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Follow AI Builders — Not Influencers

Discover real AI creators shaping the future. Track their latest blogs, X posts, YouTube videos, WeChat Official Account posts, and GitHub commits — all in one place.

T
Tibo
𝕏x•5 minutes ago

the best startup founders in 2026 won't be the best coders they'll be the best Product Managers here's the full playbook: 1. pick a problem you personally have. you need to understand your user better than anyone else, and the easiest way to do that is to be your own user 2. spin up a landing page with AI fast. make it about the pain and the fix, no fluff. and make signup 2 clicks max, remove every possible friction 3. learn to write specs, describe features and explain edge cases. that's all you really need now 4. let AI build the first version. your job is to direct, not develop 5. use whatever stack you're most comfortable with. just ship. don't overthink the tech decision - that's not your job anymore 6. host on the simplest platform possible - Vercel, Railway, whatever - don't touch servers 7. test thoroughly - especially edge cases. you're a QA now 8. charge from day 0. free users give you nothing but false hope 9. list your product on every directory you can find. it compounds your SEO and builds DR over time 10. start building in public. share the micro wins, the losses, everything. invite people to try it through DMs. I did this for all my products 11. do customer support yourself, always. redirect questions to your socials, never automate this. every support request is a product insight - that's why you do it yourself, always (I still do this for all 5 of my products) 12. automate everything else you do more than twice 13. improve one thing in your product every single day once users start coming in. watch your competitors' G2 and Trustpilot reviews closely for ideas 14. a PM's most important skill is deciding what NOT to build. for every feature request you get, say no to 9 of them - build less, but build it right 15. every week, build one free tool targeting a keyword that benefits you. it compounds over time 16. work your SEO early. build your affiliate setup early. only run paid acquisition after you're sure about PMF every day your ...

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饼干哥哥AGI
📱wechat•about 2 hours ago

AI内容工厂2.0:我把OpenClaw换成ArkClaw,接管了整个飞书

AI内容工厂2.0:我把OpenClaw换成ArkClaw,接管了整个飞书

本文介绍了内容创作工具从1.0(Obsidian + OpenClaw)到2.0(飞书 + ArkClaw)的升级方案,核心是通过飞书生态与ArkClaw智能体实现内容创作全流程自动化:从刷到灵感自动入库、AI推荐选题、生成大纲与初稿、同步云文档,到最终归档知识库,用户只需提供素材和决策。ArkClaw依托火山引擎,无缝对接飞书多维表格、知识库等原生功能,降低部署门槛,提升稳定性与效率。作者还提出3.0构想:让Obsidian专注个人风格与素材沉淀,ArkClaw作为桥梁实现双向同步,构建‘参考书+生产机器’的协同体系,真正实现AI驱动的内容工厂。

饼干哥哥AGI
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Yohei Nakajima
𝕏x•about 4 hours ago

these days we focus so much on the speed things move, but I love exercises like these to step back and look for parallels in history

@Organizermemes

I love this video

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Yohei Nakajima
𝕏x•about 4 hours ago

first funding round, over one billion not valuation, but amount raised yann lecun

@AMI Labs

Advanced Machine Intelligence (AMI) is building a new breed of AI systems that understand the world, have persistent memory, can reason and plan, and are controllable and safe. We’ve raised a $1.03B (~€890M) round from global investors who believe in our vision of universally

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Andrej Karpathy
⚡github•about 4 hours ago

Activity on repository

karpathy pushed nanochat

karpathy pushed nanochat

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Yohei Nakajima
𝕏x•about 4 hours ago

haha the @runwayml real-time characters is great. i just spent 2 minutes pitching a ghibli version of myself a bladeless blender startup it's only a 2 min demo, but the questions and tone are pretty solid. one question even threw me off a bit (the cleaning). asking which round was unnecessary. it got a little weird at the end right before it cut off, which is due to a 2 min limit when testing on site(which seems unnecessary since they're charging me for the api - why not let me talk longer?) at current pricing, this would be roughly ~$6 for a 30 min call or $12 an hour. this doesn't include the cost of running an extraction prompt in the background to store this data, but llm tokens are so cheap. very expensive compared to mean vc (free, since it's in gpt marketplace) or even a self-hosted chat agent, but it's way cheaper than an associate. while it has downsides like not being a real person, there's also benefits like having consistent and unbiased information gathering, being available 24/7, being able to talk to founders in parallel, and consistent note taking. and it will likely get cheaper (sorry my audio volume is low, i'm testing while everyone else in the house is asleep)

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Aravind Srinivas
𝕏x•about 5 hours ago

Someone built a cool tool with Perplexity Computer to port a Spotify Playlist to Youtube Music automatically by just pasting a playlist URL. Cross service migrations are going to be seamless with tools like Computer.

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Aravind Srinivas
𝕏x•about 6 hours ago
Retweeted from @Gabbar

RT Gabbar It will eat my job :) Ask any founder, finding a great performance marketing expert who doesn’t fleece you is such a pain. So why not just build one? Perplexity Computer just replaced the entire marketing dept 🥲. Such stuff is a boon for a bootstrapped startup founder. Focus on the core product, leave everything else to AI👍 Original tweet: https://x.com/GabbbarSingh/status/2031222631417131120

@Computer

Perplexity Computer replaced $225K/yr in marketing tools in a single weekend. We built an AI marketing agent that scans hourly, manages budgets, detects fatigue, and coordinates several campaigns end to end. In one test run, it made 224 micro-optimizations to our ad stack.

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Harrison Chase
𝕏x•about 7 hours ago
Retweeted from @Teng

RT Teng Yan · Chain of Thought AI "the pre-Claude era of building software (starting with a PRD) is gone." it won't ever come back again. adapt or die. good read. Original tweet: https://x.com/tengyanAI/status/2031219718460420200

@Harrison Chase

http://x.com/i/article/2031049457031331841

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Kevin Weil
𝕏x•about 7 hours ago

A look at the future/present

@Andrej Karpathy

Three days ago I left autoresearch tuning nanochat for ~2 days on depth=12 model. It found ~20 changes that improved the validation loss. I tested these changes yesterday and all of them were additive and transferred to larger (depth=24) models. Stacking up all of these changes,

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