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没用 VPN，没积分，也没买会员。 有人靠 Claude，把 479 欧元的机票找到了 159 欧元的低价！ 直接省下 320 欧元。 这 8 条找低价机票的提示词，经常出国旅行的真该收藏。👇🧵

卧槽，逆向圈这下是真炸了！！ GitHub 上那个坐拥 2.2 万星的 Craft Anything，只要丢给它一款软件，它立马就能给你复刻出另一个来。 站在 REA 的肩膀上，它把逆向整成了一条流水线：取证、写代码、验收，整个流程自动闭环。 Study：内置 320 个工具，看页面、扒代码、拆文件、跑程序，先把目标软件摸个透，每个结论都附带证据。 Build：调度 Claude Code 这类编程智能体，按你指定的语言和框架，从零起一个新项目。 Check：把成品和原版逐项比对，网页按三种屏幕宽度比相似度，游戏跑自检，不达标就自动迭代修补。 和 REA 比：REA 偏向调 IDA、Ghidra 做深度逆向调查，Craft 则把逆向、复刻、验收打包成了端到端的产品。 默认是净室模式，不读原版的代码和文件，只看它的表现来复刻；深度模式才会全量动用工具，仅限自己用。 320 个工具每个都是命令行加 MCP，任何智能体都能直接调。Apache 2.0 开源，纯 Rust 写的。 以后想要什么工具，可以直接照着做一个自己用了。 🔗

卧槽，逆向圈杀疯了！！ Github 上 2.2 万星的 Craft Anything，丢给它一个软件，直接能给你复刻一个。 站在 REA 的肩膀上，它把逆向直接做成了一条龙：取证、写代码、验收，全自动闭环。 1. Study：内置 320 个工具，看页面、扒代码、拆文件、跑程序，先把目标软件扒个底朝天，每个结论都附证据。 2. Build：调 Claude Code 这类编程智能体，按你要的语言和框架，从零写一个新项目。 3. Check：拿成品和原版逐项比对，网页按三种屏幕宽度对比相似度，游戏跑自检，不达标就自动迭代修。 和 REA 比：REA 偏向调 IDA、Ghidra 做深度逆向调查，Craft 则把逆向、复刻、验收打包成了端到端的产品。 默认是净室模式，不读原版的代码和文件，只看它的表现来复刻；深度模式才会全量动用工具，仅限自己用。 320 个工具每个都是命令行加 MCP，任何智能体都能直接调。Apache 2.0 开源，纯 Rust 写的。 以后想要什么工具，可以直接照着做一个自己用了。

HOLY F*CK, I GAVE MY TERMINAL A WEB CRAWLER AND IT CLOSED THE NIGHT AT $8,980 started this run with $63 three nights later im reading the logs twice built the terminal with GPT-6.1 and connected it to Dots to check what the crawler finds a crawler goes through websites, project updates and posts so i dont have to keep refreshing everything myself Dots passes the findings between the research roles GPT-6.1 works through the evidence and objections entries still need my approval heres how the third night went: → 22:10 crawler brought back 76 fresh launches → 22:35 SCOUT kept three with sources worth checking → 23:05 SKEPTIC caught recycled announcements on two, both rejected → 23:40 the remaining token started dumping, QUANT recalculated the entry → 00:15 RISK cleared the revised size, i approved the trade and set exit orders → 02:20 position closed, +$4,320 → 03:10 another alert woke me up, approved a second entry → 05:05 second position closed, +$2,135 one smaller attempt between those trades stopped out for -$285 third night opened at $2,810 finished at $8,980 $63 → $740 → $2,810 → $8,980 went back to the rejected tokens before checking anything else both were still being shilled while the crawler had already found the old announcements my article below covers the Dots team, role prompts and how the research gets passed around save this for ur next terminal build which would u check first, the winning trades or everything it refused?

兄弟们，我让 Grok Bot 在 15 分钟里整了三个交易机器人，第二天一醒，账户多了 2353 刀。当天我就去提离职了。 我就写了一段提示，回它几个问题，剩下全自动。搭框架、写回测、跑历史、设风控、自动部署，一气呵成。 三个机器人，三个账户，三套不同策略。MACD 抓动能，RSI 加 VWAP 做反转，CVD 背离跟着钱走。 我给这套系统起了个名字：Cabal。 20 刀起步，到 70，到 320，最后一天干到 2353。 现在回头看，当初在 FOMO 上反复盯盘、手动追涨杀跌那种焦虑感，全没了。FOMO 教会我的是情绪成本，而 Cabal 帮我彻底卸掉了它。 顶级量化机构早这么干了。机器人没恐惧、没贪婪、没 FOMO，只有算法。 fomo 链接

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis:

币圈90后一共错过多少次财务自由机会？ 比特币早期矿工用一台普通电脑参与挖矿，最多挖出记录5.5万枚BTC，当前价值约328亿元。 以太坊早期众筹，每枚投入约0.31美元；每投入1万元，当前价值约6.2亿元。 BNB早期发行，每枚投入约0.15美元；每投入1万元，当前价值约3.6亿元。 Uniswap早期用户只需完成一次链上交易，获得400枚UNI，当前价值约2.5万元。 BAYC早期铸造，每枚投入0.08枚ETH；当前地板价约6.2枚ETH，单枚价值约12万元。 ENS早期用户花几十美元注册一个.eth域名，公开报道的单地址空投当前价值约1.2万元。 Optimism早期用户投入几十美元完成跨链和交易；单地址最多获得27534枚OP，当前价值约2.6万元。 Arbitrum早期用户投入几十美元完成链上交互，单地址最多获得10250枚ARB，当前价值约1.5万元。 Blur早期用户参与NFT挂单和交易；首季单地址最多获得320万枚BLUR，当前价值约46万元。 Jito早期用户用约40美元的JitoSOL参与链上交互；公开案例空投当前价值约0.8万元。 Hyperliquid早期用户参与测试网和主网交易，公开大户获得约50.9万枚HYPE，当前价值约3.1亿元。

Bitwise 昨天在纽交所 Arca 推出了美国首只 NEAR 现货 ETF（代码：NRR）。 而 $NEAR 自 8 月底以来从 1.6 美元一路拉升至 5 美元上方、接近三倍的涨幅。 按照惯性，部分人的第一反应是：传统资金要进场扫货了，这是个可接追高的重磅利好。 但如果拆解这只 ETF 的实际结构和 NEAR 过去两个月的上涨逻辑，你会发现无脑 FOMO 的话极容易成为Sell the News）”的接盘方。 NRR 的落地确实打通了传统美股账户购买 NEAR 的合规渠道，并且它的结构是“现货持仓+质押”。 但通道的建立并不代表机构资金已经在排队买入。 Bitwise 为这只基金设定了 0.75% 的管理费，并计划将底层代币进行质押（当前链上收益率约 5%）。 值得注意的是，Bitwise 会抽走 33% 的质押收益来覆盖节点和托管成本，剩余的 67% 才会回馈给 ETF 份额净值。 这意味着，在看到确切的净申购数据和持仓增量之前，ETF 只是一个中性偏正的结构变化。8 月至 9 月的这波三倍涨幅，实际上已经提前透支了大部分的“过会预期”。 此外，Bitwise 在其招股书和宣传中，并没有把 NEAR 包装成单纯的“以太坊杀手”，而是将其定义为“AI 代理的结算层”。 这才是支撑 NEAR 近期估值重塑的核心基本面。 随着 NEAR Intents（跨链意图协议）的上线，其历史累计交易量已经突破 320 亿美元。市场的买盘逻辑在于： 未来的 AI 代理需要自己付款、换汇、下单，它们需要一个速度快、成本低且不绑定单一托管方的跨链清算网络，而 NEAR 正在抢占这个叙事生态位。 再说当前比较火的回购叙事。 伴随 Intents 的放量，NEAR 的代币经济学也完成了一次重构。协议年通胀上限从 5% 削减至 2.5%，并且自明年起，Intents 的手续费收入将用于在公开市场回购 NEAR。 但这套机制目前更多是“叙事锚点”，而非现实的买盘支撑。按目前约 2000 万美元量级的年化协议净收入计算，它依然无法覆盖 2.5% 通胀带来的每年约 1.5 亿美元的新增发行抛压。 价格涨得越高，实现通缩的美元门槛就越高。如果后续 Intents 的日均成交量无法维持，这套回购叙事会面临证伪风险。 小编个人认为，在短期随着之前价格爆拉和利好消息的兑现，盲目追高面临着极大的获利回吐风险。 而中长期老币新炒，叙事更新的整体玩法没变，依然看好。

全球七大顶级AV女优的——封神登顶之作 （记得找个小房间，调低手机音量） 1、濑户环奈｜SNOS-313、SNOS-334 2、逢泽美优｜IENFA-45901、RKI-757 3、松本一香｜MIDA-771、TTJK-169 4、美园和花｜SUN-067A、HHKL-256 5、河北彩花｜SNOS-371、SNOS-320 6、北冈果林｜NACT-178、BBAN-604 7、柏木小夏｜HUNTC-489、ATID-655 如果人生一生只能看一部， 我一定是在这里面选。 我发誓：绝不吹牛