They are adding B12 as a way to say that it’s tailored to individuals and not available.
I’ve used mainly compounded medicine over the last five years and find the fervent dislike that people have for compounders bizarre.
If you look into generic regulation in the US, the standards are already through the floor. I’d rather work with someone who has a more direct financial incentive to not fuck up.
Sure, and Novo spent 2M last quarter on lobbying. Nobody in this industry comes out looking wonderful. But the compounders who are meeting demand are not Hims & Hers.
So millions of Americans should deal with years of obesity because Novo is a disaster, insurance coverage is ridiculous (any insurer accurately charging for the purpose of risk mitigation should be paying people to take GLP-1s, when instead they are out of coverage for most plans), and there exists no government body to do what you’ve said?
While I'm sympathetic to this argument, I should point out patent time to expiration for medicine in the US is pretty inoffensive (relative to how bad it could be, like software patents), and we already have plenty of drugs for excreting excess. We get a big basket of drugs into public domain each year, and government would be wise to publicly celebrate this, I think; would help with the general sense of impending doom citizens feel.
Semaglutide molecule patent will expire in 2031 here (many caveats to this). For the most part, you can get any pill ~15+ years old for ~nothing without insurance, but associated devices like auto-injectors can extend this due to goofy rules; I expect execs thoughtfully considered medical patent law when deciding to initially trial and release GLP-1s as an injection.
Countries have incentives to manipulate population data. Most error that I’m aware of is not attributable to poor data quality. For example, if you have a real estate bubble you have a strong incentive to show population growth.
>For example, if you have a real estate bubble you have a strong incentive to show population growth.
That's one source of bias that is present at a specific time. Mostly you would have competing incentives. There is usually more than one agency that runs does the counting. Vital records registration, voter rolls and tax payers lists, for example are separate agencies in some countries. Not every tax payer is a voter and not everyone who was born still lives in the country. The sources are sometimes cross-referenced too. Then there is usually a place that needs to do macroeconomic forecasting and needs to have some numbers to do it's job.
King Louis XIV lost a bunch of his land to astronomers able to more accurately measure said land. This is the sort of thing that can happen when you want to turn your country into a world leader in science.
This study published in Nature [0] says that rural populations in particular are typically UNDERCOUNTED (exactly like the Papa New Guinea in the OP's article), and that this happens at similar rates across poorer and wealthier countries: "no clear effect of country income on the accuracies of the five datasets can be observed."
Qdrant is one of the few vendors I actively steer people away from. Look at the GitHub issues, look at what their CEO says, look at their fake “advancements” that they pay for publicity on…
The number of people I know who’ve had unrecoverable shard failures on Qdrant is too high to take it seriously.
I’m curious about this. Could you please point to some things the CEO has said, or reports of shard failures?
The bit about paying for publicity doesn’t bother me.
Edit: I haven’t found anything egregious that the CEO has said, or anything really sketchy. The shard failure warnings look serious, but the issues look closed
There used to be a benchmarking issue with a founder that was particularly egregious but I can’t find it anymore.
The sharding and consensus issues were from around a year and a half ago, so maybe it’s gotten better.
There are just so many options in the space, I don’t know why you’d go with one of the least correct vendors (whether or not the correctness is deception is a different question that I can’t answer)
There needs to be some element of magic and push back. Every turn has to show that the AI is getting closer to resolving your issue and has synthesized the information you've given it in some way.
We've found that just a "Hey, how can I help?" will get many of these customers to dump every problem they've ever had on you, and if you can make turn two actually productive, then the odds of someone dropping out of the interaction is low.
The difference between "I need to cancel my subscription!" leading to "I can help with that! To find your subscription, what's your phone number?" or "The XYZ subscription you started last year?" is huge.
Tickets are a very different domain though. Tickets are the easiest use case for AI (as you have the least constraints on real-time interaction), but reference cases in tickets have ridiculously low true-resolution (customer did not contact you about the same issue again).
The default we've seen is naive implementations are a wash. Bad AI agents cause more complex support cases to be created, and also make complex support cases the ones that reach reps (by virtue of only solving easy ones). This takes a while to truly play out, because tenured rep attrition magnifies the problem.
We're working on this problem at large enterprises, handling complex calls (20+ minutes). I think the only reason we have any success is because the majority of the engineering team has been a customer support rep before.
Every company we talk to has been told "if you just connect openai to a knowledgebase, you can solve 80% of calls." Which is ridiculous.
The amount of work that goes in to getting any sort of automation live is huge. We often burn a billion tokens before ever taking a call for a customer. And as far as we can tell, there are no real frameworks that are tackling the problem in a reasonable way, so everything needs to be built in house.
Then, people treat customer support like everything is an open-and-shut interaction, and ignore the remaining company that operates around the support calls and actually fulfills expectations. Seeing other CX AI launches makes me wonder if the companies are even talking to contact center leaders.
I’ve used mainly compounded medicine over the last five years and find the fervent dislike that people have for compounders bizarre.
If you look into generic regulation in the US, the standards are already through the floor. I’d rather work with someone who has a more direct financial incentive to not fuck up.
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