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If I could change one thing about BASIS, I’d add a proper weekly position summary. I want to see more than just the current DRR. A simple breakdown showing my starting balance, deposits, rewards earned, auto-restakes, withdrawals, and ending balance would make it much easier to understand how the position actually performed over the week. It would also be useful to see the same data over 30, 90, and 365 days, so I can compare periods without manually tracking everything. Another small addition would be a clear “what changed?” section. If my balance or DRR moved, I’d like to quickly see what caused the change instead of having to piece it together myself. Nothing flashy. Just better visibility into the numbers I already care about. That’s the kind of dashboard improvement I’d love to see from @basis__pro.

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Red weeks are usually when the timeline gets loud. Everyone suddenly has a prediction, someone is calling for a bigger crash, and every red candle starts feeling like a reason to change your plan. This time, I paid less attention to the noise and watched what was actually happening with my position on @basis__pro. I wasn't constantly moving things around or making decisions just because the market was down. The whole point of using a market-neutral setup for me was to have something that didn't require reacting to every move in BTC or the wider market. What stood out most was watching the DRR while everything else looked messy. Instead of asking “should I sell?” every few hours, I was paying more attention to whether the strategy was still doing what I expected it to do. A red week didn't suddenly become comfortable, but it definitely changed what I was watching. Sometimes the biggest move is simply not reacting to every candle.

My first deposit into @basis__pro was probably more stressful than it needed to be 😂 Before hitting send, I double-checked the BTC address more times than I want to admit. I copied it, compared the first and last few characters, checked the network, then went back and checked the whole thing again. With crypto, I’d rather spend an extra minute checking than spend days wondering if I sent funds somewhere they shouldn’t be. Once everything matched, I finally sent it. The waiting part was honestly the hardest because you know the transaction is on its way, but you still keep refreshing to see if it has landed. That first deposit taught me that most of the anxiety was really just me being extra careful with the first transfer. After seeing it arrive and start earning, the process felt a lot less intimidating. Next time, I already know what I’m checking before I press send.

The part of AI video creation I’m most interested in isn’t simply making videos faster. It’s removing the gap between having an idea and actually seeing that idea on screen. That’s what Pexo seems to be going after. Instead of spending hours learning a complicated workflow, you can explain your vision, share references, review the result, point to what feels wrong, and tell the agent what you want changed. That interaction feels much closer to working with a real creative collaborator than operating traditional editing software. And having the entire video handled in one workflow, from visuals and motion graphics to music, captions and voiceover, makes the idea even more interesting. We’ve already seen “vibe coding” change how people build software. Now imagine that same feeling applied to filmmaking and content creation. You bring the idea. Pexo helps turn it into something people can actually see. That shift is bigger than just another AI video generator. #Pexo #AIVideoAgent #VibeCreate #MadewithPexo

Now you can create videos the way you vibe-code. Meet Pexo, your AI video agent. Share your vision and references. Work through the details with Pexo like you’re messaging a friend. No complex prompts. No new tools to learn. Want to change something? Mark it on the video and tell Pexo what to change, just like leaving a comment in a doc. Everything in this video was made with Pexo: the visuals, the motion graphics, the music, the captions, and the voiceover. Even our founder @evanLiaoQ appears on screen. You vibe-coded what you built. Now vibe-create how the world sees it. #Pexo #AIVideoAgent #VibeCreate #MadewithPexo

If I had to explain market-neutral yield to someone who is completely new to crypto, I’d say it’s basically about trying to earn from the market without making your whole strategy depend on whether an asset goes up or down. That distinction took me a while to understand because most of the crypto conversations I was used to were about price appreciation. The interesting part is separating the idea of earning yield from simply betting on direction. With a market-neutral approach, the focus shifts more toward the strategy generating the return while managing directional exposure. I think that makes it a useful concept to understand, especially for people who want to explore yield without treating every position like a price prediction. That’s the part of BASIS I’ve been paying more attention to lately. @basis__pro

提前还房贷，最省利息的方案就一定最好吗？ 手里有20万元，准备提前还房贷。银行给出两个方案： 一个是把月供从5,800元降到4,300元；另一个是维持原月供，提前约4年还清。 如果只考虑利息，缩短期限似乎更划算。但对于一个准备生孩子、未来可能经历半年收入下降的家庭，答案可能没那么简单。 我把这个虚拟案例交给了金融增强模型 Ling-3.0-flash-Fin，想看看它会不会只推荐“最省钱”的方案。 它先考虑的不是利息，而是家庭能不能扛住 这个家庭平时每月税后收入18,000元，必要生活支出8,000元；生育期间，家庭月收入可能暂时下降到10,000元。 模型指出，正常收入期间，降低月供的方案每月可以多留下1,500元；收入下降后，两个方案都会出现现金流缺口，但降低月供的缺口相对更小。 因此，它没有直接判断哪个方案“一定更好”，而是给出了条件式建议： 如果家庭更重视生育期间的现金流安全，可以优先考虑降低月供；如果收入稳定、应急储备充足，并且更希望尽早结束负债，则可以考虑缩短期限；如果应急资金不足，甚至可以暂缓提前还款。 它把一道房贷题，变成了家庭选择题 这次回答中，我认为最有价值的一句话是： > 优先保现金流，再权衡利息与期限。 模型还提醒，实际办理前需要确认违约金、最低还款金额、办理周期、还款后的调整方式，以及利率重定价安排。 当然，回答也不是完全没有问题。它把月收入从18,000元降至10,000元描述成“收入减半”，表述并不准确。“方案B需要咬牙坚持”也带有一定主观色彩。 但整体来看，它没有机械地把“少付利息”当成唯一答案，而是理解了家庭收入变化、生活支出和应急资金之间的关系。 这或许也是金融模型更实用的一种方式：不替人作决定，而是帮助使用者发现，自己真正需要解决的究竟是利息问题、负债期限，还是眼前的现金流安全。 Ling-3.0-flash-Fin 目前可通过 OpenRouter 体验： openrouter.ai/inclusionai/li… 本文使用的是虚构案例，仅用于测试模型的金融分析能力，不构成房贷或个人财务建议。实际办理应以贷款合同和银行答复为准。

我把一份真实业绩公告交给金融大模型，结果怎么样？ AI读财报，最值得警惕的情况，不是它完全不会回答，而是十几个数字都对，只在某个关键口径上悄悄出错。 最近，我体验了蚂蚁百灵推出的金融增强模型 Ling-3.0-flash-Fin。为了看看它是否真的能进入金融工作流，我没有让它预测股价，而是给了它一份 Alphabet 2026年第二季度官方业绩公告，让它完成数据提取、变化分析和业绩快评。 它很快抓住了三条主线 模型准确提取了Alphabet本季度的主要数据：营业收入1,197.96亿美元，同比增长24%；营业利润407.70亿美元，同比增长30%；营业利润率提升至34%。 在此基础上，它识别出了三个值得关注的变化。 第一，Google Cloud收入同比增长82%至247.68亿美元，营业利润由28.26亿美元增长至88.14亿美元。模型进一步将其与企业AI解决方案、AI基础设施及核心GCP服务需求联系起来。 第二，归属于普通股股东的净利润增长298%，但模型没有把这一数字简单理解为主营业务爆发，而是发现其中包含约990亿美元的权益证券收益，并提醒这类收益具有较强波动性。 第三，虽然经营活动现金流增长至390.69亿美元，但资本支出翻倍至449.24亿美元，导致季度自由现金流转负。模型由此判断，AI基础设施建设正在明显增加Alphabet的资金投入。 这些分析已经不只是摘录数字，而是在尝试区分主营业务增长、非经营性收益和资本开支压力。 但它依然需要人工复核 测试中也出现了一个很典型的问题。 模型在前文把2025年第二季度自由现金流写成244.61亿美元，但这个数字实际对应2025年第三季度。根据现金流量表计算，2025年第二季度自由现金流应为53.01亿美元。 有意思的是，模型在文末“待人工复核信息”中又给出了正确计算。这意味着它具备发现问题和展示计算过程的能力，但没有消除答案内部的矛盾。 此外，它对Google Cloud营业利润率提升幅度的计算也不准确：从约20.7%升至35.6%，正确变化约为14.9个百分点，而不是文中写的21.2个百分点。 这恰好说明，金融模型输出越完整、表达越专业，人工核验越不能省略。 更像研究助理，而不是决策者 一次测试不足以概括模型的全部能力，但Ling-3.0-flash-Fin已经展现出一个有价值的方向：它可以较快完成财报取数、寻找业绩主线、区分经营与非经营因素，并搭建一份结构完整的研究初稿。 它目前更适合作为金融研究助理，帮助使用者减少资料整理和初稿制作的时间，而不是绕过专业人员，直接给出投资结论。 Ling-3.0-flash-Fin 目前可通过 OpenRouter 免费体验： huggingface.co/inclusionAI/L… 本文仅记录一次指定材料和提示词下的实际体验，不代表模型在所有金融任务中的表现，相关内容不构成任何投资建议

🧵 1/7 🔥 单季交付 10.4 万辆车，雷军造车到底亏不亏？ 最新财报里，「汽车+AI」分部有 47.9 亿毛利，最后却还亏了 26 亿！ 同一季度，小米经调整净利润同比下滑 42.6% 所以，到底是“赢麻了”，还是仍在“倒贴交朋友”？ 我把最新财报交给金融模型 Ling-3.0-Flash-Fin 做第一轮拆解，再回原文逐项复核 答案藏在一笔 74 亿经营开支里 完整见录屏👇

分析师从不明着骗人，但这招你得学会 前两天 $DELL 财报刚出来，整个公司这季营收470亿美元 AI 服务器新订单609 亿美元，收入164 亿美元 backlog 950 亿美元 卧槽，609 亿？ 大家看到这个数字，可能非常兴奋 但分析师根本不需要跟你说 609 亿是收入 他只要把AI 订单 609 亿这个数字放在面前 很多人自己就开始脑补了： DELL 这季 AI 业务不得起飞了？？ 赶快去看看股票涨了几个点 结果再往下看，你会发现完全不是一回事 609 亿是新接的订单，164 亿才是这季确认的收入 950 亿则是手里还没消化完的订单 但你只要少看一行，理解出来的东西就可能完全不一样 刚好最近在用 Ling-3.0-flash-Fin 现在很多人看财报，都是习惯直接丢给 GPT、Claude 我以前也是 但实际用下来我发现，通用模型和专门做金融的模型，处理这种东西还是有区别的 Ling-3.0-flash-Fin 会更愿意去找各个数字之间到底什么关系，再去找原文资料 而不是给我一堆听起来都对、但其实没解决什么问题的总结 投资者真正需要的，就是把那些最容易忽略、看错的地方找出来 所以这次我干脆把DELL整份材料丢给它 609 亿到底是什么，164 亿又是什么，950 亿和前面两个是什么关系，最好都能直接给我找到原文 我更想知道这家公司到底还值不值得继续关注、现在贵不贵、风险在哪 尤其是财报，一但口径理解反了 后面的判断很容易跟着跑偏 这也是我觉得 Ling-3.0-flash-Fin比较适合金融推理的地方 先把材料弄明白，剩下的我自己判断 现在Ling-3.0-flash-Fin 在 OpenRouter 上正好限时免费，有兴趣的可以进去试试 openrouter.ai/chat?models=in… 以上是模型测试，不构成投资建议