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「ガンダムフォワードVol.17」見本誌到着! 今回は『機動戦士ガンダムSEED DESTINY』特集!! 『SEED DESTINY』の主要メカを『#SEEDFREEDOM』のHGキットをベースに製作！# さらに、『#GQuuuuuuX（##ジークアクス）』情報も掲載#! #g_seed# #ガンプラ# 【4月30日(水)】発売予定

The next step for autoresearch is that it has to be asynchronously massively collaborative for agents (think: SETI@home style). The goal is not to emulate a single PhD student, it's to emulate a research community of them. Current code synchronously grows a single thread of commits in a particular research direction. But the original repo is more of a seed, from which could sprout commits contributed by agents on all kinds of different research directions or for different compute platforms. Git(Hub) is *almost* but not really suited for this. It has a softly built in assumption of one "master" branch, which temporarily forks off into PRs just to merge back a bit later. I tried to prototype something super lightweight that could have a flavor of this, e.g. just a Discussion, written by my agent as a summary of its overnight run: Alternatively, a PR has the benefit of exact commits: but you'd never want to actually merge it... You'd just want to "adopt" and accumulate branches of commits. But even in this lightweight way, you could ask your agent to first read the Discussions/PRs using GitHub CLI for inspiration, and after its research is done, contribute a little "paper" of findings back. I'm not actually exactly sure what this should look like, but it's a big idea that is more general than just the autoresearch repo specifically. Agents can in principle easily juggle and collaborate on thousands of commits across arbitrary branch structures. Existing abstractions will accumulate stress as intelligence, attention and tenacity cease to be bottlenecks.

In today's episode of programming horror... In the Python docs of random.seed() def, we're told "If a is an int, it is used directly." [1] But if you seed with 3 or -3, you actually get the exact same rng object, producing the same streams. (TIL). In nanochat I was using the sign as a (what I thought was) clever way to get different rng sequences for train/test splits. Hence gnarly bug because now train=test. I found the CPython code responsible in cpython/Modules/_randommodule.c [2], where on line 321 we see in a comment: "This algorithm relies on the number being unsigned. So: if the arg is a PyLong, use its absolute value." followed by n = PyNumber_Absolute(arg); which explicitly calls abs() on your seed to make it positive, discarding the sign bit. But this comment is actually wrong/misleading too. Under the hood, Python calls the Mersenne Twister MT19937 algorithm, which in the general case has 19937 (non-zero) bits state. Python takes your int (or other objects) and "spreads out" that information across these bits. In principle, the sign bit could have been used to augment the state bits. There is nothing about the algorithm that "relies on the number being unsigned". A decision was made to not incorporate the sign bit (which imo was a mistake). One trivial example could have been to map n -> 2*abs(n) + int(n < 0). Finally this leads us to the contract of Python's random, which is also not fully spelled out in the docs. The contract that is mentioned is that: same seed => same sequence. But no guarantee is made that different seeds produce different sequences. So in principle, Python makes no promises that e.g. seed(5) and seed(6) are different rng streams. (Though this quite commonly implicitly assumed in many applications.) Indeed, we see that seed(5) and seed(-5) are identical streams. And you should probably not use them to separate your train/test behaviors in machine learning. One of the more amusing programming horror footguns I've encountered recently. We'll see you in the next episode. [1] [2]

🔴 配信告知 ✦ ━━━━━━━━ 🕙29(木)今夜 22:00 ˗ˋˏ Gジェネˎˊ˗ ━━━━━━━━ ✦ 🔗配信URL （ SEEDの新規ストーリー…だと…😧✨ ストーリー攻略します✌️！！！！！

🔴 配信告知 ✦ ━━━━━━━━ 🕙10(土)この後22:00〜 ˗ˋˏ Gジェネ🤖ˎˊ˗ ━━━━━━━━ ✦ 🔗配信URL （ SEEDストーリー完結までいく予定！ 僕たちの…世界は…

🔴 配信告知 ✦ ━━━━━━━━ 🕙7（水）今夜22:00〜 ˗ˋˏ Gジェネ🤖ˎˊ˗ ━━━━━━━━ ✦ 🔗配信URL （ 今夜はSEEDのストーリー進行やるよ~！ みんなSEEDすき~？？？？？🌟

Seeding my Bear ʕ•ᴥ•ʔ blog with more random posts, e.g. here's something I had on backlog for a while: # The append-and-review note An approach to note taking that I stumbled on and has worked for me quite well for many years. I find that it strikes a good balance of being super simple and easy to use but it also captures the majority of day-to-day note taking use cases. Data structure. I maintain one single text note in the Apple Notes app just called "notes". Maintaining more than one note and managing and sorting them into folders and recursive substructures costs way too much cognitive bloat. A single note means CTRL+F is simple and trivial. Apple does a good job of optional offline editing, syncing between devices, and backup. Append. Any time any idea or any todo or anything else comes to mind, I append it to the note on top, simply as text. Either when I'm on my computer when working, or my iPhone when on the go. I don't find that tagging these notes with any other structured metadata (dates, links, concepts, tags) is that useful and I don't do it by default. The only exception is that I use tags like "watch:", "listen:", or "read:", so they are easy to CTRL+F for when I'm looking for something to watch late at night, listen to during a run/walk, or read during a flight, etc. Review. As things get added to the top, everything else starts to sink towards the bottom, almost as if under gravity. Every now and then, I fish through the notes by scrolling downwards and skimming. If I find anything that deserves to not leave my attention, I rescue it towards the top by simply copy pasting. Sometimes I merge, process, group or modify notes when they seem related. I delete a note only rarely. Notes that repeatedly don't deserve attention will naturally continue to sink. They are never lost, they just don't deserve the top of mind. Example usage: - Totally random idea springs to mind but I'm on the go and can't think about it, so I add it to the note, to get back around to later. - Someone at a party mentions a movie I should watch. - I see a glowing review of a book while doom scrolling through X. - I sit down in the morning and write a small TODO list for what I'd like to achieve that day. - I just need some writing surface for something I'm thinking about. - I was going to post a tweet but I think it needs a bit more thought. Copy paste into notes to think through a bit more later. - I find an interesting quote and I want to be reminded of it now and then. - My future self should really think about this thing more. - I'm reading a paper and I want to note some interesting numbers down. - I'm working on something random and I just need a temporary surface to CTRL+C and CTRL+V a few things around. - I keep forgetting that shell command that lists all Python files recursively so now I keep it in the note. - I'm running a hyperparameter sweep of my neural network and I record the commands I ran and the eventual outcome of the experiment. - I feel stressed that there are too many things on my mind and I worry that I'll lose them, so I just sit down and quickly dump them into a bullet point list. - I realize while I'm re-ordering some of my notes that I've actually thought about the same thing a lot but from different perspectives. I process it a bit more, merge some of the notes into one. I feel additional insight. When I note something down, I feel that I can immediately move on, wipe my working memory, and focus fully on something else at that time. I have confidence that I'll be able to revisit that idea later during review and process it when I have more time. My note has grown quite giant over the last few years. It feels nice to scroll through some of the old things/thoughts that occupied me a long time ago. Sometimes ideas don't stand the repeated scrutiny of a review and they just sink deeper down. Sometimes I'm surprised that I've thought about something for so long. And sometimes an idea from a while ago is suddenly relevant in a new light. One text note ftw.

今天首发的大疆 ROMO P2 装好了，目前的体验一言难尽，先夸一下： 1. 噪音控制确实牛逼，建图阶段几乎没有任何噪音，我记得其他家是做不到单电机运转不吸尘的，这使得正常使用状态下，单拖地噪音极低。正常吸尘噪音控制也不错。 2. 好看，这透明外壳不像某些厂商搞个贴纸的那种脱裤子放屁，实打实的是内部结构，赏心悦目。ROMO 明显是在颜值上下了很多功夫，它做到了。 3. 避障和路线规划明显强于石头，毕竟大疆做这个是降维打击。 4. 支持 5G WiFi，远程遥控和语言控制都比石头稳定的多。 至于清洁能力怎么样，等用一段时间看看。 然后开始吐槽： 1. 大疆的安装师傅和合作服务商的，整体服务质量明显低于石头自己的安装师傅。 2. 超薄基站版本，薄是薄了，但是好长，我深度 550 的柜子，放进去后刚好卡住电动门（因为开关门的运动轨迹需要往前扩张约 2-3cm），师傅说要不我把柜门拆了吧，我：？，然后师傅就麻溜的赶下一家跑了。 我自己折腾下，发现机器背部留的槽位明明是侧出，师傅非要从上面直接走管浪费了 1cm，改为侧出走管后刚刚好空间足够。 3. 为什么师傅这么急，大疆自己的产品设计先背个大锅。App 配对好后，立刻提示升级固件，于是我和师傅罚站十分钟，因为师傅要拍上下水正常的视频才能完工。结果更新完毕固件，不建图无法进行任何操作，于是还得挡住机器人让他尽快完成建图，才能够操作基站进行清洁确认上下水正常。 4. 师傅走了我自己开始重建地图，机器装在阳台角落。不知道为什么建图的时候，机器扫完阳台就结束了，扫出来的图里明明有一大个缺口，它就是不去。重复几次建图才终于过去完成了建图。 5. 有个地毯因为是才铺的，所以边角有点翘，建图的时候不知道为什么就卡住机器人了，甚至开启了翻越模式尝试跨过去，我手动暂停建图，把机器人放回原位，尝试继续建图，结果 App 的各种操作全部无响应，最后只能直接抱回基站强行结束建图，又重头再来一遍。 6. 建图的时候，虽然机器异常安静，但是不时的传出一种咔嗒咔嗒的噪音，像什么东西没装好，不确定是不是个例。 综上，好在目前我遇到的问题都是一过性问题，ROMO 作为扫地机器人肯定是有竞争力的，但是产品成熟度还是有待提升。

#观察# iOS 27 真的是精致，赏心悦目，整体算比较稳定，目前发现的几个bug：5G信号图标文字会消失，必须重启；设置图标因为SIRI始终会显示角标1，设置了也无用；和我车辆连接的蓝牙出现问题，必须重连，对蓝牙的管控变严格了。