The discovery underscores the increased effort being poured into Mac infostealers.

I'm currently working on Bramble, an open source password manager with P2P cross-device sync. Initially I released the Chrome extension, but recently I also published the Android app and iOS is pending Apple's approval. Besides that, the latest version also includes passkey storage for all platforms! About Bramble: It aims to be as feature-rich as all popular and a replacement for cloud-based providers. I don't think we need to store our data in the cloud and be at the whims of companies raising

The Federal Bureau of Investigation (FBI) said today it worked with industry partners to seize hundreds of domains associated with NetNut, a sprawling residential proxy service operated by the publicly-traded Israeli company Alarum Technologies [NASDAQ: ALAR]. The action comes roughly two weeks after KrebsOnSecurity published findings from multiple security firms connecting NetNut to the Popa botnet, a collection of at least two million devices that have been compromised by malicious software with little or no consent from victims.

AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.

Hey all. I recently started working on a project to improve machine-translated webnovels via style transfer. The basic idea is to take the clunky translated prose and rewrite it to something that reads like it was written by a professional author, while remaining as faithful as possible to the original text. The source material is mostly amateur/MTL output full of direct sentence structure translations carried over from Chinese, awkward honorifics, over-translated idioms, that kind of thing. The goal isn't retranslation from the source but a cleanup of the English output. The tricky part is I have no clean data pair for supervised approaches. I've been looking at a few directions: Fine-tuning on target-style prose — collect high-quality English novels, fine-tune a small LLM to rewrite in that register. Just use a local LLM — run a local LLM and provide it with guidelines on what to rewrite and leave the same. No fine-tuning or anything needed, just hoping the transformer can handle it. A few things I'm stuck on: Is the faithfulness/fluency tradeoff actually manageable at the sentence level, or do I need paragraph-level context or more to preserve narrative coherence? How do people handle domain-specific terms like terminology and catchphrase-type things that need to survive the rewrite unchanged? Hard constraints during decoding, or just hope the model learns to leave them alone? Happy to hear about similar projects, relevant papers I might have missed, or just general lessons from working in this space. Thanks. submitted by /u/Divine_Invictus [link] [留言]