While the article emphasis on chaos and short time with China's doctor, they are very accessible, 2 USD you visit a fully trained doctor at top hospital, have your CT scan done the same day with 50 USD even if you pay out of packet.
As for AI, my personal experience is ChatGPT and Gemini is more effective then DeepSeek for healthcare issues. I do hope DeepSeek or Doubao can catch up.
I’m sure that’s a state owned hospital and not a private hospital. The last time I went to a private hospital in Beijing for my yearly checkup I paid 100 RMB for my part of the checkup cost (I also had private local insurance and was eligible to use public hospitals, but never had time for that).
Yes, but they are inconvenient, you had to line up in the morning for an appointment when I lived in Beijing, and scalpers would commonly grab most of the tickets early to sell to you for a few hundred RMB. I hear they have a better system now, something based on WeChat without the scalping.
Private hospitals were mostly staffed by doctors from Eastern Europe and Iran, at least the one I went to (UFH in Lido, Beijing), lots of rich Chinese customers. It was comfortable and you just made an appointment on the phone. You got to wait in a nice waiting room with a plush couch rather than a concrete brutaleski waiting room with plastic chairs, private birthing rooms for babies rather than a community birthing room.
Having worked at both FAANG companies and startups, I can offer a perspective on AI's coding impact in different environments.
At startups, engineers work with new tech stacks, start projects from scratch, and need to ship something quickly. LLMs can wrtie way more code. I've seen ML engineers build React frontends without any previous frontend experience, flutter developers write 100-line SQL queries for data analysis, with LLM 10x productivity for this type of work.
At FAANG companies, codebases contain years of business logic, edge cases, and 'not-bugs-but-features.' Engineers know their tech stacks well, and legacy constraints make LLMs less effective, and can generate wrong code that needs to be fixed
It might not quite be there yet, but one key advantage large codebases have that I think LLMs in time will be able to better exploit is the detection of existing patterns - presuming they're consistent - and application to new code doing similar things or to fix bugs in existing code that deviates from the pattern in some way that causes a bug.
It's a different thing to what you're talking about, but it's one way I'd expect to see LLMs contribute a lot to productivity on larger codebases specifically.
large application codebase - consistent - have you worked in the field? I feel like usually there are 3 or 4 patterns from different people/teams at different points in time that spearheaded a particular ideology about how things "should" be done.
latest llama 3.1 is in a different repo, https://github.com/meta-llama/llama-models/blob/main/models/... , but yes, the code is shared.
It astonishing that in software 2.0 era, powerful applications like llama has only hundreds of lines of code, and most work hidden in training data.
Source code alone is no longer that informative as Software 1.0
Can you elaborate a bit more why render is good? we are on heroku and I have evaluated alternatives every 6 months since heroku/github outage 2 years ago [1]. But I don't see how render is better. 2 years ago render postgres did not have PITR. now they have build it, but Render's postgres offering is even more expensive than heroku, and queries run a bit slower on similar spec machines based on my test. I also don't like render charges per seat in addition to infra cost.
Aside from the content itself, the "Listen to Article" button uses a robotic, outdated TTS voice.
Shouldn't a company like Google use their latest technologies in public-facing content, particularly when discussing AI progress?
I'm genuinely curious about the decision-making process behind this choice.
As for AI, my personal experience is ChatGPT and Gemini is more effective then DeepSeek for healthcare issues. I do hope DeepSeek or Doubao can catch up.