sell tokens, so they have a clear incentive to promote LLM reasoning as an everything solution. LLM read is normally an inefficient use of processor cycles for most use cases. However, because LLM reasoning is so flexible, even though it’s inefficient from a cycle perspective, it is still the best option in many cases because the current alternatives are even more inefficient (from a cycle or human time perspective).
Identifying typos in a project update is a task that LLMs can efficiently solve.