A quick google search of crypto lendin companies that went bust: Several major cryptocurrency lending companies collapsed during the 2022 market downturn, setting off a domino effect across the industry. The highest-profile failures include: BlockFi: Filed for Chapter 11 bankruptcy in November 2022 after the collapse of FTX, which had previously extended a credit line to keep the lender afloat. Celsius Network: Filed for bankruptcy in July 2022 due to a massive liquidity crisis triggered by falling crypto prices and heavy exposure to the collapsed Terra ecosystem. Voyager Digital: Filed for Chapter 11 bankruptcy in July 2022 following massive defaults by the crypto hedge fund Three Arrows Capital (3AC). Genesis Global: The prominent institutional crypto lender filed for Chapter 11 bankruptcy in January 2023 after suffering catastrophic losses from its loans to 3AC and Alameda Research (FTX). These bankruptcies resulted in tens of billions of dollars in investor losses and highlighted the risks of extreme interconnectedness and a lack of regulatory safeguards in the crypto lending space. No body know how solvent Nexo is as a crypto lender. They could be 100% legit and 100% solvent. But if they are not 100% solvent (like Celsius, Voyager, Blockfi, Genesis), without a Mica license, all who use Nexo in the EU will need to leave and taking their crypto with them. That will be a stress test for them. submitted by /u/punishGoalhanging [link] [Kommentare]

I'm proposing a way to handle massive context longer than a model's context window by treating semantic compression as the noise function of a diffusion-like process. Instead of denoising masked tokens into coherent text (like DiffusionGemma or Nemotron-Diffusion do for generation), the model reads the source document in multiple passes at decreasing compression levels, heavy summary first, verbatim last all the while it iteratively refines an "integration state" (the output) through structured edit operations like add/replace/remove. The context window only needs to fit the current compressed view, not the full source. Three pieces: Context Diffusion: Multi-pass reader that refines an integration state across passes, each conditioned on a different compression level of the source. The source stays on disk and is never reconstructed into the window. The architectural shape converges heavily with Zhang, Kraska & Khattab's Recursive Language Models (2025) (I found their paper after writing most of mine and don't claim priority on the multi-pass structure). Diffusion-based Semantic Compression (DiSCo): The framing I think is novel. Using semantic compression as the noise function, so the "noised" view is much shorter than the source and context length is managed by the compressor, not the model window. This is a different noise domain from masked or vocabulary-level diffusion. Pass-Conditioned Reading: Training conditioned on position in the multi-pass schedule so the model learns different behaviour for early passes (broad understanding from coarse views) vs late passes (precise retrieval from verbatim views). What I actually tested and the honest result: Experiments using off-the-shelf models No fine-tuning - this was an untrained floor test. The outcome so far: "not unfeasible," not "it works": -Components worked better in isolation than the full record>retain>compose chain did. -The bottleneck is retention/recombination across passes. -Pre-registered kill-conditions failed and were reported. -The small signals were enough to justify training a small model, but not enough to call the architecture validated yet. Methodology was deliberately disciplined - kill conditions, published negatives, documented self-corrections. Hopefully that's what makes a small-scale claim credible when you don't have a lab behind you. Two questions for anyone still reading: -Prior Art: Has anyone seen compression-as-noise (using length-reducing semantic compression as the noise schedule in a diffusion-style iterative process) in the diffusion-LM literature? I've searched extensively and haven't found it, but I could easily be missing something. -The next step is model training with synthetic data to test whether training resolves the binding bottleneck. I'm an independent researcher on consumer hardware who is has not done model training before. I'd appreciate any guidance or if anyone has compute access or wants to collaborate, I'm interested. Proposal: https://github.com/dev-boz/diffusive-semantic-compression Experiments + full findings: https://github.com/dev-boz/pass-conditioned-reading Archived on Zenodo (DOI: 10.5281/zenodo.20695611), CC-BY-4.0. This is the first thing I've published. Expecting to have it torn apart. submitted by /u/Bravo_Oscar_Zulu [link] [Kommentare]

> "The mainstream consensus on gold in 2026 has organized itself around a single, load-bearing thesis: central banks are buying at the fastest pace since the 1950s, this buying is structural and price-insensitive, and it constitutes a permanent floor under the gold price." submitted by /u/NoidoDev [link] [Kommentare]

I really don’t get how people trusted a snake with their savings. It boggles my mind. It’s not like he didn’t have a reputation before and he was just some new guy in the space where maybe you could’ve given him the benefit of the doubt. In 2000, he took part in an accounting scandal for MicroStrategy and got charged for it by the SEC. STRC listed 11 percent returns through dividends for STRC that could pause dividends at any time. I know this isn’t the same but Anchor offered around a 20 percent APY in May 2022 for Luna. We all see how that went down. People were confident and sure that this yield could go on forever. I am not claiming it’ll collapse at this exact moment and won’t pretend to know the timing. However, there are so many signs pointing that this will end catastrophically. They own 4 percent of all Bitcoin in existence. People keep saying “they can just sell their Bitcoin.” If they are over-leveraged, the amount of cash and Bitcoin they have won’t matter. They’ll crash at some point and maybe those of you holding might get a dividend payment but don’t get caught with your pants down. At this point it’s a rite of passage for scammers to be on the cover of Forbes. For example, look at Sam Bankman-Fried and Elizabeth Holmes. The signs are there. Those of you invested feel free to call me whatever you want but we’ll see who’s right in the end. Just don’t pretend the signs weren’t there. Best of luck to all of you and I hope you don’t get burned badly. Edit: Those of you invested in MSTR and STRC feel free to talk your shit but I know I’m right and your post will age like milk. Yes I’m petty. submitted by /u/Repturtle [link] [Kommentare]

It seems all crypto going down and up at the same time, same rate and (not 100% accurate but) same percentage, so why does everyone say what they do? Like I hear all the time “XRP trash” and “solana is a glorified meme coin” and etc, just simplified examples. Like at this point does it matter ? It just seems everything follows bitcoin except meme coins submitted by /u/vajinasalad [link] [Kommentare]

Hi everyone! I'm a university student currently working on a UX research assignment focused on the Binance P2P experience. As part of my research, I've been using Binance for a while now and I'm also trying to understand how real users feel about the platform. If you've used Binance P2P (whether occasionally or regularly), I'd really appreciate your thoughts on stuff like: Is there a feature you wish Binance would improve or redesign? What's the most frustrating part of the P2P experience? Is there anything that feels confusing or unintuitive? Have you ever made a mistake because of the interface or workflow? If you could change one thing about the P2P UI, what would it be? Thanks in advance to anyone who shares their feedback. submitted by /u/manavalann [link] [Kommentare]

What is live continual learning? Who uses it and what are the use cases? Is it for hospitals, legal? Or corporate? Who are the real users? In the bigger picture what is it even useful for? submitted by /u/fourwheels2512 [link] [Kommentare]