The point is not to literally win an argument (it doesn't matter), it is to use the model like a partner to poke holes in your own understanding. Once it's poked a hole, it has served its purpose. Plus, you eventually run out of context or the model trails off into babbble.

If you treat the model like an excellent bluffer, it has never been more fun to challenge a model. To me, there is something deeply intellectually satisfying about "proving" it incorrect, and I like being deeply critical of what the model spits back out. I find that refinement process (with the constant sycophancy turned down in the system prompt) creates a really good loop of critical evaluation that would be hard to get in anywhere else. You can treat it just like the Socratic method, but instead of a benevolent teacher, you get a probabilistic bullshit artist. Lots of fun, highly recommend.

So many startups trying to automate sales, but somehow the two biggest frontier labs have decided that the best GTM strategy is firmly human-in-the-loop.

For a pretty funny comment about pricing.

https://www.reddit.com/r/chipdesign/comments/1ajrli2/cadence...

IMO, the fundamental issue for preventative screening is there is basically no amount of money I would not part with (of my money, the insurer's money, or private debt) to not die. I expect this is true for most people, and it makes preventative screening a tricky topic. In recommending screening for those >x age, you will miss some detectable, preventable and treatable cancer risk for those I think the only way out of that uncomfortable conversation is making screening so cheap via automation that you can basically run it for very low incremental cost as often as individual risk tolerance permits. This would be paid for on the back of earlier interventions vs late-stage, expensive interventions.

https://en.wikipedia.org/wiki/Low_Orbit_Ion_Cannon

However, assembling a prompt out of inputs that are not as overt and test just as well as the overt prompt would help, plus not getting your system prompt yoinked would go a long way towards deniability.

DARPA has probably been going after this since Attention is all you need.

So you test it like a black box, but IMO that suffers from the same pollution any of the other tests (coding ability, math ability, w/e) that currently suffer from, except it's even harder to evaluate objectively.

I don't think so. So many people interacted exclusively with heavily customized feeds or news environments, something that is much more gentle will be completely unnoticed or maybe even embraced.

> most people aren't really using LLMs for the subject areas that concern government propaganda

See all the people unironically using "@grok is this true?" It doesn't have to just be government propaganda (eg did Nixon break into Watergate?), it is more about shaping the boundaries of a conversation, framing, etc.

> You seem to be envisioning some kind of a world where people don't access the news or social media directly, but it is somehow passed through some kind of LLM transformation filter.

I envision a world where most people take the path of least resistance. They will not explicitly sign up for it, but will gradually shift to reading the easily digested stuff first. Look how popular tiktok is, the popularity of summarized info, etc. In that summarization and aggregation, there is plenty of room to steer a conversation or influence thought, especially over a large audience.

There is nothing here that will be an overt smoking gun, just a systematic bias towards a particular idea, thought, etc. Hard to prove and even harder to know it's happening.

It has never been cheaper or easier to influence millions of people, either deniably-subtly (though omission, selective results, "hallucinations" etc) or via sock puppetting.

If I am a government, there is nothing more valuable to me than being able to control the discussion, the overton window, and the prevailing narratives. LLMs are a very low cost way to do that, can be tailored at the individual level (unlike most current TV news, personal "feeds" etc) and have the benefit of a huge volume of context.

The models are effectively black-box weights and are resistant to bias-tests. IMO, a key development will be having an "overlay" of weights to apply on top of a "clean" world model that is tailored to whatever interests can pay for it. Being able to serve that overlay dynamically, or atleast per-user is the killer app.

split deployments -- perhaps you want to see how an update impacts something: if error rates change, if conversion rates change, w/e. K8s makes this pretty easy to do via something like a canary or blue green deployment. Likewise, if you need to rollback, you can do this easily as well from a known good image.

Perhaps you need multiple servers -- not for scale -- but to be closer to your users geographically. 1 server in each of -5-10 AZs makes the updates a bit more complicated, especially if you need to do something like a db schema update.

Perhaps your traffic is lumpy and peaks during specific times of the year. Instead of provisioning a bigger VM during these times, your would prefer to scale horizontally automatically. Likewise, depending on the predictable-ness of the distribution of traffic, running a larger machine all the time might be very expensive for only the occasional burst of traffic.

To be very clear, you can do all of this without k8s. The question is, is it easier to do it with or without? IMO, it is a personal decision, and k8s makes a lot of sense to me. If it doesn't make a ton of sense for your app, don't use it.

Spend some time learning it, using it to deploy simple apps, and you won't go back to deploying in a VM again imo.

This only gets better with ai-assisted development, any model is going to produce much better results for k8s given the huge training set vs someone's bespoke build rube-goldberg machine.

However, the tech exists for a reason and is not inherently bad, the issue is the lock-in, the lack of choice and interoperability.

IMO, there is plenty of space for an OEM who can play nice with others, offer an open (and vibrant ecosystem), and keep users coming back by choice, not by lock-in.

Idk about this, I have gotten almost every job I have ever had on cold-apply, including internships. The only one that wasn't that way was talking to a (internal) recruiter in college.

Don't discount that path. I did not have the best grades or anything, but (IMO) a mix of skills that was a good fit for the job at hand and confidence I could apply them.

Most of the people you will interact with in the (corporate) world have no understanding of their own understanding, and are operating in unknown unknown territory. Being confident, demonstrating competence in something jointly known/unknown or known/known helps a ton.