There are whole companies that de-anon ad data as a service. Which gives the lots of data brokers the ability to not do the last mile and feel good about themselves. It’s a joke.
I remember when the first article was posted. Their method requires two parallel corpuses e.g. people who write on LinkedIn (under their real name) and Reddit.
Also, people who post under their real name are likely to write with their real voice:
> Any deanonymization setup with ground truth introduces
distributional biases. In our cross-platform datasets, the pro-files are likely easier to deanonymize than an average profile: the very fact that ground truth exists implies that the user may not have cared about anonymity in the first place. Similarly, two split-profiles of a single user are inherently alike, whereas two pseudonymous accounts of the same person (e.g., an official and a pseudonymous alt account) might expose more heterogeneous micro-data.
> It is wise for these Chinese fabs to eventually use a very aggressive dumping strategy to price well below cost push out other players forever, especially in DRAM.
Crucial's departure from the consumer market left such a gaping hole, that CXMT doesn't even need to push other players out to gain a footing.
I haven't really looked at MCP because it sounds like it is little bit broken?
The Remote Control API is just HTTP/JSON so Claude wrote some powershell scripts to query objects from the endpoint.
We gave Claude a Character Actor in game with an AI controller attached, and it can call functions to the AIController - MovetoXYZ(), Teleport(), TakeScreenshot() etc
Jules is the first and only one to add a full API, which I've found very beneficial. It lets you integrate agentic coding features into web apps quite nicely. (In theory you could always hack your own thing together with Claude Code or Codex to achieve a similar effect but a cloud agent with an API saves a lot of effort.)
You'd be surprised what can be done when data from different source is fused together.
Large-Scale Online Deanonymization with LLMs: https://news.ycombinator.com/item?id=47139716
Robust De-anonymization of Large Sparse Datasets: https://www.cs.cornell.edu/~shmat/shmat_oak08netflix.pdf
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