Yes, value is hard to calculate, but luckily market pricing mechanisms exist exactly for this purpose. There isn't a better number to use than what people are willing to pay for them.

So he's saying that on an enterprise plan, he'd be spending $2,180.16. He's not paying that much, but enterprises are.

Conceptually, I think that is the right analogy to think about. Prediction markets "want" to be a more accurate source of information, just like stock markets, so from that lens "getting" information to be more accurate is good. When government officials are placing bets on prediction markets, though, it's a massive violation of operational security, and leaking confidential information. They probably think that they are acting anonymously, but it creates so many opportunities for unfriendly state actors to get information, especially if people do it consistently.

(comment below: https://news.ycombinator.com/item?id=44804528 )

Using non-seasonally adjusted St. Louis FRED data (https://fred.stlouisfed.org/series/NA000349Q), and the AI CapEx spending for Meta, Alphabet, Microsoft, and Amazon from the WSJ article (https://www.wsj.com/tech/ai/silicon-valley-ai-infrastructure...):

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Q4 2025 consumer spending: ~$5.2 trillion

Q4 2025 AI CapEx spending: ~$75 billion

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Q1 2025 consumer spending: ~$5 trillion

Q1 2025 AI CapEx spending: ~$75 billion

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Q2 2025 consumer spending: ~$5.2 trillion

Q2 2025 AI CapEx spending: ~$100 billion

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So, non-seasonally adjusted consumer spending is flat. In that sense, yes, anything where spend increased contributed more to GDP growth than consumer spending.

If you look at seasonally-adjusted rates, consumer spending has grown ~$400 billion, which might outstrips total AI CapEx in that time period, let alone growth. (To be fair the WSJ graph only shows the spending from Meta, Google, Microsoft, and Amazon. But it also says that Apple, Nvidia, and Tesla combined "only" spent $6.7 billion in Q2 2025 vs the $96 billion from the other four. So it's hard to believe that spend coming from elsewhere is contributing a ton.)

If you click through the the tweet that is the source for the WSJ article where the original quote comes from (https://x.com/RenMacLLC/status/1950544075989377196) it's very unclear what it's showing...it only shows percentage change, and it doesn't even show anything about consumer spending.

So, at best this quote is very misleadingly worded. It also seems possible that the original source was wrong.

People in these communities are generally quite smart, and it’s seductive to reason in a purely logical, deductive way. There is real value in thinking rigorously and in making sure you’re not beholden to commonly held beliefs. But, like you said, reality is complex, and it’s really hard to pick initial premises that capture everything relevant. The insane conclusions they get to could be avoided by re-checking & revising premises, especially when the argument is going in a direction that clashes with history, real-world experience, or basic common sense.

Comments URL: https://news.ycombinator.com/item?id=43124809

Points: 2

# Comments: 0

We train on real records, and even though they are de-identified in training we still have to keep the model closed and under careful management to protect against the possibility of information leaking.

We are, though, definitely invested in this corner of research, and want to be able to work with others to push medical AI forward.

Given that, the best model for us is to collaborate on an engagement-by-engagement basis. For now we'd look to find ways to do the work directly involving LLMD within our systems.

If you research in the field and have some ideas, I'd love to chat!

Accuracy and deploying in appropriate use cases is key for real world use. Building guardrails, validation, continuous auditing, etc is a larger amount of work than model training.

We don't deploy in EHRs or sell to physicians or health systems. That is a very challenging environment, and I agree that it would be very difficult to appropriately deploy LLMs that way today. I know Epic is working on it, and they say it's live in some places, but I don't know if that's true.

Our main production use case for LLMD at PicnicHealth is to improve and replace human clinical abstraction internally. We've done extensive testing (only alluded to in the paper) comparing and calibrating LLMD performance vs trained human annotator performance, and for many structuring tasks LLMD outperforms human annotators. For our production abstraction tasks where LLMD does not outperform humans (or where regulations require human review), we use LLMD to improve the workflow of our human annotators. It is much easier to make sure that clinical abstractors, who are our employees doing well-defined tasks, understand the limitations in LLM performance than it would be to ensure that users in a hospital setting would.

Comments URL: https://news.ycombinator.com/item?id=41878959

Points: 48

# Comments: 19

Comments URL: https://news.ycombinator.com/item?id=35809552

Points: 4

# Comments: 0

When learning to program, I found typing out code samples had a similar effect to writing them out by hand, and felt more natural to the domain. But in other subjects in school, it felt like my comprehension was lower when I typed lecture notes instead of handwriting them. I was never sure whether that was something inherent to hand writing vs typing or it was just because I had more of a barrier between typing and thinking than I did with handwriting.

It always seemed clear that the value of writing something verbatim was that it forced your brain to internalize it in a way you don’t need to when reading. It takes longer to hand write than to type — maybe that is the value of hand writing vs typing? Or when typing I needed to think more about the act of typing, whether that was thinking about formatting/positioning or just being less natural than handwriting? Maybe programming is just a special case where you always think through typing?

I lean towards thinking it’s somewhat domain specific. Clearly with physics or math if you had to typeset equations with LaTeX that would get in the way, and typing would be a barrier to understanding. With programming, every idea is expressed through typing, and so typing is a natural way to imprint ideas on your brain.

Comments URL: https://news.ycombinator.com/item?id=31899590

Points: 3

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=31897809

Points: 8

# Comments: 0

Interviewing elsewhere seems like the right thing to do. I'm confident there are environments out there where other engineers would challenge you.

It's possible that could be the case at my company, PicnicHealth. If you'd be interested in chatting, reach out (email in profile)

Comments URL: https://news.ycombinator.com/item?id=30222329

Points: 2

# Comments: 0

PicnicHealth is building healthcare's missing data layer, making medical data patient-centric, computable, and complete. We collect and structure medical records for patients, empowering them to monitor and control their care. Patients can consent to share their anonymized data with life sciences researchers working to better understand disease progression and treatment.

We just announced our $25m Series B and are growing quickly. We're hiring for front-end, full-stack, data engineering, ML, data science, and product, among other roles. Join kind, focused, mission-driven team using machine learning and modern software development practices to improve health.

https://picnichealth.com/careers

PicnicHealth works with patients to collect, digitize, and manage their medical records, empowering them to monitor and control their care via a personal health timeline. Through partnerships with life sciences companies, we aggregate and organize data from groups of research volunteers, which helps power some of today’s most cutting-edge medical research.

We also just announced our Series A & B funding rounds (totaling $35M) today.

We're hiring for:

* Data Engineers

* Site Reliability Engineers

* Engineering Managers

* Front-End Engineers

* Full-Stack Engineers

* Data Scientists

* Product Managers

And more. Check out a full list: https://picnichealth.com/careers

Tech Stack: Our main web service is in Node.js, and most other services are in Python (mostly machine learning services, using Keras, Tensorflow, etc). Our frontend is React + Redux, database is PostgreSQL, and services run in a Kubernetes cluster hosted on Google Cloud Platform (Google Kubernetes Engine).