tweets things.

Taipei, Taiwan
Joined August 2010
It still feels creepy that I can spin up 100+ machine learning models in a single night. 100+! 🤯
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I'm looking for a new Vim color scheme. What are you all using these days?
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I'm kind of good at regex, but an absolute wow moment with AI was watching it compose a complex regex in milliseconds for an rg dependency scan. Not feeling replaced; just feeling like I can code at warp speed.
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One of the prompting techniques I will use is: “How would you ... ?” This allows AI to use “a thinking unit” to plan instead of just doing. Thanks to CoT, you'll see much better results later in the conversation.
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People always say write detailed prompt (including myself as a teacher); however I am a lazy engineer, I always write short prompts. I've been enjoying the art to let AI write context by itself. 😎 What do you think?
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Since my side project is more related to data science, I still stick with Codex CLI (GPT-5.1). Hope I can find some people who have the same feeling here.
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So, I am wondering that people like Gemini 3 so much, so are devs switching to Gemini CLI? Or, it's more like another new tech lover hyper rather than a true productive model for devs?
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And, yes, you should always ask AI to fact-check its response. The AIs nowadays still are not so good at facts, even book knowledge–level facts.
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Looking forward to the day that I don't need to ask AI to fact-check its prev response.
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The best data format for AI is CSV, and the best tool for human reading CSV is vd (VisiData). SO GREAT! You can ask your AI for the keystrokes, btw.
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I just learnt that `zY` can copy a cell into sys clipboard. What the heck. AWESOME. You can also search (/), filter (| then "), sort ([ or ]) your CSV in a decent command line viewer. A wonderful tool when you work with CLI AI.
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I’ve been trying Warp lately. It uses the same AI engine as Cursor, but feels less capable. Interesting.
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The decision matrix keeps coming to mind. I feel like it was an early attempt by people in management to apply vector-like thinking—and it has ended up being one of the most effective methods.
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Me: Choosing an AC brand is too hard. Also me: Let’s make ChatGPT run cosine similarity. ✌🏻
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Being data nerds, we should totally have a metric for the domain: Functional Coverage Index = dot(R, C) / dot(R, R) ☺️
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Cursor Taipei! Thank you for having me as a panel speaker.
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Love the study! He fixed the “9.11 > 9.9” LLM issue by setting the “bible” neuron activations to 0. 👍🏻
why do language models think 9.11 > 9.9? at @transluceAI we stumbled upon a surprisingly simple explanation - and a bugfix that doesn't use any re-training or prompting. turns out, it's about months, dates, September 11th, and... the Bible?
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I think the math works like: to reach the growth goal, the company needs to calculate how much we can spend and where to reduce headcount. It doesn’t work like: AI can generate 1/4 of the new code, so let’s cut 1/4 of the team. What do you think?
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Ah, yes. Thanks for the advice.
Replying to @alexalbert__
The pieces of the true agent puzzle are starting to fall into place. If you're developing on AI today, you need to be thinking about building complimentary pieces to this reality. Because it may come faster than most expect.
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