【Hacker News搬运】How to Start Google

hackernews

Title: How to Start Google

如何启动谷歌

Text:

From: https://news.ycombinator.com/item?id=39756865

Url: https://paulgraham.com/google.html

文章《如何开始Google》由保罗·格雷厄姆撰写，主要讨论了如何开始自己的创业公司，特别是像Google这样的科技公司。文章强调，要成功创办一家公司，你需要具备三种技能：精通某种技术，拥有一个创业想法，以及找到合适的共同创始人。

保罗建议，要精通技术，最好的方法是投身于自己的项目。他鼓励年轻人不要猜测哪种技术会变得最有价值，而是选择自己最感兴趣的技术领域进行深入研究。保罗特别强调，编程是过去30年中创业公司的主要来源，未来10年可能也不会改变。

关于创业想法的获取，保罗认为一旦精通某种技术，你将能够看到世界上缺少的东西，这些缺失的点就是潜在的创业机会。他以Facebook为例，说明马克·扎克伯格是如何通过观察大学中缺少在线社交网络而创立了Facebook。

最后，保罗谈到了共同创始人的重要性。他建议通过共同参与项目来寻找合适的共同创始人，并强调了在好大学学习的重要性，因为那里可以找到最优秀的人才和创意。

总的来说，保罗的建议是，通过投身于自己的项目，精通技术，并在好大学里找到合适的共同创始人，这些都是通往成功创业之路的关键步骤。

Post by: harscoat

Comments:

extheat: At 8x86B, looks like the largest open model yet by far. Would be interesting to hear how many tokens it's been trained on. Especially important for higher param models in order to efficiently utilize all those parameters.

extheat: 8x86B，看起来是迄今为止最大的开放式机型。很有意思的是，听听它有多少代币；s进行了训练。对于更高参数的模型，为了有效地利用所有这些参数，这一点尤为重要。

ilaksh: Has anyone outside of x.ai actually done inference with this model yet? And if so, have they provided details of the hardware? What type of AWS instance or whatever?I think you can rent like an 8 x A100 or 8 x H100 and it's "affordable" to play around with for at least a few minutes. But you would need to know exactly how to set up the GPU cluster.Because I doubt it's as simple as just 'python run.py' to get it going.

ilaksh: 除了x.ai之外，有人真的用这个模型做过推理吗？如果是，他们是否提供了硬件的详细信息？什么类型的AWS实例或其他什么 我认为你可以租一辆8 x A100或8 x H100；s〃；负担得起的“；至少玩几分钟。但您需要确切地知道如何设置GPU集群 因为我对此表示怀疑；s简单到仅为；python运行.py；让它继续下去。

nasir: I'd be very curious to see how it performs especially on inputs that's blocked by other models. Seems like Grok will differentiate itself from other OS models from a cencorship and alignment perspective.

nasir: I-；d非常好奇地看到它是如何执行的；s被其他型号挡住了。看起来Grok将从协调和一致的角度将自己与其他操作系统模型区分开来。

simonw: "Base model trained on a large amount of text data, not fine-tuned for any particular task."Presumably the version they've been previewing on Twitter is an instruction-tuned model which behaves quite differently from these raw weights.

simonw: &quot；基于大量文本数据训练的基础模型，不针对任何特定任务进行微调&quot 据推测，他们的版本；我在推特上预览了一个经过指令调整的模型，它的行为与这些原始权重截然不同。

nylonstrung: For what reason would you want to use this instead of open source alternatives like Mistral

nylonstrung: 你为什么要使用它而不是像Mistral这样的开源替代品

jjcm: I think it's smart to start trying things here. This has infinite flaws with it, but from a business and learnings standpoint it's a step toward the right direction. Over time we're going to both learn and decide what is and isn't important to designate as "AI" - Google's approach here at least breaks this into rules of what "AI" things are important to label:• Makes a real person appear to say or do something they didn't say or do• Alters footage of a real event or place• Generates a realistic-looking scene that didn't actually occurAt the very least this will test each of these hypotheses, which we'll learn from and iterate on. I am curious to see the legal arguments that will inevitably kick up from each of these - is color correction altering footage of a real event or place? They explicitly say it isn't in the wider description, but what about beauty filters? If I have 16 video angles, and use photogrammetry / gaussian splatting / AI to generate a 17th, is that a realistic-looking scene that didn't actually occur? Do I need to have actually captured the photons themselves if I can be 99% sure my predictions of them are accurate?So many flaws, but all early steps have flaws. At least it is a step.

jjcm: 我认为；在这里开始尝试是明智的。这有无限的缺陷，但从商业和学习的角度来看；这是朝着正确方向迈出的一步。随着时间的推移，我们；我们将学习并决定什么是和不是；将其指定为“；AI”-谷歌；这里的方法至少将其分解为“什么”的规则；AI”；事物是重要的标签：＜p＞•让真实的人看起来说或做了他们没有做的事情；t说或做•改变真实事件或地点的镜头？生成逼真的场景；t实际发生至少这将检验这些假设中的每一个；我将从中学习并不断迭代。我很想看看每一个不可避免地会引发的法律争论——颜色校正是否会改变真实事件或地点的镜头？他们明确表示这不是；在更广泛的描述中，但美容滤镜呢？如果我有16个视频角度，并且使用摄影测量；高斯飞溅；人工智能生成的第17个场景，是一个看起来很逼真的场景；实际上没有发生？如果我能99%地确定我对光子的预测是准确的，我是否需要真正捕获光子本身 这么多缺陷，但所有早期步骤都有缺陷。至少这是一个步骤。

summerlight: Looks like there is a huge grea area that they need to figure out in practice. From <a href="https://support.google.com/youtube/answer/14328491#" rel="nofollow">https://support.google.com/youtube/answer/14328491#</a>:Examples of content creators don’t have to disclose:<pre><code> * Someone riding a unicorn through a fantastical world

Green screen used to depict someone floating in space
Color adjustment or lighting filters
Special effects filters, like adding background blur or vintage effects
Production assistance, like using generative AI tools to create or improve a video outline, script, thumbnail, title, or infographic
Caption creation
Video sharpening, upscaling or repair and voice or audio repair
Idea generation
</code></pre>
Examples of content creators need to disclose:<pre><code> * Synthetically generating music (including music generated using Creator Music)
Voice cloning someone else’s voice to use it for voiceover
Synthetically generating extra footage of a real place, like a video of a surfer in Maui for a promotional travel video
Synthetically generating a realistic video of a match between two real professional tennis players
Making it appear as if someone gave advice that they did not actually give
Digitally altering audio to make it sound as if a popular singer missed a note in their live performance
Showing a realistic depiction of a tornado or other weather events moving toward a real city that didn’t actually happen
Making it appear as if hospital workers turned away sick or wounded patients
Depicting a public figure stealing something they did not steal, or admitting to stealing something when they did not make that admission
Making it look like a real person has been arrested or imprisoned</code></pre>

summerlight: 看起来他们需要在练习中找出一个巨大的格雷阿区域。来自<a href=“https://；&#x2F；support.google.com#xx2F；youtube#xx2F！answer&#x2F！14328491#”rel=“nofollow”>https://&#x2F；support.google.com&#x2F；youtube&#x2F；答案&#x2F；14328491#</a>：内容创作者不必披露的例子：<pre><code>有人骑着独角兽穿越奇幻世界绿色屏幕用于描绘漂浮在太空中的人颜色调整或照明过滤器特效滤镜，如添加背景模糊或复古效果制作辅助，如使用生成人工智能工具创建或改进视频大纲、脚本、缩略图、标题或信息图标题创建视频锐化、放大或修复以及语音或音频修复产生想法</code></pre>创作者需要披露的内容示例：<pre><code>综合生成音乐（包括使用创作者音乐生成的音乐）语音克隆他人的语音以用于画外音综合生成真实地方的额外镜头，比如毛伊岛冲浪者的宣传旅游视频综合生成两名真实职业网球运动员比赛的逼真视频让人看起来好像有人给出了他们实际上没有给出的建议对音频进行数字更改，使其听起来像流行歌手在现场表演中错过了一个音符展示龙卷风或其他天气事件向真实城市移动的真实写照，但实际上并没有发生让人觉得医院工作人员拒绝了生病或受伤的病人描述一个公众人物偷了他们没有偷的东西，或者在他们没有承认的情况下承认偷了东西让它看起来像一个真人被逮捕或监禁</code></pre>

the_duke: They don't bother to mention it, but this is actually to comply with the the new EU AI act.> Providers will also have to ensure that AI-generated content is identifiable. Besides, AI-generated text published with the purpose to inform the public on matters of public interest must be labelled as artificially generated. This also applies to audio and video content constituting deep fakes<a href="https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai#:~:text=Providers will also have to,video content constituting deep fakes" rel="nofollow">https://digital-strategy.ec.europa.eu/en/policies/regulatory...</a>.Some discussion here: <a href="https://news.ycombinator.com/item?id=39746669">https://news.ycombinator.com/item?id=39746669</a>

the_duke: 他们不；我不想提，但这实际上是为了遵守新的欧盟人工智能法案 &gt；提供商还必须确保人工智能生成的内容是可识别的。此外，人工智能生成的文本是为了向公众通报公共利益事项而发布的，必须被贴上人为生成的标签。这也适用于构成深度伪造的音频和视频内容<a href=“https://；&#x2F；数字战略.ec.europa.eu&#x2F！en&#x2F，政策&#x2F：监管框架ai#：~：text=提供商%20will%20also%20have%20to，视频%20content%20construction%20deep%20fakes”rel=“nofollow”>https://&#x2F；数字战略.ec.europa.eu；en；策略；监管的一p> 这里的一些讨论：<a href=“https://；&#x2F；news.ycombinator.com&#x2F？id=39746669”>https://&#x2F；news.ycombinator.com&#x2F；项目id=39746669</a>

yoavz: Most interesting example to me: "Digitally altering audio to make it sound as if a popular singer missed a note in their live performance".This seems oddly specific to the inverse of what happened recently with Alicia Keys from the recent Superbowl. As Robert Komaniecki pointed out on X [1], Alicia Keys hit a "sour note" which was silently edited by the NFL to fix it.[1] <a href="https://twitter.com/Komaniecki_R/status/1757074365102084464" rel="nofollow">https://twitter.com/Komaniecki_R/status/1757074365102084464</a>

yoavz: 对我来说最有趣的例子是：；对音频进行数字更改，使其听起来像是流行歌手在现场表演中错过了一个音符” 这似乎与最近超级碗中艾丽西亚·凯斯的遭遇正好相反。正如Robert Komaniecki在X[1]上指出的那样，Alicia Keys打出了一个“；酸味”；NFL对其进行了静默编辑以修复它。[1]<a href=“https://；&#x2F；twitter.com&#x2F：Komaniecki_R&x2F；status&#x2F，1757074365102084464”rel=“nofollow”>https://&#x2F；twitter；Komaniecki_ R；status；1757074365102084464</a>

sigmoid10: >Some examples of content that require disclosure include: [...] Generating realistic scenes: Showing a realistic depiction of fictional major events, like a tornado moving toward a real town.This sounds like every thumbnail on youtube these days. It's good that this is not limited to AI, but it also means this will be a nightmare to police.

sigmoid10: &gt；一些需要披露的内容示例包括：[…]生成真实场景：显示虚构重大事件的真实描述，如龙卷风向真实城镇移动 这听起来像是最近youtube上的每一个缩略图。它；这不仅限于人工智能，这很好，但也意味着这将是警方的噩梦。

thrdbndndn: The emphasis here is Single Image, but can this model generate with multiple images too?We know that a single image of an object physically can't cover all the sides of it, so it's all guesswork in AI. This is totally fine for certain scenario, but in lots of other cases, it's trivial to have multiple images of the same object, and if that offers higher fidelity, it's totally worth it.I'm aware there are many algorithms or AI models that already
do that. I'm asking about Stability's one specifically because if they have impressive Single Image result, surely their multi-image results would also be much better than state-of-the-art?

thrdbndndn: 这里的重点是单图像，但这个模型也能用多个图像生成吗 我们知道，物体的单个图像在物理上可以；t覆盖它的所有侧面，所以它；这都是人工智能中的猜测。这对某些场景来说是完全可以的，但在许多其他情况下；具有同一对象的多个图像是微不足道的，并且如果这提供了更高的保真度；这完全值得；我知道已经有很多算法或人工智能模型这样做。I-；m询问稳定性；特别是因为如果他们有令人印象深刻的单图像结果，那么他们的多图像结果肯定也会比最先进的要好得多？

kouteiheika: Just tried to run this using their sample script on my 4090 (which has 24GB of VRAM). It ran for a little over 1 minute and crashed with an out-of-memory error. I tried both SV3D_u and SV3D_p models.[edit]Managed to generate by tweaking the script to generate less frames simultaneously. 19.5GB peak VRAM usage, 1 min 25 secs to generate at 225 watts.[/edit]

kouteiheika: 只是试着在我的4090（它有24GB的VRAM）上使用他们的示例脚本来运行这个。它运行了1分钟多一点，由于内存不足而崩溃。我尝试了SV3D_u和SV3D_p两种模型 [edit]通过调整脚本来同时生成更少的帧，从而实现了生成。19.5GB峰值VRAM使用量，1分25秒以225瓦的功率产生。[&#x2F；编辑]

nbzso: Billions purred into technology with minimal use case application.
What is the direct implication of this tech?
Porn on demand?

nbzso: 数十亿美元投入到使用最少用例应用程序的技术中。这项技术的直接含义是什么？色情点播？

Filligree: If the animations shown are representative, then the mesh output may very well be good enough to use in a 3d printer.Looking forward to experimenting with this.

Filligree: 如果显示的动画具有代表性，那么网格输出可能非常好，足以在3d打印机中使用 期待着对此进行试验。

ionwake: Im sorry for dumb lazy question. But would the input require more than one image? Is there a demo url to test this? I think it might jsut be time to buy a 3d printer.EDIT> Does "single image inputs" mean more than one image?

ionwake: 我很抱歉问了这个愚蠢而懒惰的问题。但是输入是否需要多个图像？有没有一个演示url来测试这个？我想也许是时候买一台3d打印机了 编辑&gt；是否“；单个图像输入“；意思是不止一张图片？

dsign: I remember when the omission of stack frame pointers started spreading at the beginning of the 2000s. I was in college at the time, studying computer sciences in a very poor third-world country. Our computers were old and far from powerful. So, for most course projects, we would eschew interprets and use compilers. Mind you, what my college lacked in money it compensated by having interesting course work. We studied and implemented low level data-structures, compilers, assembly-code numerical routines and even a device driver for Minix.During my first two years in college, if one of our programs did something funny, I would attach gdb and see what was happening at assembly level. I got used to "walking the stack" manually, though the debugger often helped a lot. Happy times, until all of the sudden, "-fomit-frame-pointer" was all the rage, and stack traces stopped making sense. Just like that, debugging that segfault or illegal instruction became exponentially harder. A short time later, I started using Python for almost everything to avoid broken debugging sessions. So, I lost an order of magnitude or two with "-fomit-frame-pointer". But learning Python served me well for other adventures.

dsign: 我记得2000年代初，堆栈帧指针的省略开始蔓延。当时我正在上大学，在一个非常贫穷的第三世界国家学习计算机科学。我们的电脑很旧，功能也很差。因此，对于大多数课程项目，我们都会避免解释和使用编译器。请注意，我所在的大学缺乏资金，但它通过有趣的课程工作来弥补。我们研究并实现了低级数据结构、编译器、汇编代码数值例程，甚至Minix的设备驱动程序 在我大学的头两年里，如果我们的一个项目做了一些有趣的事情，我会附上gdb，看看组装级别发生了什么。我习惯了“；行走堆栈”；手动操作，尽管调试器通常帮助很大。快乐的时光，直到突然，&quot-格式帧指针“；风靡一时，堆叠痕迹不再有意义。就这样，调试segfault或非法指令变得越来越困难。不久后，我开始在几乎所有的事情上使用Python，以避免调试会话中断。所以，我失去了一两个数量级的“-fomit帧指针”；。但学习Python对其他冒险活动很有帮助。

rwmj: I'm glad he mentioned Fedora because it's been a tiresome battle to keep frame pointers enabled in the whole distribution (eg <a href="https://pagure.io/fesco/issue/3084" rel="nofollow">https://pagure.io/fesco/issue/3084</a>).There's a persistent myth that frame pointers have a huge overhead, because there was a single Python case that had a +10% slow down (now fixed). The actual measured overhead is under 1%, which is far outweighed by the benefits we've been able to make in certain applications.

rwmj: I-；我很高兴他提到Fedora，因为它；在整个分发版中保持启用帧指针是一场令人厌倦的战斗（例如<a href=“https://；&#x2F；pagure.io&#x2F，fesco&#x2F）issue&#x2F！3084”rel=“nofollow”>https://；#xx2F；page.io&#x2F；fesco&#x20F；issue&#x2F！3084</a>） 有；这是一个持续存在的神话，即帧指针有巨大的开销，因为有一个Python案例的速度慢了+10%（现在已经修复）。实际测量的开销低于1%，这远远超过了我们所获得的好处；我能够在某些应用中制作。

ReleaseCandidat: That's one thing Apple did do right on ARM:> The frame pointer register (x29) must always address a valid frame record. Some functions — such as leaf functions or tail calls — may opt not to create an entry in this list. As a result, stack traces are always meaningful, even without debug information.<a href="https://developer.apple.com/documentation/xcode/writing-arm64-code-for-apple-platforms" rel="nofollow">https://developer.apple.com/documentation/xcode/writing-arm6...</a>

ReleaseCandidat: 那个；苹果在ARM上做得很好的一件事：；帧指针寄存器（x29）必须始终寻址有效的帧记录。某些函数（如叶函数或尾部调用）可能会选择不在此列表中创建条目。因此，即使没有调试信息，堆栈跟踪也总是有意义的 <a href=“https://；&#x2F；developer.apple.com&#x20F；文档&#x2F：xcode&#x2F，writing-arm64-code-for-apple-platforms”rel=“nofollow”>https://&#x2F；developer.apple.com&#x2F；文档；xcode&#x2F；正在编写第6条</a>

adsharma: I was at Google in 2005 on the other side of the argument. My view back then was simple:Even if $BIG_COMPANY makes a decision to compile everything with frame pointers, the rest of the community is not. So we'll be stuck fighting an unwinnable argument with a much larger community. Turns out that it was a ~20 year argument.I ended up writing some patches to make libunwind work for gperftools and maintained libunwind for some number of years as a consequence of that work.Having moved on to other areas of computing, I'm now a passive observer. But it's fascinating to read history from the other perspective.

adsharma: 2005年，我在谷歌站在了争论的另一边。我当时的观点很简单：即使$BIG_COMPANY决定用帧指针编译所有内容，社区的其他人也不会。所以我们；我将被困在与一个更大的社区进行一场无法获胜的争论中。事实证明，这是一场长达20年的争论 我最终写了一些补丁，使libunvell为gperftools工作，并因此维护了libunvelle好几年；我现在是一个被动的观察者。但是它；从另一个角度阅读历史很有趣。

titzer: Virgil doesn't use frame pointers. If you don't have dynamic stack allocation, the frame of a given function has a fixed size can be found with a simple (binary-search) table lookup. Virgil's technique uses an additional page-indexed range that further restricts the lookup to be a few comparisons on average (O(log(# retpoints per page)). It combines the unwind info with stackmaps for GC. It takes very little space.The main driver is in (<a href="https://github.com/titzer/virgil/blob/master/rt/native/NativeStackWalker.v3);">https://github.com/titzer/virgil/blob/master/rt/native/Nativ...</a> the rest of the code in the directory implements the decoding of metadata.I think frame pointers only make sense if frames are dynamically-sized (i.e. have stack allocation of data). Otherwise it seems weird to me that a dynamic mechanism is used when a static mechanism would suffice; mostly because no one agreed on an ABI for the metadata encoding, or an unwind routine.I believe the 1-2% measurement number. That's in the same ballpark as pervasive checks for array bounds checks. It's weird that the odd debugging and profiling task gets special pleading for a 1% cost but adding a layer of security gets the finger. Very bizarre priorities.

titzer: Virgil不；t使用帧指针。如果你不；t具有动态堆栈分配，给定函数具有固定大小的帧可以通过简单的（二进制搜索）表查找找到。Virgil；s技术使用了一个额外的页面索引范围，该范围进一步将查找限制为平均几个比较（O（log（每页#个retpoints））。它将展开信息与GC的堆栈映射相结合。它占用的空间很小 主驱动程序位于（<a href=“https://；&#x2F；github.com#xx2F；titzer#xx2F，virgil#xx2F）blob#xx2F、master#xx2F。rt#xx2F！native#xx20F；NativeStackWalker.v3）；”>https://&#x2F；github.com&#x2F；titzer；virgil；blob；master；rt；native&#x2F；Nativ</a> 目录中的其余代码实现元数据的解码 我认为只有当帧是动态大小的（即具有数据的堆栈分配）时，帧指针才有意义。否则，在我看来，当静态机制足够时，却使用动态机制是很奇怪的；主要是因为没有人就元数据编码的ABI或展开例程达成一致 我相信1-2%的测量数字。那个；s与数组边界检查的普适性检查大致相同。它；奇怪的是，奇怪的调试和分析任务得到了1%成本的特殊恳求，但添加一层安全性却受到了指责。非常奇怪的优先级。

codethatwerks: What is the parallel advice for a 40 year old? I guess the programming and tinkering stuff still applies. But university cannot be redone. Sure I can study but I wont have any connection to most young people. I’ll be the old guy.
And this assumes I can afford the loss of income.I assume a lot of start ups are started by older people too.I think for older people an advantage is to solve older people problems. Like how sucky accessing all kinds of “adulting” things are from aged care to dealing with myriad systems with kids schools or any other problems that have inevitably been chucked at you. Some of these “startups” might actually be lobbying/political work for the good that doesn’t make money, some might be startups.Also being older I don’t care about making a unicorn. I see that as an odd goal for a founder of any age but a great goal for an investor.

codethatwerks: 对于一个40岁的人来说，类似的建议是什么？我想编程和修补的东西仍然适用。但大学是不能重建的。我当然可以学习，但我与大多数年轻人没有任何联系。我会是那个老家伙。这是假设我能承受收入的损失 我想很多创业公司也是由老年人创办的 我认为对老年人来说，一个优势是解决老年人的问题。比如，从老年护理到处理儿童学校的无数系统，或者任何其他不可避免地被抛在你头上的问题，获取各种“通奸”的东西是多么的愚蠢。这些“初创公司”中的一些实际上可能正在游说；不赚钱的政治工作，有些可能是初创公司 而且年纪大了，我不在乎做独角兽。我认为这对任何年龄的创始人来说都是一个奇怪的目标，但对投资者来说却是一个伟大的目标。

dirkc: This seems to be the main message. And it's followed with such a sweet lacing of irony in the associated footnote> The empirical evidence is clear on this. If you look at where the largest numbers of successful startups come from, it's pretty much the same as the list of the most selective universities.

dirkc: 这似乎是主要信息。并且它；s在相关的脚注中加入了如此甜蜜的讽刺；这方面的经验证据很清楚。如果你看看成功创业公司的最大数量来自哪里；It’这与最具选择性的大学名单差不多。

YossarianFrPrez: PG is remarkably consistent in his advice; much of his writing is about developing a nose for "what's missing" and having the chops and resources to attempt a solution.Also, I think 'Google' in this instance is more for motivation rather than a literal comparison. He's leaving out the part that Google was founded by graduate CS students a) looking for a thesis, b) into node-link graphs, and c) inspired by academic citation metrics. Would PG advise anyone to go to grad school to learn how to find scientific-discovery-based startup ideas these days?

YossarianFrPrez: PG的建议非常一致；他的大部分作品都是关于培养“嗅觉”的；什么；s丢失”；以及拥有尝试解决方案的能力和资源 此外，我认为；谷歌；在这个例子中，更多的是动机，而不是字面上的比较。他；s省略了谷歌由CS研究生创建的部分：a）寻找论文，b）进入节点链接图，以及c）受到学术引文指标的启发。如今，PG会建议任何人去研究生院学习如何找到基于科学发现的创业想法吗？

red_admiral: Paul Graham is second to no-one in understanding the startup ecosystem, but there's some points here that only tell one side of the story.Before I get to the complaints though - am I the only one with the feeling there would be a huge market niche for a search engine that gave as useful results as Google did in its earlier days? It sometimes feels like half the results for non-tech-related searches these days lead to low-quality AI-generated SEO-optimised fake content.> If you're not sure what technology to get good at, get good at programming.We tried this with unemployed former coal miners in Appalachia. It turns out, the real secret sauce here is "be the kind of person who can get good at programming". I'm with Freddie deBoer here, as he says in his book The Cult of Smart: we need to accept that not everyone has the same intellectual abilities. Once we do that, we can start thinking about how we make a world that works for the half of the population below the median on this dimension.> ... facebook ...The other story I heard about Zuckerberg is that he got his first 1000 users by scraping everyone's profile picture off the university "facebooks", then making a page where you could rate the women as "hot" or "not". I feel like missing this part out gives a rather one-sided picture of the story - especially if there were any young women in the class that PG originally gave this talk to. That's a shame because PG makes a very different point in "Why it's safe for founders to be nice" [1].> (US uni admissions are done badly)I agree with footnote 3 that determination and resourcefulness are important, but you also need to be able to program and reason mathematically if you want to start the next google. There are a lot of incredibly determined and resourceful students on liberal-arts or law degrees who might go far in the world, but they're not the person you want as a technical co-founder.[1] <a href="https://paulgraham.com/safe.html" rel="nofollow">https://paulgraham.com/safe.html</a>

red_admiral: Paul Graham在理解创业生态系统方面是无人能及的，但有；这里的一些观点只说明了事情的一个方面 不过，在我开始抱怨之前，我是不是唯一一个觉得搜索引擎会有一个巨大的市场利基，它能像谷歌早期那样提供有用的结果？现在，非技术相关搜索的一半结果有时会导致低质量的人工智能生成的SEO优化的虚假内容 &gt；如果您；I’我不确定该擅长什么技术，擅长编程 我们在阿巴拉契亚的失业前煤矿工人身上尝试过这种方法。事实证明，这里真正的秘密酱汁是&quot；做那种能擅长编程的人&quot；。I-；正如弗雷迪·德波尔在《聪明的邪教》一书中所说：我们需要接受并非每个人都有相同的智力。一旦我们做到了这一点，我们就可以开始思考如何创造一个在这个维度上为低于中位数的一半人口服务的世界 &gt。。。脸书 我听说的关于扎克伯格的另一个故事是，他通过抓取每个人；“大学外的个人资料图片”；facebooks”；，然后制作一个页面，你可以将这些女性评为“；热的“；或“；不是“；。我觉得错过了这一部分，对故事的描述相当片面，尤其是如果PG最初给这个班上的年轻女性做演讲的话；这很遗憾，因为PG在“；为什么；创始人做好人是安全的&quot；1.p> &gt；（美国大学招生做得很糟糕）我同意脚注3的观点，即决心和足智多谋很重要，但如果你想启动下一个谷歌，你也需要能够进行数学编程和推理。有很多拥有文科或法律学位的学生意志坚定，足智多谋，他们可能会走得更远，但他们；你不是你想要的技术联合创始人 [1]<a href=“https://；&#x2F；paulgraham.com#xx2F；safe.html”rel=“nofollow”>https://&#x2F；paulgraham.com&#x2F；safe.html</a>

necovek: I don't see any mention of being ready to completely pivot from your original, lofty mission ("best search with no ads") to exactly the opposite ("ads are money").Google was a successful, unsustainable search engine before it was a successful business.Facebook was a successful social network waay before it was a successful business.Reddit was... You can see where I am going with this.

necovek: 我不；我看不出有人提到你已经准备好从最初的崇高使命（“没有广告的最佳搜索”）完全转向相反的使命（“广告就是金钱”） 在谷歌成为一家成功的企业之前，它是一个成功的、不可持续的搜索引擎 在Facebook成为一家成功的企业之前，它曾经是一个成功的社交网络 Reddit是。。。你可以看到我拿着这个要去哪里。