lots of folks prepping talks next week (congrats!). Some thoughts from RLing on thousands of hours of engineer- and researcher- focused talks: - AI generated svgs > AI generated imgs. MAXIMUM 4 ai slop images in your slides, I don't care how pretty your mom thinks they are (exception ofc if your talk is ABOUT imagegen) - Be pointy. Better to have 1 message with 5 surprising applications, than 5 messages with no concrete examples. - Put code on screen. Engineers like to see code. Especially if they can nitpick it to death over things that aren't the point. - Don't forget to Entertain. Being actually funny, or good at telling relevant anecdotes, is more important than adding yet another bullet point. - Have a Thesis. every talk gets ONE "if you remember one thing from this talk, it is this" card. 1) SO MANY PEOPLE DON'T USE IT. 2) SO MANY PEOPLE DON'T PLAN FOR IT. you get one. use it well? - Have a Thesis Slide. you've been in that talk where everyone gets their phone out to take a photo of the slide. Because people see 1000x more images than videos, you are much more likely to have a viral talk/slide if you have a single viral slide. You are much more likely to have a viral slide if you TRY and most people do not TRY. Struggling? Collect examples from talks you like and adapt their format to your thing. Your slides have a power law - don't spend 5% of your time each on 20 slides, spend 80% of your time on 1 slide and have the rest build up to that 1 slide. - You might not need slides. live demo in IDE, off the cuff rambles, pull audience member up to roleplay, do call and response with the audience, sing/perform, voice over vibe videos, I have seen it all. higher risk/effort, higher reward when done well. - Be pleasant to listen to. Talks with bad/no visuals still can be listened to. Talks with bad audio are DOA. Be confident, project, remember vocal variation, evoke emotion. - Design the emotional journey. Start strong, end strong, have a peak aha/laugh/thesis moment in the middle. Everything else is buildup. - Data driven talks are underrated. Present pretty charts and surprising, authoritative numbers. Use the stage authority to infer from the data to support the broader thesis. Done well, it will feel like my obvious conclusion from objectively looking at the data you presented, not you feeding the conclusion to me. - How to shill your product/company without feeling salesy: teach me everything I didn't know I should to know about the problems you solve, and then you shall have earned the right to convince me you are the guys to trust to solve it once and for all. - Actively watch a lot of talks. like with anything you both need a lot of reps AND you need to explore to find the styles/role models that you shine in. Passive = no introspection after watching, just mindlessly autoplay next video. Active = trying to articulate why a talk was good/bad after watching. If you want to REALLY hone it, think about how you feel about a famous talk and then TRANSCRIBE the talks and read the words on the printed page, and then compare with a "normal" talk and define rules you will follow for yourself to improve. thanks to @dexhorthy for organizing the AIEWF Speaker prep meetup tonight. we should actually do more of these....
很多人都在准备下周的演讲（恭喜！）。这是 RLing（通过大量实践打磨）了成千上万小时、面向工程师和研究者的演讲后的一些想法：- AI generated svgs 胜过 AI generated imgs。你的 slides 里最多放 4 张 AI slop 图片，我才不管你妈觉得它们有多好看（当然，如果你的演讲本来就是讲 imagegen，那是例外）- 要尖锐、聚焦。与其讲 5 个没有具体例子的观点，不如只讲 1 个核心信息，再配上 5 个让人意外的应用。- 把 code 放到屏幕上。工程师喜欢看 code。尤其是当他们可以对着那些不是重点的细节吹毛求疵、挑刺到死的时候。- 别忘了娱乐性。真的有趣，或者很会讲相关的轶事，比再多加一个 bullet point 更重要。- 要有 Thesis。每个 talk 都该有且只有一张“如果你只记住这场演讲的一件事，那就是这个”的卡片。1）太多人根本不用它。2）太多人根本没为它做设计。你就这一次机会。好好用，行吗？- 要有一张 Thesis Slide。你肯定经历过那种演讲：大家纷纷掏出手机拍某一页 slide。因为人们看到的图片比视频多 1000 倍，所以如果你有一张能传播开的单页 slide，你的 talk/slide 更可能火。只要你去尝试，你做出 viral slide 的概率就会高很多，而大多数人根本没在尝试。没思路？去收集你喜欢的演讲里的例子，借鉴它们的格式，改成适合你内容的版本。你的 slides 服从 power law（幂律分布）——不要把时间平均分成 20 张 slide、每张只花 5%；把 80% 的时间砸在 1 张 slide 上，其余内容都为那 1 张做铺垫。- 你可能根本不需要 slides。直接在 IDE 里 live demo、即兴漫谈、拉一位观众上来 roleplay、和观众 call and response、唱歌/表演、给氛围感视频配 voice over，我什么都见过。风险和投入更高，但做好了回报也更高。- 要让人听着舒服。视觉做得差甚至没有 visuals 的 talk，至少还能听；音频差的 talk 则是 DOA（到场即死）。要自信，声音打出去，记得做 vocal variation（语调变化），调动情绪。- 设计情绪曲线。开头强，结尾强，中间要有一个 aha/笑点/Thesis 的峰值时刻。其他一切都是铺垫。- data driven talks 被低估了。展示漂亮的图表和令人意外、又有权威感的数据。利用舞台赋予你的权威，从数据中推出结论，以支撑更大的 Thesis。做好了，听众会觉得“这是我从你展示的客观数据中自然得出的显然结论”，而不是你把结论硬塞给我。- 怎么推销你的 product/company 又不显得 salesy：先把那些我原本不知道、但其实应该知道的、关于你所解决问题的一切都教给我；这样你才算赢得了说服我的资格，让我相信你们就是那个值得信任、能一劳永逸解决问题的团队。- 主动地去看很多 talks。和任何事情一样，你既需要大量 reps（重复练习），也需要广泛探索，找到最适合你发光的风格和 role model。Passive = 看完没有复盘，只是无脑自动播放下一个视频。Active = 看完后尝试说清楚一场 talk 为什么好/不好。如果你真的想把这件事磨到极致，就先想想你对一个著名演讲的感受，然后把它 TRANSCRIBE（转写）出来，读印在纸上的文字；再和一场“普通”演讲对比，最后给自己制定一套改进时会遵守的规则。感谢 @dexhorthy 今晚组织 AIEWF Speaker prep meetup。我们确实应该多办这种活动……
we are going to have to Rebuild So. Much. Infra. for the age of Software Factories
在 Software Factories 时代，我们将不得不重建太、太、多的基础设施（Infra）。
LOTS of alpha in this pod: - Why Databricks beat Snowflake (! a straight answer!) - Why everyone is building a metaharness now - Why the @neondatabase made so much sense (so much @nikitabase glazing its not even funny) - How LTAP solves the HTAP dream I discussed with @ankrgyl in our @braintrust pod - What happened to @MosaicML + DBRX - How to maintain research/startup culture in a $175B megacorp - What's more important knowledge/experience in the race to the agent cloud: databases, operating systems, or.... networking! very honored to be invited to @Data_AI_Summit to interview two of the top people in our industry and somehow be able to jam on everything from the @bennstancil modern data stack theme to @alighodsi's amazing keynote aura
这期 pod 里有很多 alpha（高价值信息）：- 为什么 Databricks 打赢了 Snowflake（！一个直截了当的答案！）- 为什么现在人人都在做 metaharness - 为什么 @neondatabase 如此说得通（对 @nikitabase 的夸赞多到都不好笑了）- LTAP 如何解决我在和 @ankrgyl 做 @braintrust pod 时讨论过的 HTAP 梦想 - @MosaicML + DBRX 后来到底发生了什么 - 在一家 1750 亿美元的超级巨头公司里，如何维持 research/startup culture - 在通往 agent cloud 的竞赛中，knowledge/experience 之外更重要的是什么：databases、operating systems，还是…… networking！非常荣幸受邀来到 @Data_AI_Summit，采访我们行业里两位最顶尖的人物，并且居然能一路畅聊，从 @bennstancil 的 modern data stack 主题，一直聊到 @alighodsi 那场气场惊人的 keynote。