On top of breaking into the top 10 crypto rankings by market cap, Hyperliquid's Open Interest hit $9.3B on July 1, 2026, surpassing Bybit, MEXC, Gate, and other major CEXs to become the second-largest perp exchange by OI, behind only Binance ($22.1B). A primer for those unfamiliar with Open Interest (OI): it is the total number of outstanding derivative contracts that have not yet been settled or closed. For the perps space, OI is the equivalent of Total Value Locked (TVL) in DeFi it basically shows how much capital is actually live and trading. Besides HIP-3's constant OI records, HYPE also experienced a trend shift with stocks perps tripling its share of HIP-3 trading volume to 47%, with commodities declining to 21%. This is from our Q2 2026 crypto industry report ,more charts and breakdowns on Hyperliquid, HYPE, and the rest of the market in the full writeup if anyone's interested. writeup: https://www.coingecko.com/research/publications/2026-q2-crypto-report submitted by /u/khai0001 [link] [Kommentare]

Hey everyone, Like a lot of you, I've been in the crypto space for a while. And like a lot of you, I've experienced the sheer anxiety of trying to cash out profits. Between jumping through hoops on multiple exchanges, waiting days for ACH transfers to clear, and paying ridiculous withdrawal fees, the off-ramp process is still way harder than it needs to be. I realized there had to be a more seamless way to go from crypto to fiat without the traditional exchange bottlenecks. So, for the past few months, I’ve been building XRPay. What is XRPay? It’s a streamlined crypto cashout app designed to make off-ramping as easy as buying a cup of coffee. Here is what I’ve focused on: Speed: Getting your funds to your bank account fast, without the typical multi-day waiting periods. Low Fees: Skipping the heavy exchange withdrawal fees so you keep more of your gains. Simplicity: No need to swap to a stablecoin, send to a secondary exchange, and then wait. Just a straight path from crypto to cash. It’s currently in early release/beta, and I would genuinely love to get feedback from this community. What are your biggest pain points when cashing out? What features would make an off-ramp app a "must-use" for you? You can check it out here: https://apps.apple.com/us/app/xrpay-crypto-cash-out/id6777407162 Thanks for reading, and I'll be hanging around the comments to answer any questions! submitted by /u/Nearby_Tea_228 [link] [Kommentare]

hey, first time going through this process, the deadline is by today AoE so i'm kinda worried, i just can't seem to find it next to the "official comment" button on my submission, what am i doing wrong? anyone who's having the same issue? https://preview.redd.it/lywderhn4rdh1.png?width=1980&format=png&auto=webp&s=9674e608dce438035bc037c1df904d53758db8f1 submitted by /u/DLLDoesShit [link] [Kommentare]

I had a robo-butler for my birthday create this wonderful piece of work that only tracks BTC mvrv z-score, and will send alerts based on undervalued threshold, which is usually behind a paywall. we still have 3 months to go. custom value alerts coming when tokens reset submitted by /u/vovawasabi [link] [Kommentare]

I have released EU AI Act OpenRAG, a downloadable corpus of Regulation (EU) 2024/1689 designed for RAG and legal-NLP experimentation. Instead of sliding character windows, the corpus chunks on the Regulation’s legal structure: one chunk per article paragraph one per recital one per Article 3 definition one per annex point chapter, section and provision metadata stored separately The resulting SQLite database contains 933 chunks and a normalized 1024-dimensional BGE-M3 embedding for every chunk. It also includes exact EUR-Lex links, Article 113 application-date metadata and deliberately narrow derived labels. Direct textual classification is stored separately from broader regulatory-regime association, and ambiguous cases remain NULL. I evaluated it against the AI Act Evaluation Benchmark using a like-for-like whole-unit baseline: scenario article recall@20: 0.541 structural vs 0.449 baseline QA article hit@10: 0.927 structural vs 0.898 baseline overall RAG classification remained close and was slightly lower on the structural corpus, suggesting that generator behaviour dominates that task more than chunk granularity I have published the full results, limitations, derivation methodology, label audit and licensing breakdown rather than only the favourable metrics. Dataset: huggingface.co/datasets/faitholopade/aiact-openrag I would appreciate technical feedback, particularly on the retrieval evaluation, structural chunking methodology and what additional baselines would be most useful. submitted by /u/Automatic-Forever-63 [link] [Kommentare]