The private lending held back involved a) high interest rates b) unconventional methods of governance/collections which would be somewhat decoupled from Ant's lending platform down to the local level of collections.
An example of the ugly side of China private lending is what is termed "flesh loans". A young girl is forced to have nude pictures taken of her to secure her high interest loan repayment. Ant was going to put hundreds of billions in capital backing loans that had loosely controlled and often unethical/violent collections system.
Yes, curtailing loads of easy capital can be at odds with pressing domestic consumption growth. In this case, I think the government made a tough but decent decision.
From a legal perspective the US may be safer than other places if the US is the one seeking your data. The US doesn't need legal process to authorize digging into your foreign server.
From a practical perspective, I'm not sure any servers are safe anywhere...depending on who may want your data.
This type of attack has been happening a lot the past 2 years. I've seen one that was very well done...the GitHub account of a fairly well known security researcher had been compromised...their identity and code was being used as part of the recruitement. I reached out to the person...who was understandably embarrassed and told me they had reported this to LinkedIn + Github but saw no action.
This is the part that really irks me: LinkedIn and Github know this is the end goal of many of the rampant supply chain attacks but they a) don't have a first class mechanism for reporting b) don't seem to be improving their systems or even warning people. I have been hit be this enough times that I follow along to get screenshots of the scammer. One might think with all the surveillance systems Microsoft/LinkedIn/Github/Google-Meet/Calendly have in place that a potential victim reporting it along with an actual picture of the scammer could get us somewhere.
Nice. How easily is the protocol categorized by an external observer? I'm noticing protocols like wireguard more commonly hitting problems as websites rely on third party systems to protect them from non-human interactions.
...and the answer is still incorrect. You seem to want the short "answer" western media has pressed into your mind. The real answer is more complex. Protests were widespread throughout China. They were about the economy. The economy was regressing quickly as a result of a sharp western recession. Workers were losing everything and there was little social safety net in place as there is today. People had been told to work hard, get their kids to study hard and they would be rewarded...it was all falling apart. Western media wants you to focus on a small subset of student protesters regarding democracy.
LLMs are simply trained on inputs. For topics such as this you cannot expect the "correct answer" as it requires a nuanced discussion and more background info.
In short, its an inappropriate question be asking any LLM. This is the sort of thing that requires a small study group of human minds...open ones.
I’ve been using podman on Mac. It’s been a nice fit as the container build files are identical to what I use on my fedora server. I have noticed my 2 virtual core 4 gb Linode vps runs apps faster in the same container as when run on my MacBook Air M2 16 gb. I expected some performance overhead but didn’t think it would be noticeable as it is. Overall happy with podman. How might OrbStack differ?
I built this Clojure lib for robust high scale LLM calls wherein the consumer is usually a http request waiting on an SSE stream. https://github.com/jhancock/aimee
The article states: "Most applications are built on an architecture like the one above, where there are a number of stateless horizontally scaleable server replicas that can handle client requests."
Using the library I built, I have yet to worry about this as Clojure core.async, http libs and Java VM are so rock solid, I don't have a fragile set of stateless servers. Sure, at some point there are rare edge cases but it's nice to get very far along without worrying about them.
z.ai did go public on the HK exchange. They are under pressures similar to other public companies.
I know that China models are increasingly being trained and run using Huawei chips instead of Nvidia. I know China has a surplus of electricity from renewables (wind, solar, hydro).
z.ai models are open weights. GLM-5.1 is very close to Opus with obvious exception of session length.
Only academic models will be true open source as companies can't legally afford to disclose learning inputs.
In regards to "They want to train models on our engineering to replace us". Some software engineers in China can run circles around some of the best teams in Silicon Valley. Days of U.S. hegemony are over. I recommend you make peace and make friends.
I've been using z.ai and codex latest models since last September.
Each release has been an improvement.
codex handles longer sessions but the quality seems to decline and it tends to over engineer and lose focus. It will happily add slop on top of slop...which may pass immediate tests of "code works" but doesn't pass my criteria of "code as craft"
I'm using z.ai GLM with opencode. It's obvious when GLM loses its mind when the session gets too long.
I've been using AI to support programming for around 3 years now. The models have gotten amazing. However, unless there is a significant breakthrough I have determined that it's best for me to focus on short sessions.
I a) organize my work, b) improve my AGENTS.md, ensure source has appropriate comments to guide the models to the patterns and separation of concerns c) use shorter sessions d) review and test without AI. This approach means I still own my code. The AI is just an assistant.
With this approach GLM-5.1 is an excellent model. I never run out of token allotment on z.ai or codex plans. At this point, I only keep my OpenAI subscription as the ChatGPT desktop app is excellent at long web research tasks and I get codex with it.
AST of what? Will it read my clojure code's forms as such? What if my source file has a paran balancing error? I feel I'm thinking of this at the wrong level/angle.
I usually want the codex approach for code/product "shaping" iteratively with the ai.
Once things are shaped and common "scaling patterns" are well established, then for things like adding a front end (which is constantly changing, more views) then letting the autonomous approach run wild can *sometimes* be useful.
I have found that codex is better at remembering when I ask to not get carried away...whereas claude requires constant reminders.
Depends on your app cache needs. If it's moderate, I'd start with postgres...ie. not have operate another piece of infra and the extra code. If you are doing the shared-nothing app server approach (rails, django) where the app server remembers nothing after each request Redis can be a handy choice. I often go with having a fat long lived server process (jvm) where it also acts for my live caching needs. #tradeoffs
An example of the ugly side of China private lending is what is termed "flesh loans". A young girl is forced to have nude pictures taken of her to secure her high interest loan repayment. Ant was going to put hundreds of billions in capital backing loans that had loosely controlled and often unethical/violent collections system.
Yes, curtailing loads of easy capital can be at odds with pressing domestic consumption growth. In this case, I think the government made a tough but decent decision.