@Sam

opplic is the AI Growth OS for web agencies. Connect the client sites you manage, approve real improvements in one click, and report the leads each one earned.

These ten configuration tweaks dramatically improved accuracy, reduced interruptions, and saved hours of engineering time every month.

This major update transforms Maxint into a more comprehensive financial workspace by introducing full-scale, institutional-grade financial reporting alongside advanced tax intelligence and streamlined investment tracking.

A Scaling Law Perspective for Marginal Utility per Dollar

AI didn't just make content cheap. It made authenticity expensive.

MIT Associate Professor Phillip Isola explains what agentic AI is, how these systems are used, what applications they are best suited for, and what the future may hold for this exploding technology.

Tldr: yes Ill try to make if short. Co-worker, in her late 60, approaches me to ask some questions about crypto. She thinks she got scammed of about £250k. She doesn’t know how. She started in 2024, by depositing money into different exchanges, until the banks blocked her deposits. Opened other accounts, with different banks, to deposit more. Got blocked. Then, from what i understood, english is her 2nd language, she got in touch with some people she found on facebook, to help her buy crypto for her. Got scammed for some money, then the avalanche of scammers started to come in. She then started talking, or they got in touch with her (unclear), some law firm from Switzerland, with HQ in centre London, where a furniture store is, as per their contact page. And she got allocated a lawyer/recovery person, which sent her 28 ETH on Solona network, in base wallet. Apparently, what they could recover. Now, these 28eth are not worth £112K, the amount it shows in her wallet. Now this law firm, this recovery person, is asking for 6.4 ETH either for taxes, insurance, EU tax, all sorts of things. I have told her to cut all communication with everyone. I told her she got scammed and to trust no one and that, unfortunately, the 28eth is not real. I dont know how else to help her. Any suggestions please? submitted by /u/indeclin3 [link] [Kommentare]

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: STRAP (Krishna et al., EMNLP 2020) — reframe as paraphrase generation, create pseudo-parallel pairs automatically, fine-tune a style-specific inverse model. Seems like the cleanest unsupervised framing. Unfortunately, it focuses on the sentence level, and I need a way to maintain context over thousands of pages Translating away Translationese (Jalota et al., EMNLP 2023) — directly targets the "sounds like a translation" problem with a self-supervised + LM fluency + semantic similarity loss setup. 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 termonlify 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] [Kommentare]

Hyperliquid wrapped Here's the thing: PnL is the one number we all obsess over because it's right there. But everything around how you actually trade? We're basically guessing. How many trades did you really make this year? What's your actual win rate, not the vibe, the number? What asset do you trade most, and is that even on purpose? What time of day do you trade most? (mine was 2am, which explains a lot) Full disclosure: I work at Otomato and we built something that lays this out. We're sharing it because we found this genuinely useful ourselves before we even thought about others. Happy to link it he Mostly it made me realize how little we know about our own trading, even though we live in this app every day. Genuinely curious: do you think you have an accurate read on your own trading habits? Or do you suspect you'd be surprised? TL;DR: Built a tool to surface the full picture of your Hyperliquid history submitted by /u/dyloum84 [link] [Kommentare]