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我做 App 已经两年了，前几天统计了一下，收入刚好超过 50 万 虽然这个数字并不多，但是我觉得这样的生活是非常轻松的，因为我的工作时间很少，大部分收入都是被动收入，而且远超过我的想象：原来我不靠上班也能挣到钱 为什么从来没想象过呢？因为做自由职业完全不在我的人生规划之中。 我是在23年研究生毕业，然后毕业就去到特斯拉工作。结果工作没满一年就遇到大裁员，给了我N+3，我拿着十几万块钱，在上海偏僻的出租屋里面不知所措 在遇到裁员之前，我的人生规划是毕业去大厂上班，干几年跳到另一个大厂，再干几年再跳到另一个大厂，然后35岁差不多拿着一点现金存款，该干嘛干嘛去。 结果我刚毕业一年，上天就帮我提前跳到最后一步了，确实拿着现金存款，但确实只有一点，而且我也想不到该干嘛 我喜欢看国外一些独立开发和自由职业的内容，然后看了几本创业的书，比如《低成本创业》《精益创业》《小而美》，还有 marc lou、levelsio 这些独立开发者的故事。 我就想我要不要尝试一下做产品，能不能养活自己？反正手里还有一些钱，大不了花完再重新找工作。（其实选择了这条路，后面很难再回职场了） 然后我就开始做 APP 了，大概做了半年开始有起色，其实还是吃过很多苦的，尤其是没有收入那段时间，每天都在想要不要去找个班上 😂 经验分享，我觉得大概这几点： 1️⃣ 多做市场 2️⃣ 多花时间设计 onboarding、付费墙 3️⃣ 不停的发帖，在小红书、抖音、TikTok、thread，任何你能想到的社区宣传你的产品 其实我做的产品没有什么亮点，就是上面这几点做的比较好。如果你关心是什么产品的话，我贴在了评论区里。 最后还是感慨一下，我们当下处于的时代是人类历史上对创作者来说最好的时代 1️⃣在 AI 的帮助下，创作门槛极低 2️⃣自媒体作为传播杠杆，传播成本非常低 3️⃣市场庞大到无法想象 人生真的很奇妙啊

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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.