Hao Hao Tan

@Jiaxi_Cui 对于愿意研究和维护 openclaw 的一群，确实是这样的。但好用的 SaaS（用户体验好，粘性强，功能广度深度足够），应该还会是大部分 non-tech 群众的首选吧。CLI在 UI上还是有局限性的，而既有的 SaaS 并入 agentic 迭代是值得期待的方向

其实离 Meta 宣布收购 Manus，还不到三个月 第一个月隐约觉得 CLI 会吞噬掉很多 SaaS 的份额，开始讨论什么种类的 SaaS 在以后还活得下去 第二个月各种 skill 火起来了，传统固定流程需要付费的 SaaS 被OpenClaw + skill 干死了 而这第三个月还没结束，从创业者到投资人都开始喊 build for agent了 难以置信，我们生活在一个什么样的时代

@mstockton How do you compare with @superwhisper?

By far my biggest advice to anyone trying to adopt AI properly: 1. Pay a little bit of money to Anthropic 2. Download Claude Code 3. Open Claude Code 4. Press 'Shift-Tab' until it says 'plan mode on' 5. Open Voice Memo on your iPhone. Just talk about all the things you want to accomplish. When you think you are done, just keep talking. Make sure it is at least 10 minutes, hopefully longer 6. Send this Voice Memo to your computer 7. Download MacWhisper and use it to transcribe this voice memo. Trust me, you will want MacWhisper and will use it later a lot 8. Type into Claude Code: "I have never used you before but I talked about some things. I will paste those things in below. Please read the things and ask me any questions you need to in order to help me figure out how to use you to be awesome. Ask me lots of questions until I tell you I am done" 9. Then paste in the transcript 10. Then press enter Then just let Claude take the wheel, and them please send me a DM if this works. Also, if this just sounds crazy, just literally take this entire message and paste it into whatever AI you are using and say 'some weird person told me to paste this into you, I want to use it, but I don't know how. What should I do?' I am just trying to help you get started. Curiosity and persistence are the most important things.

@Jiaxi_Cui 深受启发。向您学习

年底总结一波： 我的账号基本闲置了半年。年初破 1 万粉后成就感突然见顶，就当生活号随便刷，内容也跟着变水。10 月决定重启：屏蔽掉 timeline 里一堆没营养的段子手，关注更多全球各地的开发者、AI 研究员、VC，尽量不再传播低质量内容。账号像是重新活了过来——每次认真分享一些东西，收到的点赞和新增关注，都会让我觉得「又被看见了」。 想写点真实的背景： 我第一次创业在 2023 年，几乎把市面上能搜到的坑都踩了一遍：股权混乱、合伙人太多、把教授也拉进来、和高校的关系边界说不清……但更重要的是，我们在做“国内法律大模型落地”，而我其实并不信仰法律，只是机缘巧合站到了这个赛道。每次听到更多行业规则，我都觉得糟透了——这可能不是我喜欢的事。我们训练新的 chat 模型、embedding 模型，我甚至应该是第一个提出可以用小参数的LLM来训练embedding的，而且效果确实不错，可我看到的这个行业是：没有真正有技术信仰的人在这个赛道上一决高下。 好在我遇到很好的合伙人，她是我高中以来很好的朋友，帮了我很多。我们商业化跑了半年就迅速退出。那段时间我夹在各种人和事之间，非常痛苦、内耗，身体和心理几乎都没健康过。有一天她很认真地对我说： 如果你做的事情已经让你很痛苦了，那真的不如放弃，不是所有事情都要坚持下去的。 我记了很久，可能以后也会一直记下去。太早期的退出算失败吗？或许不算——至少我们及时把公司卖掉了。 之后我花了一段时间找自己、找方向。我慢慢发现：我可能就是个 nerd——喜欢深奥的科技，喜欢简洁、有美感的工具，想研究难但有趣的领域。兜兜转转，我最喜欢的还是多模态和视频。 于是我补了这两年落下的前沿进展，发现图文视频数据是个很难的问题。我们最开始花了几个月做了 DataTager：一个给 AI researcher用的数据标注SaaS产品。当时的想法很天真：让researcher别把时间浪费在标注上。后来发现，这个需求并不需要一个 SaaS，于是转成定制化数据。靠相对高质量的数据和 research 背书（我 Google Scholar 好歹还是有 3k 引用的），很快跑到了 $1M 收入。 但再往下做，就越来越依赖“行业关系”。有次南方某地部门号称要批量采购数据集，我当天晚上飞过去吃饭喝酒，酒过三巡才打听到他们要的返点非常夸张。我真的很不喜欢这种事：他们并不在乎你的 insight，也不在乎更合理的评测标准，只希望你报更高的价格，好给他们更高的返点。即便双方都能赚钱，我还是更想在赚钱的基础上做点有趣的事。 所以今年我砍掉了之前所有业务，重新回到我真正喜欢的方向：做视频产品、在 global 运营。为用户提供价值——用户认可价值，用订阅支持你——这是非常明确的正反馈。 过去也和 VC 朋友聊过很多点子：有些被说“不可能”，有些被说“没想象空间”。但时间过去，那些“不可能”反而变成行业标准，转变只是OpenAI发了一个新的论文说明他们现在是如何做的。现在想明白了：以前我有点讨好 VC，想顺着对方的意思讲下去，之后懒得做了，爱咋咋滴吧，就这个产品和我的这摊规划，你觉得合理咱们再继续聊。 自从专注在喜欢的领域，我发现我的脑子三天两头就会冒出有趣的事，这非常让人开心。之后用积累的一点点资金去探索，我想把视频本地化做得更好、想做全网视频的索引等等 也许其中有些产品最后会长成小公司，而这些公司都由我们控股。想想就很有趣。 今天粉丝到 16k 了，很开心。之后我会继续保持这个节奏：只围绕创业思考、产品设计/开发、模型训练输出；不靠短期流量、不靠接广告，我也能更自由地表达。谢谢大家 🤝

🌶️ Some (perhaps) spicy thoughts. It’s been a while since my last tweet, but I wanted to write about how disorienting it has been from academia to an LLM lab 😅 The kind of research I was trained to do during my PhD almost doesn’t exist here. The obsession with mathematical elegance and novelty is mostly gone. Everything is about scaling data and compute. For a while, that really got to me. At my lowest point, I felt like I’d lost interest in building LLMs altogether. I didn’t feel intellectually challenged anymore. What made this even stranger was that, at a technical level, things worked. If there was a capability I wanted to teach a model, scaling the right data and compute always got me there, no exception (so far). But recently, I found a way to reconcile with myself.. I realized the real competition isn’t in the ML recipe anymore. Most teams do roughly the same thing. What actually matters is how fast you can iterate, test ideas, and recover from mistakes. And that speed is mostly backed by infrastructure 🏗️ Faster loops, fewer bugs, better tooling. Seeing this made me excited again! Infra is its own deep, hard, and intellectually fun problem space. In 2026, I want to become an ML researcher who’s really good at infra. And I'll come back to ML problems with that edge, and will be excited to share what I find 😌

@howariou @ylecun 😂😂

@ylecun did not invent anything: I just found this breadcrumbs collector at my gf's grandparents'. What a pioneer work!

#heading=h.ivdr7fmrbeab" target="_blank" rel="nofollow noopener">docs.google.com/document/d/1y5…

Malaysia now has her very own Chopin Competition prize winner! Vincent Ong from Penang won 5th prize in this very prestigious piano event 🎉 His performance in the third round was much applauded for his elegance and unique interpretation: youtu.be/jN8y20xwVp4?si…

If you are interested in `modulation extraction` for synth parameter inference (which most of the synth presets out there are modulated), do check out this work! It's super wonderful to get to work with Christopher @frozenmango (finally!!)

We ran a randomized controlled trial to see how much AI coding tools speed up experienced open-source developers. The results surprised us: Developers thought they were 20% faster with AI tools, but they were actually 19% slower when they had access to AI than when they didn't.

Built a bunch of assistive tools to help musicians on BandLab create better with AI (Voice Changer, audio-to-MIDI, Recompose...) The best part is to see them on "combo" - combining these tools into an actual workflow. Check out the full suite here! bandlab.com/ai

torchlpc v0.7 * All kernels are rewritten in C++/CUDA * New parallel scan kernel for CPU * Running 1.2 to 2 times faster on GPU * Return final delay values (aka `zf`) See release notes for more details. github.com/DiffAPF/torchl…

I am pleased to announce a new version of my RL tutorial. Major update to the LLM chapter (eg DPO, GRPO, thinking), minor updates to the MARL and MBRL chapters and various sections (eg offline RL, DPG, etc). Enjoy! arxiv.org/abs/2412.05265

An intuitive approach to understanding Flow Matching: youtube.com/watch?v=7cMzfk…

Many exciting recent works on critique+ improving FAD! Time to update the toolkit repo lol

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Kubeflow Pipelines is VERY hard to use. I find myself drowned in an ocean of documentation, especially with the V2 migration. Anyone facing the same infra setup pain points? Or are there easier solutions out there?

Interesting observation. Although not formally proven yet.

At @nushackers today too. Favourite project: gyatword.com. Wordle-style gen-alpha slang crossword puzzle. I'm a millennial and I only can get 3 right, lol