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Hi everyone! I recently decided to dive into animatronics and started building an open-source robotic desk lamp inspired by expressive animated lamps from Pixar. This is still a very early mechanical prototype. Right now only the base and the first joint move, and I'm designing the rest of the arm. I'm looking for advice on one of the biggest design challenges: how would you route the cables through the moving joints while keeping them hidden and minimizing stress on them? If you've built articulated robots or mechanisms before, I'd really appreciate any examples, photos, or resources. I'd also love to hear your ideas for useful features. My current plan is to eventually add a depth camera so the lamp can understand its surroundings and interact with people. As a fun long-term challenge, I'd even like to see if I can teach it to jump or move around on its own. Any suggestions or criticism are very welcome! submitted by /u/Ok_Stress3654 [link] [Kommentare]
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FarRail Tours bietet Reisen für Eisenbahnfreunde, Fotografen und Videografen. FarRail offers tours to the last remaining steam operated railway lines in the world.
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One AI assistant is a tool you drive. A fleet of agents working unattended is a management problem: an inbox, approval gates, written rules, and a weekly cadence. Lessons from running a real business this way.
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I have worked with revision control and on revision control systems for quite some time now and I had every opportunity to develop strong opinions. Many of those opinions have changed as parallel LLM development became the norm. Tomorrow, the process will likely evolve even further. So, how do I see modern revision control? Can I really have any stable opinion here? The only stable thing now is constant change. How can source code management help here? Should we use worktrees, patch stacks, CRDT merges, trunk-based development? The correct answer is that I do not know what will work tomorrow. So the revision control system must, first and foremost, be adaptable to any new process, including project-specific processes. Beagle, the revision control system, is built to fit this exact condition. There is libdog, the layer of fast and deterministic tools of the trade: git compatibility, source code tokenization, indexing, data exchange format, diff and merge algorithms. There is a minimalistic JavaScriptCore-based JS runtime JAB. I do not use node.js and others because of their weight and ridiculous supply chain issues. Finally, on top of JAB there is the malleable part - the revision control system itself. Beagle is made in a very specific way - it uses an orthogonal basis of HTTP verbs (GET, HEAD, POST, PUT, PATCH, DELETE) to do any work and URIs to address any resource. The runtime itself can execute any "verb", so diff, status, list, log and many other normally expected things are there. In fact, I write it as I go. I need a tool, I tell Claude to make a tool. Changes to libabc I carefully read, changes to libdog I look at, beagle I can vibe. This way I keep the actual behavior malleable without devolving the codebase into slop. Frankly, Netscape invented JavaScript exactly for this kind of use. Yesterday I wanted a new blame view. It took me a couple of hours, and I called it why. Because, when you work with LLMs, you can only blame yourself! It is all JavaScript, all the heavy lifting is done by the libdog backend, and it works. If I do not like how it works, I can tell Claude to correct things. JavaScript is sufficiently safe and malleable to make this process painless. In fact, that became the norm: I have a terminal to work on a thing and another terminal on the side to work the tool to work the thing.
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Every post I publish represents at least two things I’ve learned: the thing that prompted me to write the post, and the thing I learned in the course of writing it. If I don’t learn anything new while I’m writing, it’s not interesting enough to publish. Typically I learn way more than two things. For instance, in my o3 geoguessr post, I started out with the idea that most AI prompts probably don’t work, and I ended up learning that newer OpenAI models have lost o3’s ability to geolocate. That’s interesting! In my most recent post on C2PA, I started out with the idea that C2PA requires near-universal adoption, but I learned a ton of things about PKI, managing private keys on local devices, how C2PA actually works, and so on. In my post on the Luddites, I started out with the idea that the Luddite movement was fundamentally decentralized, but ended up fascinated by Luddite culture (which was far more elitist, misogynist, and violent than the pop-Luddism books describe). I could do this for every single post on the blog. Taking a position I think the core reason this works is that every single one of my blog posts argues a point. I never publish a post that just gives some scattered thoughts on a topic, or a post that only says “yes, I agree with this other article”. If I write a draft that nobody sensible could disagree with, I scrap the draft. Making sure that everything I write is at least minimally controversial is a forcing function: it forces me to think about what the most interesting part of my position is, and it forces me to do enough research to defend it against the obvious criticisms. This is contrary to a lot of advice I read about blogging, which encourages the aspiring blogger to treat their posts as a form of unstructured self-expression. If unstructured self-expression is what you want to do, that’s cool. The point of having a blog is that you get to write what you want. However, this advice isn’t as helpful as it sounds. Before I was in tech, I was a philosophy grad student. But before that, I was a poet. One thing you learn when you try to write poetry is that it is way easier to write to a restrictive structure than it is to simply “write what you feel”. This should be obvious when you actually think about it. The task of a poet is to repeatedly choose the next word. Writing to a structure (typically rhyme or meter) narrows that choice to a small set of words, instead of the entire English language. It’s the same with blogging. Forcing yourself to write about specific, potentially-controversial points makes consistently writing easier, not harder. Writing, thinking, and research Writing is the best way to think clearly about a topic. It’s easy to believe you understand something when you’re just turning it over in your head. When you have to condense that down into words, you find out exactly how much you do or don’t understand. I am constantly having moments where I type something, stop myself, and think “wait, that can’t actually be right”, or “is that really true?” By the time I write my way to the end of the post, I’m usually thinking so much more clearly about the topic that my conclusion paragraph is way better than my introduction. In fact, I’ve picked up the habit of going back and immediately rewriting the first paragraph as part of my first-draft process, because I know I’m going to end up doing it anyway. I also change my mind a lot while I write. Here are a bunch of examples of posts where I began writing them with the opposite opinion to the one that eventually made it into the post. I think this is a good sign, and I hope I never stop doing it. You should be researching and thinking about every post you write, and that means you should frequently learn new things that change your mind. Because of all this, I deliberately choose to write blog posts about things I don’t yet quite understand but would like to, like LLM steering, Stripe’s Tempo blockchain, C2PA and watermarking, space cooling, interaction models, LLM inference internals, and so on. This is great for me, because I learn a lot. Is it great for my readers? Is blogging to learn irresponsible? I sometimes worry that I should only be writing about areas I already know very well, like tech company dynamics or working in large codebases, rather than presenting myself as an authority on fields I’m actually still learning. Should I let historians of the Luddites write about Luddism, Web3 engineers write about blockchains, and so on? I think this is acceptable for three reasons. First, it’s sometimes easier for a beginner to write an introduction to a field than for an expert. Experts routinely overestimate the knowledge of the general public, and have often internalized the reasons why their field is important so deeply that they struggle to express them. I think my explainer posts are valuable because I always spend the first chunk of the post talking about what the original problem is before I get into the technical solution. Second, sometimes the public consensus on a topic is just plain wrong, to the point where even a little bit of research is enough to demonstrate why. Many of my posts I’m proudest of have been along these lines: arguing that the “500ml per prompt” water usage figure for LLMs was ludicrous, or that the popular Apple “Illusion of Thinking” paper was tracking persistence, not reasoning, that GPUs live longer than three years and the AI companies have large profit margins on inference, and so on. Third, I try to make it clear on my blog who I am and what my credentials actually are. Even if it’s not explicitly described in the post, I have my real name and resume available on my /about page, so I don’t think a careful reader could be easily fooled into thinking I’m an expert on 19th-century England or space physics or LLM economics or anything like that. Feedback Even if nobody reads what you write, writing is still a good discipline for getting your thoughts in order. But another big reason why writing is a great learning tool is that you can get feedback. I think it’s obvious why this is useful, but I do want to make two points about feedback. First, if you do make your posts public, you need to have a pretty thick skin. People on the internet often fall over themselves to come up with the most cutting criticism or the harshest dunk. This goes double if you take my previous advice and try to write posts that make a clear, controversial point about a subject you’re learning. If you’re the kind of person whose whole day is ruined when a stranger is cruel to them, you might want to keep your blogging private or only share it among friends. Second, even if your blogging is private, you can get feedback from LLMs. Like humans, LLMs will often give junk feedback. In my experience, OpenAI models will always tell me to moderate my claims or add caveats and hedges until I’m not saying anything at all. Sometimes their criticism will be straight-up wrong. But — particularly about technical topics — LLMs are great at pointing out areas you’ve genuinely misunderstood, and they’re far kinder than the average Lobsters or Hacker News commenter. Conclusion I’m pleased and grateful that people enjoy reading my posts, but even when nobody did, I still got a lot of value out of blogging. I write as a method of thinking more clearly, as an excuse to do research on topics I want to learn about, and as a way of getting feedback. If you’d like to try it yourself, I suggest watching for these two things. First, you should be changing your mind a lot as you write. If not, you probably aren’t doing enough research. Second, your first draft’s conclusion should be much tighter and more expressive than its introduction. If not, you probably haven’t learned anything from the writing process, which means the draft can be scrapped. I strongly recommend this practice to anyone with an interest in writing. You will see the benefits even if you don’t publish any of your writing on the internet, particularly now that you can get good technical feedback by pasting your post into a LLM1. For what it’s worth, I’ve fiddled with careful “review prompts” and it’s basically as good to just write “review, please:” and paste your article. ↩ If you liked this post, consider subscribing to email updates about my new posts, or sharing it on Hacker News.Here's a preview of a related post that shares tags with this one.Saying the obvious thingStating the obvious is surprisingly useful. Most of your knowledge lives below the threshold of conscious awareness, so it’s possible for a piece of writing to remind you of what you already know. It’s common to know you don’t like something without being quite sure why, and reading an obvious statement (such as “accuracy matters, even when you agree with the broad strokes”) can help clarify why you find certain things distasteful.Continue reading...
