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明日は巾着田行くぞｵﾗｧﾝ
車乗り付けはやめて、近くの駅前に停めて、アクセス自体は鉄道にしようかね
駐車場ストレス感じたくないし
スチームをLinuxで遊ぶための全知識: トラブル・躓きやすいポイントを徹底フォロー！: Windowsユーザの為の徹底ガイド！ https://youtu.be/jH7tbxEhuYA?si=MnIFcvxrSnyGCTj8
Pop OS is getting beta
Link: https://system76.com/pop/pop-beta/
Discussion: https://news.ycombinator.com/item?id=45384481
아 전어(I’m uh)먹고싶다
「Cursor物語」というタイトルで、DeveloperIO Sapporo 2025の10分LTに登壇しました。#devio2025
https://dev.classmethod.jp/articles/cursor-developerio-sapporo-2025-kaji/
I think this needs to be repeated, since I tend to be quite negative about all of the 'AI' hype:
I am not opposed to machine learning. I used machine learning in my PhD and it was great. I built a system for predicting the next elements you'd want to fetch from disk or a remote server that didn't require knowledge of the algorithm that you were using for traversal and would learn patterns. This performed as well as a prefetcher that did have detailed knowledge of the algorithm that defined the access path. Modern branch predictors use neural networks. Machine learning is amazing if:
The second of these is really important. Most machine-learning systems will have errors (the exceptions are those where ML is really used for compression[1]). For prefetching, branch prediction, and so on, the cost of a wrong answer is very low, you just do a small amount of wasted work, but the benefit of a correct answer is huge: you don't sit idle for a long period. These are basically perfect use cases.
Similarly, face detection in a camera is great. If you can find faces and adjust the focal depth automatically to keep them in focus, you improve photos, and if you do it wrong then the person can tap on the bit of the photo they want to be in focus to adjust it, so even if you're right only 50% of the time, you're better than the baseline of right 0% of the time.
In some cases, you can bias the results. Maybe a false positive is very bad, but a false negative is fine. Spam filters (which have used machine learning for decades) fit here. Marking a real message as spam can be problematic because the recipient may miss something important, letting the occasional spam message through wastes a few seconds. Blocking a hundred spam messages a day is a huge productivity win. You can tune the probabilities to hit this kind of threshold. And you can't easily write a rule-based algorithm for spotting spam because spammers will adapt their behaviour.
Translating a menu is probably fine, the worst that can happen is that you get to eat something unexpected. Unless you have a specific food allergy, in which case you might die from a translation error.
And that's where I start to get really annoyed by a lot of the LLM hype. It's pushing machine-learning approaches into places where there are significant harms for sometimes giving the wrong answer. And it's doing so while trying to outsource the liability to the customers who are using these machines in ways in which they are advertised as working. It's great for translation! Unless a mistranslated word could kill a business deal or start a war. It's great for summarisation! Unless missing a key point could cost you a load of money. It's great for writing code! Unless a security vulnerability would cost you lost revenue or a copyright infringement lawsuit from having accidentally put something from the training set directly in your codebase in contravention of its license would kill your business. And so on. Lots of risks that are outsourced and liabilities that are passed directly to the user.
And that's ignoring all of the societal harms.
[1] My favourite of these is actually very old. The hyphenation algorithm in TeX trains short Markov chains on a corpus of words with ground truth for correct hyphenation. The result is a Markov chain that is correct on most words in the corpus and is much smaller than the corpus. The next step uses it to predict the correct breaking points in all of the words in the corpus and records the outliers. This gives you a generic algorithm that works across a load of languages and is guaranteed to be correct for all words in the training corpus and is mostly correct for others. English and American have completely different hyphenation rules for mostly the same set of words, and both end up with around 70 outliers that need to be in the special-case list in this approach. Writing a rule-based system for American is moderately easy, but for English is very hard. American breaks on syllable boundaries, which are fairly well defined, but English breaks on root words and some of those depend on which language we stole the word from.
LibreOffice 25.2.6 Rolls Out with 37 Bug Fixes
#Linux
https://linuxiac.com/libreoffice-25-2-6-rolls-out-with-37-bug-fixes/
이유가 있어서 업어옴
ゆめみにいた人たちはどうなるんだろ……
RE: https://mastodon.social/users/htby/statuses/115270153550109822
別子銅山でバカほど階段上がったし歩いたのでバカみたいなご飯を食べることをお許しください
アクセンチュア、1300億円規模のリストラ計画発表 人員1万人減：日本経済新聞
https://www.nikkei.com/article/DGXZQOGN25D650V20C25A9000000/
今日プリンを食べるという
Today we did a beer stop at thé Tolkien's Bar in Zagreb.
헌법에 검찰이라는 단어는 검찰총장 임명 관련 조항 하나밖에 없는데, 이게 검찰청의 존속 이유가 될 수가 있나? 기소청의 청장을 검찰총장이라는 직명으로 부르면 끝 아닌가.
明日フェリー乗る時間以外ガチ目にノープランなんだよな どうしよ〜
미스키에 뭔가
한 노트만 타임라인에 계속 반복으로 보여주는 버그가 있는 것 같은데
재현 조건을 모르겠네 새로고침하면 사라짐
If you're a UK citizen, please sign this petition against introducing mandatory digital ID. At 800,000 signatures currently
모니터 TV는 모니터 크기의 TV라서 KBS 수신료 지불 의무가 있습니다.
디지털 튜너 TV가 없어도 아날로그 튜너 TV를 가지고 있다면 놀랍게도 KBS 수신료 지불 의무가 있습니다. 
집에 TV가 없는데 TV 수신 카드가 있다면 의외로 KBS 수신료 지불 의무가 없습니다. 
집에 TV가 없는데 지상파 DMB 디바이스를 가지고 있다면 KBS 수신로 지불 의무가 없습니다.
단호박, 두부, 미소, 페페론치노랑 드라이 허브가 들어간 마늘콩피를 갈아서 크림 없는 크림파스타를 만들었다. 적당히 머릿속에서 이 조합이면 괜찮겠다 싶어서 만들었는데 진짜 너무 맛있다.
두부랑 올리브유, 마늘을 갈아버리는게 약간 허머스 같은 느낌도 나서 빵같은데 찍어먹기도 좋아보인다.
fistbump !!! #SealFedi
レモンサワーと揚げ物の相性は異常
いいな〜〜〜BST
唐揚げでハイボールとかぐいぐい飲みたい
ハイボールが止まらない！黄金色の唐揚げナイト｜estpls https://note.com/maniacs_n/n/na7ba722a1dc1?sub_rt=share_pw
ムシャムシャしてやった、半生はしていません。
明日から東京方面へ
誰も人がいない、不思議な感覚
#misskey写真部 #photography
塗り〜 #qoo3_brocante
people of bluesky, I enjoy hearing from you, and bridgy has started sending me links when people who are not bridged @ me from bluesky so that I can notice them. However, keep in mind that I can't reply unless you're also bridged! https://fed.brid.gy/docs#bluesky-get-started
しごおわすきー
ミスドの新メニュー全部買った😤
@windy@iqhina.org
MSN 메신저 -> 스카이프 -> 마이크로소프트 Teams
그렇게 됐습니다
대방어 (Great Defense)
메탈기어를 만드는 쪽은 악역이라구
모니터 TV는 모니터 크기의 TV라서 KBS 수신료 지불 의무가 있습니다.
디지털 튜너 TV가 없어도 아날로그 튜너 TV를 가지고 있다면 놀랍게도 KBS 수신료 지불 의무가 있습니다.
집에 TV가 없는데 TV 수신 카드가 있다면 의외로 KBS 수신료 지불 의무가 없습니다.
집에 TV가 없는데 지상파 DMB 디바이스를 가지고 있다면 KBS 수신로 지불 의무가 없습니다.
미스키에 뭔가
한 노트만 타임라인에 계속 반복으로 보여주는 버그가 있는 것 같은데
재현 조건을 모르겠네 새로고침하면 사라짐
Amazon WorkSpaces에서 인터넷 접속이 안 될 때는?
https://dev.classmethod.jp/articles/jw-troubleshooting-amazon-workspaces-internet-issues/
어류음식사진
빨리추워졌으면좋겟다 방어먹게
UK Petition against introducing Digital ID cards.
https://petition.parliament.uk/petitions/730194
EDIT : 7:10am it's at 526K votes, lets get it to at least 1M.
8:27am it's now 600K 👏👏👏
2:53pm 1M achieved! Lets go!!! 👏👏👏 Lets aim for 2M now.
Sat 27th 8:45am and we're at over 1.6M. (1,621,180). 2M might be achievable today! 😀 Well done folks. 👏👏👏🤗Here's hoping we find more folk who haven't signed yet, and support this.
Sat 10pm 2M achieved! 👏👏 Now Let's get 3M, you legends. 👏🤗
夜
呑んだ
오인페를 살 것이야
夕凪兎
DevelopersIO 
わしゃ
리피 
とるねど
Glyph
Danny Garside
4/12 
開始！！