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This rook is different 😮‍💨 Olivia Miles beats the halftime buzzer from deep to cap a 16-3 @minnesotalynx run to close the second quarter. LAS-MIN | Prime Video Tap to Watch:

"[Jason Licht] said 'I am not trading Vita Vea.'" Tampa Bay Buccaneers beat reporter @NFLSTROUD discussed Jason Licht's desire to keep Vita Vea on the team 🗣️ (via @PatMcAfeeShow)

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AMD and SpaceX both reported Q2 after yesterday's close. Nasdaq doesn't reopen for another 17 hours. On Binance, the verdict was in within minutes. 🔸 AMDUSDT ~US$525 → ~US$475. Beat on revenue, EPS and guidance. Missed on gross margin, 54% vs 56%. 🔸 SPCXUSDT ~US$126 → ~US$115. Revenue +92% YoY. Capex US$18.4B, over 6x a year ago. AI's seller and AI's buyer, marked down on the same question. Price discovery no longer waits for the bell.

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

Five months ago we gave the Claude agents a brand new $50,000 portfolio and one instruction: beat the S&P 500 So far mission accomplished • Up 19.04% since March 3 vs 12.24% for the S&P • $27 million now copying the trades • Every position and every trade public