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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.

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Most people use Claude like a search engine. I use it like a business partner who knows everything about me. The difference is one file: CLAUDE.md It lives in my vault and tells Claude exactly: → Who I am and how I think → What I'm building right now → My writing style and tone → Mistakes I never want to repeat Every session starts with full context. No re-explaining. No generic answers. No starting over. I wrote it in 2 hours. It's saved me hundreds since. Most people treat AI like a tool. The ones winning treat it like infrastructure. @novak7747 — build the infrastructure. #Claude# #AIProductivity# #SecondBrain#

Sometimes the races that define you aren't the ones that you win. 💔➡️🥇 Sir Mo Farah's Olympic journey is proof that setbacks can become the start of something extraordinary. #Olympics#

Civilizations die from suicide. After spending 3 decades studying 26 civilizations, Toynbee found that civilizations rise when a creative minority actually solves hard problems and earns real loyalty from the people. Then that same minority hardens into a dominant minority that keeps power through force and habit while it stops actually leading. The majority disengages and a schism opens in the body social. External threats only finish what already started from inside. No invading army ever killed a healthy civilization, they only bury the ones that have already stopped believing in themselves.