Panda Detour

learner of languages🇨🇳🇬🇧🇯🇵🇩🇪🇪🇸🇮🇩🇷🇺🇫🇷🇹🇷🇰🇪🇪🇬🇮🇳🇭🇰 & traveler & YouTuber

[YokerAI @IATheYoker]

Durante 3 años defendí ChatGPT.

Hoy uso Claude.

Y voy a decir algo que hace 6 meses no habría dicho:

Es la tecnología que más ha cambiado mi vida desde Internet.

Estos 10 prompts explican por qué cambié 👇

[GREG ISENBERG @gregisenberg]

I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.

My observations:

1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.

2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.

3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.

4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.

5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.

6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.

They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.

7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.

8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.

Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.

9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.

10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!

11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.

12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.

13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.

14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.

15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.

16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.

17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?

18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.

19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.

20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.

21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.

Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.

Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.

Happy to be home and locked in.

Until next time!

[Pato Bonato @patobonato]

Una de las formas más fáciles de perder un año es pasar 12 meses buscando la oportunidad perfecta para ganar dinero online 👨🏻‍💻

El problema no es que existan pocas oportunidades.

Es que existen tantas que mucha gente nunca elige ninguna.

Mi regla:
Primero, descartá lo incompatible con tu realidad.

Después, elegí una alternativa.

Finalmente, probala y medí los resultados.

No necesitás otras 100 ideas.

Necesitás convertir una decisión en una experiencia real

[Marco | IA @Marco_Exito]

ÚLTIMA HORA: GOOGLE GEMINI AHORA PUEDE ANALIZAR CUALQUIER ACCIÓN COMO UN ANALISTA DE WALL STREET (GRATIS).

AQUÍ TIENES 9 INCREÍBLES PROMPTS DE GEMINI QUE REEMPLAZAN LAS TERMINALES DE BLOOMBERG DE $4,000 AL MES:

GUÁRDALO EN FAVORITO📂

[阿蔺A-Lin @alin_zone]

🛑 快停下！如果你花 5 块钱买完 Gemini 会员只是注册了 Muse 就把它扔一边了，那你真的亏麻了

前两天很多朋友为了注册 Muse，去闲鱼花了 5 块钱买下 18 个月的 Gemini 会员，搞完就把它当一次性工具扔在一边，简直暴殄天物 🤦‍♂️

如果你手里已经有号，或者还没上车（闲鱼直接搜🔍 Gemini 依然 5 块钱拿下），听劝，这 5 块钱其实让你直接买断了 Google 顶配全家桶！

我直接帮你盘点下你手里的 Gemini 的 7 大逆天权益：
☁️ 5TB 超大云盘：Drive/相册/Gmail 空间自由，支持全家共享，大素材随手扔。
🧠 Gemini 3.1 Pro + 100万 Token 上下文：告别切片，几十份研报、整个代码工程直接打包喂给它。
🔍 Deep Research (深度研究)：甩个选题，十几分钟自动爬网写出带引用的万字长报告。
🤖 Gemini Spark (云端 Agent)：能 24 小时在云端帮你跑定时任务、查资料。
📚 满血版 NotebookLM：私人知识库神器，生啃不动的资料一键转成“双人对谈播客”。
🎬 Google Flow 每月 1000 积分：官方 AI 视频大杀器，影视级分镜、短片直接包圆。
👨‍💻 Jules (代码助理)：直接连上 GitHub 帮你自动修 bug、提 PR，你只管审核。

💡 最离谱的是，以上仅仅是冰山一角，Google 全家桶里还有超多隐藏玩法和神级权益等着你去开发！

用一杯蜜雪冰城的钱，撬动全网最强 AI 基础设施，赶紧把它的算力榨干 😁！

[Nico Garcia | Ingeniero de IA @NicoGarcia_IA]

🚨 OJO: APRENDE UN IDIOMA EN SEMANAS

7 prompts que transforman a ChatGPT en un tutor perfecto

Guárdalos 👇

[Fran Pradas | Ingeniero de IA @franpradasAI]

🔴 CLAUDE TE LLEVA YOUTUBE GRATIS.

Es tu guionista, estratega y experto.

Monta tu canal desde cero con estos 7 prompts: ↓↓

[Marc Lou @marclou]

Being fit is a skill.
Happiness is a skill.
Getting rich is a skill.

Stop calling it luck. Everything can be learned.

[𝑽𝒂𝒍𝒆𝒓𝑰𝑨 @ValeriA_Tech]

Tu iPhone puede traducir una llamada en tiempo real, mientras la otra persona te habla en un idioma que no conoces.

Lleva un año ahí y casi nadie la usa.

Guarda este hilo. Te explico cómo activarla.

[Ian (伊恩) @ianneo_ai]

蹲一波大家的个人站

不管是用 AI 搓的、静态博客还是小工具，只要你自己觉得好玩、有审美，可以甩出来。看对眼的顺手转一波，互相串个门

[draven @dravenip]

ok, this one made me rethink a couple of habits.

the person who built Claude Code spent an hour and a half walking through how they use agents at Anthropic, and some of it goes completely against what most people do

the parts that surprised me:

→ he uses the best model even for small things, because the cheap one burns more tokens getting it wrong
→ most tasks start in plan mode, so no code until the approach makes sense
→ his agents get a goal and tools, not a step-by-step script
→ on his team even finance writes code now

the model one is the one I can’t stop thinking about. everyone I know switches to the cheaper model to save money, and he’s saying that’s the expensive choice

what I want to understand is where the line is. does that hold for quick edits, or only for bigger tasks? and how much of the 5 agents / 30 PRs a day comes from the setup rather than the model?

not saying everyone should work like this tomorrow, but it’s rare to see the people who build the tool show exactly how they use it

saving this for the weekend 👇

[isaac @theisaacmed]

Increasingly becoming convinced the only value left will be in the 5% ai can’t do for a few decades well (at least).

1. Physical.
2. Strategy and very novel ideas.
3. Complex compliance moats.
4. Brands.
5. Community.
6. Authority and trust.
7. Community and events.

Dollars will shift around accordingly.

[Kieran Drew @ItsKieranDrew]

You are not creating content. You are building trust.

Write accordingly.

[逸尘 @gengdaJ]

如果你有Codex和Claude Code，但是你不知道Monid（Agent的手和脚）。

就等同于你买了车子，但一直没给它加油。

[Elvis @ecraig91]

自己也在出海这条路上折腾了很久，从跨境电商到独立站，再到 AI 工具站、SEO 和 Affiliate，各种方向都尝试过。

最大的感受就是：现在出海的门槛确实越来越低，但真正赚到钱，没想象中那么容易。

我自己坚持了一年，目前已经实现了月入万刀。

回头看，其实没有什么特别的秘诀，就是不断尝试、踩坑、复盘，再继续尝试。

尤其是刚开始做出海项目，正向反馈真的很慢。可能折腾一两个月，网站没什么流量，也赚不到多少钱，很容易怀疑方向是不是错了。

但很多时候，不是项目完全没机会，而是还需要时间去验证和积累。

出海最重要的不是一开始就选对方向，而是敢于多尝试，并且在没有正向反馈的时候还能坚持下去。

别想着一上来就赚大钱，先跑通第一单，再想办法复制和放大。

坚持做，持续试，机会总会比原地观望多一些。

[工具兔🐰 @tool_hopper]

🚨突发：Gemini现在能修图到让人以为你雇了专业摄影师的程度。

你的脸部和身份保持不变。

这是5个提示词：👇

[Miles Ma @miles_mazy]

哥飞大佬的话，我是信的

[JasonZ @Jason_WealthAI]

未来十年，信息早就过载了。
普通人不需要学更多技能，死磕这5件事就够了：

1. 学会用AI干活
别去卷底层的死力气。把AI当成你的24小时外包团队，一个人就是一家公司。

2. 打造自己的个人IP
无论做跨境、出海还是做内容，核心都是“学会卖东西”。把你的认知和技能产品化，让流量和客户主动找你。

3. 投资全球硬资产，远离短线赌博
别迷信死存钱，长期抱紧标普、纳指和核心硬通货。赚慢钱，吃复利，靠资产的被动升值跑赢通胀。

4. 经营好身体和精力
少点无效社交，戒糖、睡好、保持情绪稳定。未来的竞争是拼长跑，身体垮了，赚多少都是给医院打工。

5. 建立多被动收入，告别假稳定
铁饭碗早已不存在。尽早搭建第二、第三收入渠道，有持续的现金流，你才拥有随时掀桌子的底气。

未来十年，不是比谁懂得杂，而是比谁做得精。
抓住这5条底层逻辑，把时间花在有复利的事情上，

你就已经赢过了90%的人。

[Hux @huxlab]

谷歌首席执行官桑达尔·皮查伊：

“如果你现在不学会如何编排Agent，

你将在2027年花费时间追赶那些今天就开始的人。”

他用30分钟详细解释了为什么最好的工程师不再手写代码，转而开始编排Agent。

大多数人认为从0到1构建一个Agent需要工程学位。

实际并非如此。

只需要一份指南和一个下午。观看采访。

这就是全部所需。

[知识分享官 @knowledgefxg]

推荐一位风趣幽默的美籍华裔英语老师: Jon Peng
他有两个 YouTube 频道，Jon Peng English，Jon Peng Podcast，前者带你练日常口语，后者属于播客类栏目。每期就像看一期脱口秀，用标准到不能再标准的美式口音讲故事、聊生活、吐槽日常，想找轻松有趣、又能练英语的听力素材，这个完全够用。