Claude AI recovers an 11 yrs old BTC wallet holding 400k USD
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一位比特币交易者在丢失钱包访问权限 11 年后，在 Anthropic 的 Claude AI 帮助下，成功找回了约 40 万美元的加密货币。这位在 X 上名为 cprkrn 的用户在"吸食大麻"后更改了钱包密码，随后忘记了密码，导致 5 个 BTC 无法访问。十多年来多次尝试破解均告失败后，用户将整个大学时期的电脑文件上传给 Claude，作为最后一搏。
突破来自 Claude 在这些数据中发现的一份 2019 年 12 月的旧备份钱包文件。 AI 还识别出密码配置中的一个关键错误，该错误一直阻碍着使用开源比特币钱包恢复工具 btcrecover 的尝试。在修复该问题并找到早于密码更改的旧钱包文件后，Claude 成功运行 btcrecover 解密出私钥，使用户能够将这 5 个"丢失"的 BTC 转回当前钱包。
这一事件既展示了 AI 在加密货币恢复中的潜力，也提醒了其局限：Claude 并非凭空猜出密码，而是解决了一个长期阻碍恢复的技术问题。该故事也与其他著名的加密货币丢失案例形成对比，例如有人因法院裁定不允许他搜寻被丢弃笔记本的 Welsh landfill 而失去了价值 7.8 亿美元的比特币。此次成功找回还强调了妥善备份的重要性以及加密货币钱包安全性多年来的演变。
A Bitcoin trader has recovered $400,000 worth of cryptocurrency after losing access to their wallet for 11 years, thanks to the help of Anthropic's Claude AI. The user, known as cprkrn on X, had changed their wallet password while "stoned" and subsequently forgot it, leaving 5 BTC inaccessible. After more than a decade of failed attempts to crack the password, the user turned to Claude as a last resort, uploading their entire college computer files to the AI.
The breakthrough came when Claude discovered an older backup wallet file from December 2019 hidden within the user's data. The AI also identified a critical bug in the password configuration that had been preventing recovery efforts using btcrecover, an open-source Bitcoin wallet recovery tool. With this issue resolved and access to the older wallet file predating the password change, Claude successfully ran btcrecover to decrypt the private keys, allowing the user to transfer the five "lost" BTC to their current wallet.
The recovery highlights both the potential and limitations of AI in cryptocurrency recovery. While Claude didn't magically guess the password, it solved a technical problem that had stymied the user for over a decade. The story stands in contrast to other notable cases of lost cryptocurrency, such as the man who lost $780 million in Bitcoin after a court ruling prevented him from searching a Welsh landfill where his laptop containing 8,000 BTC had been discarded. The successful recovery also underscores the importance of proper backup practices and the evolving nature of cryptocurrency wallet security over the years.
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一位开发者在审计公司最初拒绝其申请后，借助 Claude 成功识别出 8,000 美元的研发税收抵免；Claude 引用了正确的 IRS 法规，并通过优化 AWS 成本每月节省 250 美元，从而使 AI 订阅在经济上变得划算。
模型正在迅速改进，目前参数量在 100–200 亿的模型，其表现已经超越了据称拥有 1.8 万亿参数的 GPT-4（2023 年版本），这表明大多数用户很快就能从可负担的本地运行模型中获得足够能力，而无需依赖昂贵的订阅服务。
美国税制常被批评为被刻意复杂化，会惩罚那些无力请专业人士的人；对于被迫雇佣昂贵外部公司来做分析的小公司来说，研发税收抵免的流程尤其繁重。
Claude Code 在数据恢复任务中也证明了其价值，例如从损坏的 SD 卡中提取自定义格式的图片，以及从崩溃的浏览器中提取内部数据，挽救了数小时的工作成果。
一位顾问使用 Claude 来理解一个没有源代码管理、没有测试、包含数十个命名混乱文件夹的遗留 Windows 应用，这在理解代码库方面节省了数天的工作量。
关于比特币钱包恢复的报道得到了澄清：Claude 并未破解加密或通过暴力破解密码，而是找到了一个主人没有想到要搜索的备份钱包文件，这更多是人类疏忽而非 AI 的超常能力。
人们对 AI 伦理和访问权限表达了担忧，随着模型越来越多地拒绝取证类任务，如何表述请求变得至关重要以避免触发安全限制，于是本地模型在无需限制的场景中变得越来越有价值。
钱包恢复涉及一个密钥派生函数，多年前暴力破解的计算成本非常高，但随着摩尔定律的发展，成本下降，使得在现代硬件上此类攻击变得可行，甚至较小的本地 AI 模型也能参与其中。
几位评论者分享了丢失比特币财富的经历，包括挖矿收益不抵电费、丢弃含有钱包的硬盘，或在每枚 80 美元时卖掉，总体共识是很多人无论如何都会提前套现。
专业数据恢复公司仍凭借昂贵的硬件工具和"雇佣我们不会被解雇"的服务保障保持优势，尽管 AI 让一些恢复技术对普通开发者更易获取。
讨论揭示了实际应用 AI 所带来的显著财务回报模式，从税收优化到成本节省，通常能够多次覆盖订阅费用。人们普遍认为模型效率在快速提升，较小的模型正逐渐接近更大前代模型的能力，这可能会使访问更为民主化。比特币钱包恢复的故事引发了最多争论，很多人澄清 AI 的贡献主要是系统性地搜寻文件，而非在密码学上取得突破，这突出了 AI 成功往往依赖人类与 AI 的协作，而非纯粹的 AI 能力。关于 AI 限制和请求表述日益重要的伦理担忧成为次要话题，用户指出在某些合法但敏感的任务中，本地模型可能变得必要。 • A developer used Claude to identify $8,000 in R&D tax credits after an auditing firm initially denied their claim, with Claude citing the correct IRS code, and also saved $250/month by optimizing AWS costs, making the AI subscription financially worthwhile.
• Models are rapidly improving, with 10-20B parameter models now outperforming GPT-4's alleged 1.8T parameters from 2023, suggesting that most users will soon get sufficient capability from affordable, locally-run models without needing expensive subscriptions.
• The US tax system is criticized for being deliberately complex and punitive toward those who can't afford professional help, with the R&D tax credit process particularly burdensome for small companies forced to hire expensive outside firms for analysis.
• Claude Code proved invaluable for data recovery tasks, including extracting images from corrupted SD cards with custom file formats and rescuing hours of work from a frozen browser by extracting text from browser internals.
• A consultant used Claude to make sense of a legacy Windows application with no source control, no tests, and dozens of confusingly named folders, saving days of work in understanding the codebase.
• The Bitcoin wallet recovery story is clarified: Claude didn't crack encryption or brute-force passwords, but simply found a backup wallet file the owner hadn't thought to search for, making it more a story of human oversight than AI brilliance.
• There's concern about AI ethics and access, as models increasingly refuse forensic tasks, with the framing of requests becoming crucial to avoid triggering safety restrictions, and local models becoming more valuable for unrestricted work.
• The wallet recovery involved a key derivation function that made brute-forcing computationally expensive years ago, but Moore's Law has since reduced the cost, making such attacks feasible with modern hardware even for smaller local AI models.
• Several commenters share stories of lost Bitcoin wealth, including mining coins that weren't worth the electricity, throwing away hard drives with wallets, or selling at $80/BTC, with the consensus being that most people would have cashed out early regardless.
• Professional data recovery companies still maintain advantages through expensive hardware tools and the "nobody got fired for hiring us" factor, even as AI makes some recovery techniques more accessible to average developers.
The discussion reveals a pattern of practical AI applications delivering significant financial returns, from tax optimization to cost savings, often paying for subscriptions manyfold. There's strong consensus that model efficiency is improving rapidly, with smaller models approaching the capability of much larger predecessors, which will likely democratize access. The Bitcoin wallet recovery story generated the most debate, with many clarifying that the AI's contribution was systematic file searching rather than cryptographic breakthroughs, highlighting how AI success stories often involve human-AI collaboration rather than pure AI capability. Ethical concerns about AI restrictions and the increasing importance of request framing emerged as a secondary theme, with users noting that local models may become necessary for certain legitimate but sensitive tasks.