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DOW JONES DOWN 384.49 POINTS, OR 0.72 PERCENT, AT 53,078.56 AFTER MARKET OPEN NASDAQ DOWN 129.18 POINTS, OR 0.49 PERCENT, AT 26,201.91 AFTER MARKET OPEN S&P 500 DOWN 25.84 POINTS, OR 0.34 %, AT 7,682.14 AFTER MARKET OPEN

🔥First Japanese Public Company Buys $HYPE Tokyo Stock Exchange Growth Market-listed Eole Inc. disclosed purchasing ~1,078 HYPE for about ¥10.1 million (~$66,000) around July 28 (public coverage July 29–August 1). It plans to scale the position to ¥100 million (~$611,000) by end of August via additional purchases. This is reported as the first such acquisition by a Japanese listed company. Eole framed it as part of its “Neo Crypto Bank” strategy, citing Hyperliquid’s infrastructure suitability for AI-agent commerce/settlement alongside existing Bitcoin holdings.

📊 This week, 95,993 RWA holders were onboard this week, bringing the total to 1,091,507. @RobinhoodApp contributed the most among all networks and platforms at 90,078. Meanwhile, @Solana, @xStocksFi, @Ethereum, @OndoFinance added thousands of new RWA holders. @BNBCHAIN, @tether, @Paxos, @Spiko_finance, and @base round out the top 10.

BREAKING: NovaRed Mining’s Wilmac Project Is Nearly 3x the Size of Manhattan, Comparable to ~30,000 Football Fields - and Already Entering the Conversation as One of the Largest Emerging Copper Projects by Land Scale $NRED / $NREDF is becoming impossible to ignore. The company’s Wilmac Copper-Gold Project in British Columbia now spans: • 16,078 hectares • 160.78 km² • ~39,732 acres • Nearly 3x Manhattan Island • Roughly 30,000 football fields By land scale alone, Wilmac is already comparable to some globally recognized copper mining districts and large-scale projects. This is why many investors are beginning to view NovaRed as a potential mega-project in development rather than a typical junior exploration company. But the bigger differentiator may be the AI infrastructure behind the land package. NovaRed’s MetalCore AI platform is designed to integrate: • satellite imagery • geology • geochemistry • magnetic anomalies • geophysics • ownership records • predictive modeling into one mineral intelligence engine capable of identifying high-probability exploration targets at scale. And the market opportunity could be massive. There are roughly 77 million landowners in the United States controlling more than 1.3 billion acres of land — most of which has never been analyzed using modern AI-driven mineral discovery systems. At the same time, copper prices are surging toward historic highs as demand accelerates from: AI infrastructure, hyperscale data centers, robotics, EVs, defense systems, transformers and global electrification. Recent North Lamont results reported copper values up to 379 ppm Cu alongside porphyry indicators and magnetic anomalies, with additional geophysical work already underway. Still early-stage. Still speculative. But by land scale, AI vision and exposure to the copper supply chain, this is becoming a very serious story to add to your watch list. DYOR! Do your work ! Do your own research ! Don’t rely on this information ! Everybody can lose in stocks !

Training data for terminal agents often ships tasks where instruction, environment, solution, and verifier disagree — producing unsolvable tasks. A new synthesis framework cuts that at the root. Title: FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis URL: 📌 Overview Reconstructs related skills into rich scenarios, builds the environment first, and grounds instruction, solution, and verifier in that same executable state. 🧩 Problems it solves ・Multi-stage generation loses source dependencies and intermediate states ・Misaligned instruction/environment/solution/verifier yield unsolvable, unverifiable tasks ⚙️ Method ・Collects 71K+ skills, reconstructs into 5-dimensional scenarios ・Builds the environment in Docker, exposing its real state as a shared channel ・Generates instruction→solution→verifier sequentially; a router pinpoints and repairs only the failing part 📊 Results ・Synthesizes 6,078 validated tasks at 22.77 tests/task on average ・Fine-tuning Qwen3.5: 4B +40.5%, 9B +30.1%, 27B +16.5% ・The 27B (47.57) nears the ~15x larger 397B (49.06) ・70% end-to-end yield vs 15–28% for competitors A clear case that high-quality executable tasks come from careful state grounding, not brute-force generation. #LLMAgents# #TerminalBench#