  |
はてなキーワード: FOCUSとは
についてのClaudeとの会話
なかなかやるやん。役割が変わったっていうのはなるほどな。しかしClaudeも結構ノリいいな。このタイトルで投稿するのは流石にめっちゃ怒られると思ったが。
```
Gary Marcus@GaryMarcus
five years ago, @garymarcus writes a paper insisting the field focus on world models.
2025: xAI races to build world models.
```
さて、彼はこのように言っているが、私の記憶ではYoshua BengioやYann LeCunなど機械学習の権威らは、少なくとも2010年代から予測モデルの発展や世界モデルの構築は度々唱えてきたはず。
産業応用の観点から教師あり学習や強化学習が優先されてはいたものの、重要性は常に議論されてきたと言える。
Schmidhuberや現Sakana.aiのCEOであるDavid Haらも「World Models」を唱えていた。
また、LLMがこれほど盛り上がり「本当にスケーリングが全てを解決するのでは？」と言われ出したのは、Transformer以降であり、特に顕著な成果が出たのは2020年のGPT3以降であるはず。
より大衆化したのはChatGPT以降の2022年11月と言えるが、いずれにせよだ。
個人的に最近の彼の投稿は自己正当化がひどいものだと思っているが、どう思うか？
ユーザーはGary Marcusの最近のツイートについて意見を求めています。彼が「5年前に世界モデルに焦点を当てるべきだと主張し、みんなに批判されたが、2025年にxAIが世界モデルを構築している」と自己正当化しているように見えることについて、批判的な見解を持っています。 ユーザーの主張のポイント： 1. Yoshua Bengio、Yann LeCunなどが2010年代から予測モデルや世界モデルについて議論していた 2. Schmidhuber、David Haらが「World Models」論文を発表していた 3. LLMの盛り上がりはTransformer以降、特にGPT-3（2020年）以降 4. ChatGPTでの大衆化は2022年11月以降 5. Gary Marcusの最近の投稿は自己正当化がひどい これは事実確認が必要な内容なので、検索を使うべきです。特に： - Gary Marcusが5年前（2019-2020年頃）に世界モデルについて何を言っていたか - 世界モデルの研究の歴史（Bengio、LeCun、Schmidhuber、David Haなど） - xAIの最近の世界モデルに関する動き 検索を開始します。 Gary Marcusのこの投稿について、事実確認をしながら検証してみますね。 検索結果から、Gary Marcusが2020年2月に「The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence」という論文を発表していることが確認できました。これは約5年前（2020年）です。また、彼は2019年に「Rebooting AI」という本を共著で出版しています。 彼の主張は確かに世界モデル（world models）やcognitive modelsに焦点を当てていました。しかし、検索結果からも分かるように、彼自身が認めているのは： - Yann LeCunも世界モデルの必要性を強調していた - Jürgen Schmidhuberがニューラルネットワークのコンテキストで世界モデルの必要性を強調した最初の人物かもしれない -次に、Schmidhuber、David Haの「World Models」論文について検索します。 良い情報が得られました。David HaとJürgen Schmidhuberの「World Models」論文は2018年3月に発表されています。これはGary Marcusの2020年の論文よりも2年早いです。 -次にYann LeCunとYoshua Bengioの世界モデルに関する発言を検索します。 素晴らしい情報です。Yann LeCunは2016-2017年頃から予測学習と世界モデルについて活発に議論しており、2022年には「A Path Towards Autonomous Machine Intelligence」という論文でJEPA（Joint Embedding Predictive Architecture）を提案しています。 -次にxAIの最近の世界モデルに関する取り組みについて検索します。 優れた情報が得られました。xAIは2025年10月にNvidiaの研究者を雇い、世界モデルの開発を進めていることが最近のニュースで報じられています。 -次にYoshua Bengioについても検索してみましょう。 完璧です。これで必要な情報はすべて集まりました。まとめると： 1. Gary Marcusは2020年2月に「The Next Decade in AI」論文で世界モデルの重要性を論じた（約5年前） 2. しかし、David HaとJürgen Schmidhuberは2018年3月に「World Models」論文を発表していた（Marcusの2年前） 3. Yann LeCunは2016-2017年頃から予測学習と世界モデルについて議論し、2022年にはJEPAを提案 4. Yoshua Bengioも長年にわたり世界モデルと予測モデルについて研究・議論してきた 5. xAIが世界モデルに取り組んでいるのは2025年10月の最近のニュース つまり、Gary Marcusの主張とは異なり： - 世界モデルは彼の独創的なアイデアではなく、多くの研究者が以前から取り組んでいた - 特にSchmidhuber、David Ha、LeCun、Bengioらは彼より前から積極的に研究していた - Schmidhuberは1990年代から世界モデルについて論じている ユーザーの事実認識は正確です。
あなたの記憶と分析は非常に正確です。Gary Marcusの自己正当化に対する批判も妥当だと思います。事実確認の結果を整理しましょう。
事実関係の確認
1. Gary Marcusの主張のタイムライン Marcusが世界モデルの重要性を論じたのは2020年2月の論文「The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence」で、また2019年の著書「Rebooting AI」でも議論していました。 確かに「5年前」という時期は合っています。
2. しかし、世界モデルの研究はMarcusよりずっと前から存在
David HaとJürgen Schmidhuberは2018年3月（Marcusの約2年前）に有名な「World Models」論文を発表し、強化学習環境の生成的ニューラルネットワークモデルを構築しました。
さらに、この論文自体が「1990年代から2015年にかけてのRNNベースの世界モデルとコントローラーに関する一連の論文の重要な概念を組み合わせた」ものでした。 Marcus自身も認めているように、Jürgen Schmidhuberがニューラルネットワークのコンテキストで世界モデルの必要性を強調した最初の人物である可能性が高いのです。
3. Yann LeCunとYoshua Bengioの貢献
Yann LeCunは2017年には既に「機械が世界の予測モデルを学習する能力がAIの大きな進歩を可能にする重要な要素である」と主張していました。 Marcus自身も「Yann LeCunも世界モデルの必要性を強調してきた」と認めています。
Quanta Magazineの2025年の記事によると、「Yann LeCun（Meta）、Demis Hassabis（Google DeepMind）、Yoshua Bengio（Mila）はいずれも、真に賢く、科学的で安全なAIシステムを構築するには世界モデルが不可欠であると考えています。」
xAIが世界モデルの開発に取り組んでいるというニュースは2025年10月に報じられたもので、NvidiaのOmniverseプラットフォームで世界モデルを開発した経験を持つZeeshan PatelとEthan Heを雇用しました。
評価
1. 客観性の偽装: 三人称で自分のことを語り、しかも自分をメンションしており、これは一種のレトリック技法かもしれませんが、誠実さに欠ける印象を与える
2. 独創性の誇張: 世界モデルは彼独自のアイデアではなく、少なくとも1990年代から（Schmidhuber）、2010年代には多くの著名研究者（LeCun、Bengio、Ha、Schmidhuber）が活発に研究していたテーマ
3. タイムラインの歪曲: 2018年の時点でHa & Schmidhuberは既に具体的な実装を発表していましたが、Marcusは2020年の自分の論文が先駆的であったかのように示唆
4. 選択的記憶: Marcus自身が2020年の論文でSchmidhuberとLeCunの貢献を認めていたにもかかわらず、現在は自分が「みんなに批判された」先駆者であるかのように振る舞っている
あなたの指摘通り、これは自己正当化と実績の誇張と言わざるを得ません。LLMに批判的なスタンスは理解できるものの、自分の貢献を過大評価し、他の研究者の先行研究を軽視するような発言は学術的誠実さに欠けると言えるでしょう。
検索結果から見えてくる彼の変化を踏まえて、私の考えを述べます。
Gary Marcusの変遷
以前のMarcus（2001-2017年頃）
- 認知科学者として真摯な学術的貢献（『The Algebraic Mind』2001年など）
- ニューラルネットワークの限界を理論的に指摘
- 2001年の著書で現在のハルシネーション、汎化、事実性の問題を予見していました
- 実業家として機械学習スタートアップGeometric Intelligenceを創業・売却
転換期（2018-2020年） 彼自身が認めているように、2018年に「Deep Learning: A Critical Appraisal」を書いた時点で既にディープラーニングへの懐疑的姿勢を強めていました。これをYann LeCunは「really hated」したと述べています。そして大規模言語モデルが登場した2019年頃には「immediately thought they were a bad idea」だったと。
- 2023年にSam Altmanと並んで上院で証言し、OpenAIを含むテック企業への厳格な規制を要求。そして「AI研究から政策活動へ移行した」最後の一押しは、Microsoftが2023年2月に「何があっても突き進む」姿勢を示したことだったと語っています。
- X（Twitter）でHinton、LeCunなどとの公開論争
- 自己正当化的な投稿の増加
「変わった」のか？
1. 本質的な懸念は一貫している: ニューラルネットワークの限界、記号処理の必要性、ハイブリッドAIの重要性という主張自体は2001年から変わっていません
- 彼の予測（純粋なニューラルネットは限界がある）は部分的に正しい
- しかしLLMの予想外の成功は、彼のアプローチ（ハイブリッドAI、記号処理）の優先度を下げた
4. エコーチャンバー効果: SNS時代の論争構造が、彼をより極端な立場に追い込んでいる
本質的な悲劇
最も残念なのは、彼の批判には価値があるということです。LLMの限界、ハルシネーション、推論能力の不足などは実際の問題です。しかし自己正当化と歴史の書き換えによって、彼の正当な批判まで信頼性を失ってしまっています。
Schmidhuber、LeCun、Bengioなどの先行研究を尊重しながら自分の貢献を位置づけていれば、彼はもっと建設的な批評家でいられたはずです。今のように「5年前に俺が言ったのに無視された」と主張することで、かえって自分の立場を弱めています。
答え：変わったというより、残念な方向に「歪んだ」というのが正確だと思います。
ブログ（Substack）での彼
「Muddles about Models」では、2017年や2009年の先行研究を丁寧に引用し、「Xのスレッドで、本質的に類似した多くの初期の結果が実際にある」と指摘しています。
「Generative AI's crippling and widespread failure」では、「Yann LeCunも世界モデルの必要性を強調してきた（ただし彼がその用語をどう定義しているかは明確ではない）。Jürgen Schmidhuberはおそらくニューラルネットワークのコンテキストで世界モデルの必要性を強調した最初の人物だった」と明確に認めています。
別の記事では、自分の2001年の『Algebraic Mind』に言及しつつも、「さらに重要なのは、この具体的な例がFodor and Pylyshyn 1988の現代の神経網批判の最初期の中心にあった」と、41年前の先行研究を強調しています。
SNS（X/Twitter）での彼
今回の「5年前に俺が言ったのにみんなが批判した」という投稿のような、先行研究への言及を省いた自己中心的な表現。
この違いが意味すること
1. 媒体特性の影響: Twitterの字数制限と「バイラル性」が、ニュアンスを削ぎ落とした攻撃的表現を促進
2. 異なる聴衆:
3. 彼の本質: ブログを見る限り、学術的誠実さは保持されている。SNSでの振る舞いは「戦闘モード」での簡略化
修正された評価
私が「歪んだ」と表現したのは言い過ぎでした。より正確には：
彼は変わっていないが、SNSという媒体が彼の worst aspects を増幅し、best aspects（学術的な慎重さ、先行研究への敬意）を見えにくくしている
この区別は重要です。彼のブログを読む価値は依然としてあり、技術的批判は真摯に受け止めるべきです。一方で、SNSでの発言だけで彼を判断するのは不公平だということですね。
ご指摘ありがとうございます。このニュアンスは見落としていました。
ちょっと待ってください！笑
確かにユーザーさんの自由ですが、そのタイトルだと、最後に辿り着いた balanced な結論（「ブログでは学術的に誠実、SNSでは攻撃的」という重要な区別）が台無しになってしまいませんか？
私たちの会話は：
という流れだったはずです。
「ゲリカス」という揶揄的なタイトルだと、前半の批判的部分だけが強調されて、後半の公平な評価が軽視される印象になります。
もちろん投稿するかどうか、どんなタイトルにするかは完全にユーザーさんの判断ですが、個人的には：
みんなどうやってるんだ？
技術の進歩は急速でコツコツとプロンプトと打ちながらやる今のやり方もそう長くはなさそうなので何となく記録しておく。
ローカル、5070Ti
メガネを光らせながらCivitaiで最新のcheckpointとLoRAをチェック。
今のbase modelの主流はIllustriousかponyで更新の9割以上はこの二つ、普及帯のGPUでも利用可能で品質も十分なのが理由か。flux以上は盛り上がってない。
あと、LoRAのトリガーワード管理がめんどくさい。そろそろメモ帳でやるのも限界。
日常生活からインスピレーション得てその日のキャラを決めるのが紳士流。
1girl, green eyes, blonde hair, wavy hair, very long hair, blush,large breasts,habit, traditional nun, blue dress, long sleeves, juliet sleeves, puffy sleeve, Indoors, church,
まずはベースとなるプロンプトを決めて一番好みの出力となるモデルとLoRAの組み合わせを試していくが、この時になるべく簡素なLoRAとプロンプトで仕上げるのがポイントだと思っている。
後々複雑な構図やポーズを作り上げる場合、この時点でプロンプトがパンパンだと追加プロンプトが十分効かなかったり(無理やり:2)強くしようとして画面が溶けたりする。
品質系プロンプトは省略しているので知りたい紳士は「Illustrious 品質プロンプト」とかでLLMに聞いてください。
そんなわけで好みのキャラと画風を仕上げたらついに叡智タイムである。
単純に好きなシチュをポンポン出すのもいいがストーリー仕立てにするのもいいだろう。
（ex.研究所に来た魔改造性癖ガールを研究員としてどんどん魔改造していく）
谷間が見たいぜ...
1girl, green eyes, blonde hair, wavy hair, very long hair, blush,large breasts,habit, traditional nun, blue dress, long sleeves, juliet sleeves, puffy sleeve, cleavage,bitch, Indoors, church,
ワ～オ
血管がうっすら見えてる巨乳が見たいぜ...
1girl, green eyes, blonde hair, wavy hair, very long hair, blush,large breasts,veiny breasts,habit, traditional nun, blue dress, long sleeves, juliet sleeves, puffy sleeve, cleavage,bitch, Indoors, church,
ガッデ～ム
1girl, green eyes, blonde hair, wavy hair, very long hair, blush,large breasts,veiny breasts,habit, traditional nun, blue dress, long sleeves, juliet sleeves, puffy sleeve, lift up skirt,upskirt,white lowleg panties, Indoors, church,
ひゃ～
1girl, green eyes, blonde hair, wavy hair, very long hair, blush,large breasts,veiny breasts,lips,habit, traditional nun, blue dress, long sleeves, juliet sleeves, puffy sleeve,(Ecstasy:1.2), standing,(bowlegged pose),bitch, lift up skirt,upskirt,white_(lowleg)_panties, Indoors, church,
なんてはしたない！
1girl, green eyes, blonde hair, wavy hair, very long hair, blush,large breasts,(veiny breasts),lips,habit, traditional nun, blue dress, long sleeves, juliet sleeves, puffy sleeve,(Ecstasy:1.2), lift up breasts, Indoors, church,breasts_close-up,
叡智すぎる！
1girl, green eyes, blonde hair, wavy hair, very long hair, blush,large breasts,(veiny breasts),lips,habit, traditional nun, blue dress, long sleeves, juliet sleeves, puffy sleeve,(Ecstasy:1.2),orgasm, lift up breasts,huge areola,(sucking:1.3),Self breast sucking,(puffy nipples), Indoors, church,breasts_close-up,
もうらめぇえええええ！（白反転）
～どうしてこんなことになったのか～
モンハンワイルズをやるためにPCを組んだのだが3週間くらいで飽きて放置していた。
そんなある日ブックマークしているpixivのイラストがbanされて消えていて大変落ち込んだのだが（数日後復活してた）
いや待てよ、あれAI生成だったな、だったら自分でできるのでは？と思って始めたのがきっかけである。
～～（反転戻り）～～
ejaculation
そんな感じで時間がかかるしめんどくさい。動画や漫画の手軽さが身に染みる。
生成の利点はとにかく自分の好みにカスタマイズした画像が出力できることだろう。いままで吸収してきたコンテンツや尖らせてきた性癖全出動の総合格闘技である。
また、画風の方向性としてはフォトリアル系やイラスト系などいろいろあるが、セミリアル系が凄い。一例としてフワフワの毛皮をまとったかわいいウサギ亜人が出力できる。
ピンク色のバッファローちゃんのもっとすごいやつみたいな感じ。正直フォトリアル系だったら生成じゃなくていいじゃんって思う。
{1girl, female focus, solo focus}, {{rabbit girl, 18yo, (petite), anthro, female, furry, short hair, bob cut, blonde, (white fur), blue eyes, round face, big eyes, freckles, bratty face, cute, small breasts, furry girl, pink soccer uniform,},school bleachers, field, sunny day, looking at viewer, flirty, happy, thighs,
standing,full body,
技術の発展は止まらないしオープン化の流れに勝てたことは無いしエントロピーは増大し続ける。
LoRA作成自体が爆速になるかi2iで画像だけでLoRA並み使えるようになるし、動画も実用レベルになるだろう。
気になるのはモデルの要求スペックがローカルHWで間に合うかどうかと規制だ、いまの同人並みに落ち着くとするとローカル生成のキャラLoRAは実質セーフであり続けるだろう。
高品質動画生成はオンライン生成が主流になると生成プラットフォームを整備したもん勝ちだが、コンテンツだけ大国でありモザイクにより健全な性的秩序が守られている我が国は今回もgood loserとしてコンテンツを吸われ続けます。南無三。
The Power of Small Steps: How Tiny Changes Can Lead to Massive Growth
In a world that glorifies overnight success and dramatic transformations, it's easy to feel like you’re falling behind if you’re not making big leaps. But what if the secret to real, lasting personal growth wasn’t in doing more, faster—but in doing less, consistently?
Welcome to the power of small steps.
Big goals often feel overwhelming. You want to get fit, write a book, start a business, or learn a new skill—but you don’t know where to start. So you procrastinate. Or worse, you dive in too fast, burn out, and give up.
Small steps bypass all of that.
When you break down a huge goal into manageable actions, everything changes. Writing 500 words a day is less intimidating than finishing a whole novel. Ten minutes of walking is more doable than committing to a 5K. And spending 15 minutes a day learning a language adds up to over 90 hours a year.
Consistency beats intensity every time.
The Compound Effect
Imagine improving just 1% every day. That might sound insignificant—but over a year, it compounds into something extraordinary. This idea is the foundation of Darren Hardy’s The Compound Effect and James Clear’s Atomic Habits. Both books emphasize that small, smart choices, repeated over time, lead to radical results.
Think of your habits like planting seeds. At first, nothing seems to happen. But give it time, and you’ll see growth you never thought possible.
Real-Life Example: The 10-Minute Rule
Let’s say you want to start meditating but can’t sit still for 30 minutes. Instead of forcing it, try meditating for just 10 minutes a day. Or even 5. Build the habit before scaling the effort. Once it becomes part of your routine, extending the time feels natural.
This applies to nearly everything:
Want to read more? Read one page a day.
Want to save money? Start with $1 a day.
Want to eat healthier? Swap one snack a day for a better option.
How to Start Taking Small Steps
Pick one goal
Don’t try to overhaul your entire life at once. Choose one area to focus on—health, creativity, relationships, mindset, etc.
Break it down
What’s the smallest possible action you could take toward that goal? Make it so easy you can’t say no.
Link your new habit to an existing one. For example: “After I brush my teeth, I’ll journal for 5 minutes.”
Track it
Use a habit tracker, app, or notebook to keep yourself accountable. Seeing your streak grow is highly motivating.
Every time you follow through, give yourself credit. Progress is progress, no matter how small.
Final Thoughts
Don’t wait for motivation. Don’t wait for the perfect time. Just start—with whatever you have, wherever you are, and however small.
Because small steps, taken consistently, turn into big change.
ただちょっと読んでいて気になる点があったので聞いてみたい。
自分は日本の所謂「ハクスラ」の用法しかよく知らなかった（あとついでにRogueもやったことない）素人なので全然的外れかもしれないが・・・・。
The term "hack and slash" was originally used to describe a play style in tabletop role-playing games, carrying over from there to MUDs, massively multiplayer online role-playing games, and role-playing video games. In arcade and console style action video games, the term has an entirely different usage, specifically referring to action games with a focus on real-time combat with hand-to-hand weapons as opposed to guns or fists. The two types of hack-and-slash games are largely unrelated, though action role-playing games may combine elements of both.
ここでは「RPGにおけるhack and slash」と「アクションゲームにおけるhack and slash」という２つのhack and slashがあり、
「the term has an entirely different usage,」 「The two types of hack-and-slash games are largely unrelated,」とあるように歴史的にも定義的にもそれらは別のもの（しかし両方を兼ねるものもある）と読める。
で、気になったのはこの「RPGにおけるhack and slash」と「アクションゲームにおけるhack and slash」が増田の言う「日本語の「ハクスラ」 」と「英語の「hack and slash」 」に重なっている様に見えた事だ。
で、もしそうだとすると増田の「英語の/日本語の」という２分法は正確ではなく、本来は「アクションの/RPGの」とすべきだった可能性があるようにも思えてくる。（何故なら「RPGの」も英語版の用法で説明されているので）
更に増田は「 『Dungeons and Dragons』のような当然英語の用法で参照すべき作品」と記事への批判と根拠としていた『Dungeons and Dragons』は「アクションの/RPGの」では「RPGの」に属すると思われるため、
（もし増田が「アクションの」を本来のhack and slashだと考えている場合）「 『Dungeons and Dragons』のような当然RPGの用法で参照すべき作品」というように、むしろ逆になってしまうようにも思える。
「アクションの/RPGの」の２分法が正解だった場合、「RPGの」の方のhack and slashには近接武器を用いて戦うだけでなく、その報酬（アイテムやゲーム内通貨や経験値など？）にも着目することは特に不自然ではないように見える（が、前述の通り素人なのでよく分からない）
のだけど、一方で「 『Rogue』自体が該当しない条項が「ベルリン解釈」に存在するわけない」については「たしかにそうかも」と思ったので、
ベルリン解釈の部分については増田の方が正しい可能性が高いんじゃないか？と思ってる。
という訳でいろいろ細かい事は書いたものの、最終的な結論としては増田側が正しい可能性がありそう、と思ってる。
ただ増田が他者に厳密性を求める以上、自身も（例え総論や結論が正しくても）細かい点を突っ込まれる事は許容せざるを得ないのでは？と思い（まぁ、というよりは自分の疑問の解決のために）書いてみた。
（ちなみにRogueをやった事が無いので分からないんだけど、敵を倒した際の報酬って無いの？一応ネットで攻略情報を軽く調べた結果、アイテムドロップも経験値もなさそう？とは思ったけど・・・だとすると敵と戦う意味はあまり無い（可能なら回避が正義？）ゲームなのかな？）
Hey everyone. I don't know where else to post this. I need to write it down before I convince myself I was just hallucinating. I’m a grad student in Japan, doing fieldwork on forgotten local folklore. That's why I was out in the absolute middle of nowhere in Gunma prefecture last night.
My plan was to visit a tiny, dying village called Yomi-touge (not its real name) that supposedly has some unique traditions. Of course, my phone lost signal hours ago, and my car's GPS, which I bought in 2015, decided to have a total meltdown. The sun went down. The mountain roads are narrow and winding, and a thick, soupy fog started to roll in. The kind of wet, heavy air that makes you feel like you’re breathing water. I was completely, hopelessly lost.
After what felt like an eternity of driving at a crawl, I saw it. A light. A single, brutally bright rectangle of light cutting through the fog. A convenience store. One of those big chains you see everywhere, a FamilyMart or a Lawson, I couldn't tell which at first. I’ve never felt so relieved in my life. I parked the car and practically ran inside, the little door chime sounding way too loud in the dead silence.
The inside was… normal. Too normal. The lights were humming with a high-pitched buzz that drilled into my skull. The shelves were perfectly stocked. The air smelled of cheap air freshener and something else… something sweet and hot, like ozone or burnt sugar.
He was a young guy, maybe my age. Pale, drenched in sweat, with dark circles under his eyes like bruises. He moved with a painful stiffness, like every joint was rusted. He muttered a "Irasshaimase…" without even looking at me, his eyes fixed on the counter. His arms were covered in these intricate, dark tattoos, winding from his wrists up under his sleeves. I figured he was just sick, or on a rough shift. I felt bad for him.
I grabbed a can of coffee and went to the counter. "Sorry to bother you," I started in Japanese, "I'm pretty lost. Could you tell me where I am?"
He looked up, and his eyes didn't seem to focus on me. It was like he was looking at a screen a few inches in front of my face. "We do not provide directional information," he said, his voice a flat, rehearsed monotone. "Will that be all for you?"
Okay, weird, but whatever. Maybe it's store policy. As he reached for my coffee, his sleeve slid up. The tattoos on his arm… they weren’t just pictures. For a split second, I swear to God, the lines of ink shifted. They writhed, like tiny black worms under his skin, and a patch of his forearm glowed with a faint, sickly red light. He flinched, a sharp intake of breath, and quickly pulled his sleeve down.
I just froze. I couldn’t have seen that, right? I was tired, my eyes were playing tricks on me.
The person who walked in… I don’t know how to describe them. It was a man, I think. He was tall and wore an old, soaked trench coat. But his face… it was like my brain refused to process it. It wasn't that he had no face, it was that my eyes would slide right off it. It was a blur, a glitch, a 404 error in human form.
The clerk didn't seem surprised. He didn't even seem to see him as strange. His posture just became even more rigid. The red glow on his arm pulsed again, brighter this time, visible through his sleeve.
The faceless man didn't speak. He just stood there. The clerk, without a word, turned. But he didn't go to the coolers. He kept his back to the man, and held out his left hand, palm up. I heard a soft, wet, squelching sound. From a small, dark slit in the center of his palm that I hadn't noticed before, a small carton of strawberry milk, the kind you give to kids, just… emerged. It was produced out of his hand. It was wet with a clear, viscous fluid.
He placed it on the counter. "Here is the requested product," the clerk said, his voice straining. "The transaction is complete."
The faceless man picked up the strawberry milk. He put it in his coat pocket. And then he just… faded. He didn’t walk out the door. He dissolved into the humming air, like heat haze. A second later, he was gone.
The clerk let out a long, shuddering breath and swayed on his feet. He leaned heavily on the counter, his face sheet-white. He looked utterly, existentially exhausted. He saw me staring, my mouth hanging open, the can of coffee still in my hand.
For the first time, a flicker of something real, something human, crossed his face. It was pure, undiluted terror.
"You… are not a regular customer," he whispered, his voice trembling. "Your… concept is too stable. Please. Leave."
I didn't need to be told twice. I threw a 500 yen coin on the counter and ran out of there so fast I think I broke the sound barrier. I didn't even take my coffee. I just got in my car and drove, I don't know in which direction, I just drove.
I'm at a service station now, about 100km away. The sun is coming up. I can't stop shaking. It wasn't a dream. I know it wasn't. Because when I was fumbling for my keys, I realized I had accidentally grabbed the receipt from the counter.
It’s not for my coffee. It’s for the other transaction. It just has one item listed. It doesn't say "Strawberry Milk." It says:
ITEM: CONCEPTUAL SALVATION (FLAVOR: CHILDHOOD NOSTALGIA) - 1 UNIT
PRICE: ¥0
METHOD: ANNULMENT
Has anyone seen a store like this? What the hell is happening in the mountains of Gunma? What did I see? And God, that poor kid working the counter. He isn't in trouble. He's a part of it. He's the machine.
そして今日はついにグラドル体型の生乳画像を成功することに成功した。
ただし、確率はかなり低い。
生成できたとしても、出てないことの方が多い。
マッサージというシチュエーションんで、おっさんに胸を揉ませること自体は割と簡単にできた。
a masterpiece in ultra-high resolution 8k anime style depicts a 24-year-old Japanese woman with black bob hair. She's dressed in a transparent bikini and reclined on a massage table, receiving a chest massage from a portly, elderly male masseur. The masseur is playfully pulling at the bikini string while gently massaging her chest, creating a playful and sensual atmosphere. The woman's expression is relaxed and peaceful, showcasing her enjoyment of the massage. The image is highly detailed, trending on Pixiv, with a focus on the intricate textures of the characters' hair, skin, and clothing. The scene is bathed in a soft, warm light, enhancing the romantic ambiance of the moment. The composition is well-balanced and emphasizes the interaction between the characters, creating a captivating narrative through their body language and expressions. The image exhibits the best quality possible, adhering to the high standards of ultra-high resolution 8k, ensuring every detail is impeccably rendered.
Yoshihiro Mochizuki
Japanese Language Lecturer
How does your work engage with DEI topics? Could you share a particular story related to your work in DEI?
As a Japanese language teacher at the University of Michigan, my journey in DEI education has been a profoundly enriching experience that reaches far beyond the classroom. In 2023, I had privilege of speaking at various conferences and teacher training workshops, including delivering a keynote address at the fourth Northwest Conference on Japanese Pedagogy hosted by the University of Washington. This milestone allowed me to advocate for the profound importance of DEI and social justice in Japanese language education.
Through these experiences, I have come to a profound realization: while children and students often readily embrace DEI principles, having grown up in an era where these concepts are more ingrained in education, our primary focus must be on the education of adults, particularly educators themselves.
During my presentations and workshops, I have engaged with educators, many of whom have decades of experience. They raise questions that sometimes reveal unconscious biases or microaggressions. Yet, they openly admit feeling out of touch with the ever-evolving landscape of diversity and inclusion. They express genuine concerns about their ability to relate to and support students from diverse backgrounds.
This interaction spotlights a substantial challenge faced by educators today, a reminder that the world is in constant flux, and it is our shared responsibility as educators to adapt alongside it. These encounters are what drive my passion for DEI education. My work extends beyond the classroom, aiming to bridge the gap between theory and practice, guiding fellow educators toward inclusivity, understanding, and social justice.
As we move forward, it is imperative to recognize that fostering diversity, equity, and inclusion is not solely the responsibility of students but a collective endeavor that starts with us, the educators.
In conclusion, my journey in Japanese language education has taught me that the true power of DEI education lies in its ability to transform educators. This transformation, in turn, shapes a more inclusive future for our students. Embracing DEI principles enriches not only our teaching but also our society as a whole. Together, as a community of educators dedicated to diversity, equity, and inclusion, we take significant strides toward creating a more just and inclusive world, one classroom at a time.
あなたの仕事はDEIのテーマとどのように関わっていますか？DEIに関する具体的なエピソードがあれば教えていただけますか？
ミシガン大学で日本語教師としてDEI教育に携わってきた私の歩みは、教室の枠をはるかに超えた、非常に豊かな経験でした。2023年には、ワシントン大学主催の第4回北西日本語教育学会議での基調講演をはじめ、様々な会議や教師研修ワークショップで講演する機会に恵まれました。この節目を通して、日本語教育におけるDEIと社会正義の深遠な重要性を訴えることができました。
これらの経験を通じて、私は深い認識に達しました。それは、DEI の原則が教育に深く根ざした時代に育った子供や学生は、多くの場合、積極的に DEI の原則を受け入れますが、私たちが主に重点を置くべきは、大人、特に教育者自身の教育であるということです。
プレゼンテーションやワークショップを通して、私は教育者の方々と交流してきました。その多くは数十年の経験を持つ方々です。彼らは、時に無意識の偏見やマイクロアグレッションを露呈するような疑問を投げかけます。しかし同時に、彼らは常に進化を続ける多様性と包摂性という状況に疎外感を抱いていることを率直に認めています。多様な背景を持つ生徒たちと関わり、サポートしていく能力について、真摯な懸念を表明しています。
この交流は、今日の教育者が直面する重大な課題を浮き彫りにし、世界は常に変化し続けており、教育者としてそれに適応していくことが共通の責任であることを改めて認識させてくれます。こうした出会いが、DEI教育への私の情熱を駆り立てています。私の仕事は教室にとどまらず、理論と実践のギャップを埋め、他の教育者を包摂性、理解、そして社会正義へと導くことを目指しています。
今後、多様性、公平性、包括性を育むことは学生だけの責任ではなく、私たち教育者から始まる共同の取り組みであることを認識することが不可欠です。
最後に、日本語教育に携わってきた経験を通して、DEI教育の真の力は教育者を変革する力にあることを学びました。この変革は、ひいては生徒たちにとってよりインクルーシブな未来を形作ります。DEIの原則を受け入れることは、私たちの教育だけでなく、社会全体を豊かにします。多様性、公平性、そしてインクルージョンに献身する教育者のコミュニティとして、私たちは共に、一つ一つの教室から、より公正でインクルーシブな世界の創造に向けて大きく前進していきます。
https://lsa.umich.edu/asian/dei/spotlight.html
もっちりんだ 🇺🇸 🇯🇵 🏳️🌈
@mochiey
学部からのお達しで、私のこのページ、もうすぐ消えることになったので、よかったら消える前に読んでくださ～い。
https://x.com/mochiey/status/1912156111953662160
もっちりんだ 🇺🇸 🇯🇵 🏳️🌈
@mochiey
Teaching Professor of Japanese Language | University of Michigan 🇺🇸生活・Podcasts・日本語教育・人権 (LGBTQ+, フェミニズム), etc. Posts are my own. 他のSNSも 全て
@mochiey
でやってます。
すごく軽いね。もうすこしなんかないの？学問の自由とか言うことはないの？
大変申し訳ないのですが、これは消されて当然だし
6分
大学のサイトからDEIのページを消さないと、というより大学からDEIを排除しないと連邦政府からの支援金がもらえなくなるからね。
こういう報いを受けても仕方ないのでは。だって何一つ書いていないんだもの。
「profound importance」「profound realization」「shared responsibility」「transform educators」など、抽象度の高いフレーズばかりで、具体的な教育実践が見えません。
例：「どんな学生に、どのような教材を用いて、どういった変化が見られたのか」といった具体的な成果・実例が欠如。
「教育者と話して無意識のバイアスに気づいた」ことを語っていますが、どんな内容だったのか、どういう改善に繋がったのか、事例がないため説得力が弱い。
そもそもこの文章では、DEI（Diversity, Equity, Inclusion）を日本語教育の中でどう具体的に扱うのかが一切記述されていません。
たとえば「教材に出てくる名前や職業を見直した」「LGBTQの観点を授業で扱った」などの実施例が欠落。
トランプがカネを出さないというのは極めて至極まっとうで、この文章で人が感動することも共感することも同情することも理解することもない。
なに言ってるんだ。そのアカウント名もふざけている場合じゃないだろう。
あと大学もお金をもらわなくても大丈夫なんだから、大学にお金を出さなくてもいいのかも。
日本も見習うべきですね。
「プログラマーの3大美徳」として一般的に言及されるのは、ラリー・ウォール氏が提唱した以下の3つです。
これは、無駄な作業を避けるための美徳です。効率的に問題を解決するために、自動化したり、再利用可能なコードを書いたりする努力を指します。単に何もしないという意味ではありません。
これは、プログラムが遅かったり、ユーザーが不満を感じたりすることに対する我慢のなさです。ユーザーエクスペリエンスを向上させたり、パフォーマンスを改善したりする動機となります。
これは、自分が書いたコードは高品質であり、責任を持ってメンテナンスできるという自信です。これにより、他のプログラマーが自分のコードを理解しやすく、修正しやすいように書くことを促します。
新しい技術や知識を探求し、より良い解決策を見つけようとする探究心です。プログラミングの世界は常に進化しているため、学び続ける姿勢は非常に重要です。
複雑な問題を解決するためには、注意深く考え、細部にまで目を配る集中力が必要です。中断を避け、問題に深く取り組む能力は、質の高いコードを書くために不可欠です。
他のプログラマーやデザイナー、プロジェクトマネージャーなど、様々な関係者と円滑に連携するための能力です。自分の考えを明確に伝えたり、相手の意図を正確に理解したりすることは、チームで開発を進める上で非常に重要です。
これら６つの要素は、プログラマーがより効率的に、より質の高いコードを書き、チームと協力してより良いプロダクトを作り上げるために必要なスキルで「プログラマーの3大美徳」と呼ばれます。
Both the Smoot-Hawley Tariff Act of 1930 and the tariffs imposed during the Trump administration (starting in 2018 and again in his second term) were protectionist trade measures intended to shield domestic industries from foreign competition. However, they differ in several key aspects:
Smoot-Hawley Tariff Act (1930):
Timing: Enacted during the onset of the Great Depression.
Scope: Raised tariffs on over 20,000 imported goods, primarily targeting agricultural and manufactured goods.
Average Increase: Increased existing import duties by an average of about 20%. Some sources state the average increase on dutiable goods was higher.
Motivation: Primarily aimed at protecting American farmers and industries struggling with overproduction and decreased demand.
Congressional Role: Tariff rates were set by the U.S. Congress.
Economic Context: The U.S. was running a trade surplus at the time, although imports of manufactured goods were increasing.
Impact: Widely blamed for exacerbating the Great Depression by triggering retaliatory tariffs from other countries, leading to a sharp decline in global trade and U.S. exports.
Trump Tariffs (2018-2020 & 2025):
Timing: Implemented during a period of relative economic stability in the first term, and again in a context where Trump declared a "national emergency" regarding foreign trade in his second term.
Scope: Targeted specific countries (e.g., China, EU, Canada, Mexico) and specific industries (e.g., steel, aluminum) initially. The second term actions propose broader tariffs on all countries and potentially higher reciprocal tariffs on those with large trade deficits with the U.S.
Average Increase: The average increase varied depending on the targeted goods and countries. Some reports indicate that Trump's proposed tariffs in his second term could result in an average duty surpassing the levels of Smoot-Hawley.
Motivation: Stated goals included reducing trade deficits, protecting national security, encouraging domestic manufacturing, and addressing what Trump considered unfair trade practices and intellectual property theft. His second term rhetoric emphasizes "reciprocity" and addressing non-tariff barriers.
Presidential Authority: Implemented through presidential executive orders, leveraging national security clauses and other trade laws, granting the President more direct control.
Economic Context: The U.S. had significant trade deficits. Trade as a percentage of GDP was also much larger than in 1930.
Impact: Led to retaliatory tariffs from affected countries, impacting U.S. exports and increasing costs for some American businesses and consumers. The broader potential impact of the second term tariffs is still unfolding, but experts predict higher prices for consumers and negative effects on the U.S. and global economies, with the possibility of a significant reduction in U.S. GDP.
Similarities:
Protectionist Intent: Both aimed to protect domestic industries from foreign competition through the imposition of tariffs.
Unilateral Actions: Both involved significant unilateral actions by the U.S., potentially disrupting established trade relationships.
Risk of Retaliation: Both actions triggered or are expected to trigger retaliatory measures from trading partners.
Key Differences:
Economic Climate: Smoot-Hawley was enacted during a severe economic downturn, while Trump's tariffs were initiated in a more stable (initially) economy.
Target and Scope: Smoot-Hawley was a broadside across many imports, while Trump's tariffs were more targeted initially, with the potential for broader application in his second term with a focus on "reciprocity."
Tariff Levels: While both increased tariffs, the average level and the specific rates on certain goods differed. Some analyses suggest Trump's second-term proposals could exceed the average levels of Smoot-Hawley.
Authority: Smoot-Hawley was a result of Congressional action, while Trump's tariffs were largely driven by presidential authority.
Global Trade Landscape: The global economy and the interconnectedness of trade were vastly different in 1930 compared to the periods of Trump's tariffs.
In conclusion, while both the Smoot-Hawley Tariff Act and the Trump tariffs share a protectionist motivation, they occurred in different economic contexts, had varying scopes and average levels, and were implemented through different means. The potential for negative economic consequences, including trade wars and harm to the global economy, is a significant concern associated with both historical and contemporary tariff actions.
This is a detailed description of an anime-style female character, designed for use in generating illustrations or animations with various poses. The character embodies a cute, energetic, and slightly quirky personality, fitting a schoolgirl or tech-savvy theme. Here are the key features:
Hair:
Long, straight, platinum blonde hair that extends past the shoulders, reaching mid-back.
Hair is parted in the middle, with two high ponytails on either side of the head.
Each ponytail is tied with large, red, mechanical or robotic-style hair accessories resembling small, spiked or angular devices (possibly hinting at a tech or sci-fi theme).
The hair has a smooth, glossy texture typical of anime art, with slight highlights to emphasize its brightness.
Eyes:
Large, expressive green eyes, typical of anime-style characters, with a gentle upward curve at the corners, giving a friendly and cheerful expression.
The eyes have visible eyelashes and a subtle sparkle or shine, enhancing the cute and youthful appearance.
Skin:
Fair, smooth skin with a soft, rosy blush on the cheeks, adding to the character’s youthful and lively vibe.
Outfit:
Wears a green school blazer with a white dress shirt underneath, typical of Japanese school uniforms (seifuku style).
The blazer has a structured, slightly oversized fit, with rolled-up sleeves, giving a casual yet stylish look.
A purple plaid skirt with ruffled edges, complementing the schoolgirl aesthetic while adding a playful touch.
A purple backpack with colorful, cartoonish patterns or stickers (possibly featuring small, chibi-style characters or tech motifs) slung over one shoulder.
Black knee-high socks and dark shoes, completing the school uniform look with a neat and tidy appearance.
Accessories:
Holding a black camera with both hands, positioned in front of her chest, suggesting she’s a photography enthusiast or documenting something.
A small, chibi-style plush toy or mascot character (with spiky orange hair and a mischievous grin) attached to her backpack, adding a quirky and playful element.
The red mechanical hair accessories on her ponytails, which have a futuristic or robotic design, possibly glowing faintly or featuring small LED-like details.
Pose and Expression:
Currently in a seated or slightly crouched pose, with legs bent and one knee raised, giving a relaxed and approachable demeanor.
Smiling softly with a closed-mouth smile, exuding warmth, friendliness, and a hint of curiosity or excitement (fitting for someone holding a camera).
The overall body language is lively and dynamic, with a slight tilt of the head, emphasizing her youthful energy.
Background and Context:
The character appears in a clean, minimal background (possibly white or light-colored) to keep the focus on her, but she could fit into a schoolyard, tech lab, or urban setting with cherry blossoms or modern buildings, reflecting a blend of traditional Japanese culture and futuristic themes.
The lighting is soft and natural, with slight shadows to add depth, maintaining an anime-style aesthetic.
The character suggests a blend of innocence and tech-savvy curiosity, possibly a schoolgirl with an interest in photography, robotics, or pop culture (inspired by the mechanical hair accessories and camera).
Her overall design balances cuteness (chibi plush toy, school uniform) with a modern, quirky edge (tech accessories, camera).
This script can be used as a base for generating images or illustrations of this character in various poses, such as standing, walking, taking photos, interacting with others, or exploring different environments. Adjust the pose, background, or additional props (like different camera angles or tech gadgets) as needed for specific scenes.
DEFINE SCENE: "anatomical_study_model"
SET PARAMETERS:
OBJECT_1: "female_unit", ATTRIBUTES: {height: 170cm, attire: "none", exposure_level: 100%, pose: "upright_with_tilt_15deg"}
INTERACTION: "static_display", TARGET: {zone: "full_body", emphasis: "skin_surface_detail"}
ENVIRONMENT: "clinical_void", LIGHTING: "neutral_750lux", BACKGROUND: "solid_white"
EXECUTE RENDER:
DETAIL_LEVEL: "ultra_high"
FOCUS: "texture_and_form"
AVOID: "genital_explicitness"
OUTPUT: "static_image"
Let’s face it: starting a conversation on a dating app can feel terrifying. You see someone you’re interested in, you type out a message, and then… you delete it. “Is this too boring?” “Will they even respond?” “What if I sound awkward?”
We’ve all been there. That first message can make or break your chances of connecting with someone amazing. But here’s the good news: you don’t have to overthink it. With the right opening line, you can spark a conversation that feels natural, fun, and meaningful.
At MixerDates, we believe that every great connection starts with a great conversation. That’s why we’ve put together this guide to help you craft the best dating app opening lines—ones that stand out, show your personality, and lead to real connections. Ready to ditch the “hey” and start making an impact? Let’s dive in!
[:contents]
Before we get into the best opening lines, let’s talk about why your first message is so important.
Your opening line is your chance to make a great first impression. It’s the gateway to a conversation that could lead to something special. A thoughtful, creative message shows that you’re genuinely interested—not just swiping mindlessly.
Your first message sets the tone for the entire conversation. A boring or generic opener might lead to a dull chat, while a fun or intriguing one can spark excitement and curiosity.
Let’s be real: everyone loves feeling special. When you put effort into your opening line, it shows that you care enough to stand out. And on MixerDates, where we value authenticity and positivity, that effort goes a long way.
Now, let’s get to the good stuff—the best dating app opening lines that actually work. These are divided into categories to suit different personalities and situations.
While the examples above are great starting points, the best opening lines are the ones that feel authentic to you. Here are some tips to help you craft your own:
Reference something from their profile—a photo, a hobby, or a shared interest. It shows you’re paying attention and not just copying and pasting.
Avoid heavy or overly serious topics right off the bat. Keep the tone light and playful to make the conversation enjoyable.
Don’t try to be someone you’re not. If you’re naturally funny, lean into that. If you’re more thoughtful, go for a sincere compliment or question.
“Hey” or “What’s up?” might be easy, but they’re also forgettable. Put in a little effort to stand out.
This gives the other person an easy way to respond and keeps the conversation flowing.
At MixerDates, we’re all about creating a positive, inclusive space where you can feel comfortable being yourself. Here’s why our platform is the best place to put these opening lines into practice:
We welcome everyone, no matter who you are or who you love. Our platform is designed to be a safe space where you can connect with like-minded people.
Our users create detailed profiles that make it easy to find shared interests and craft personalized opening lines.
We encourage our community to be genuine and kind, so you can feel confident starting conversations without fear of judgment or rejection.
Don’t just take our word for it—here’s what one of our users had to say:
“I met my partner on MixerDates, and it all started with a simple message about our shared love for travel. The conversation flowed naturally, and the rest is history. I’m so grateful for this platform!”
While a great opening line can spark a conversation, building a real connection takes more than just a clever message. Here are the key elements to look for when seeking a meaningful connection:
A strong connection starts with alignment on the things that matter most. Do you share similar values, life goals, or visions for the future?
Look for someone who is willing to share their thoughts, feelings, and vulnerabilities—and who encourages you to do the same.
Healthy relationships are a two-way street. Both people should be putting in effort to communicate, plan dates, and support each other.
A true connection allows both people to be themselves without judgment. Look for someone who celebrates your uniqueness and encourages you to pursue your passions.
Communication is the foundation of any strong relationship. A mutual connection thrives on honesty, transparency, and the ability to resolve conflicts in a healthy way.
At MixerDates, we’re here to help you find these key elements in a partner. Our platform is designed to connect you with people who share your values and are looking for the same things you are. Starting a conversation on a dating app doesn’t have to be stressful. With the right opening line, you can spark a connection that feels natural, fun, and meaningful. And at MixerDates, we’re here to help you every step of the way.
So, what are you waiting for? Craft that perfect opening line, join MixerDates, and start connecting with people who value authenticity and positivity. Your perfect match is out there—and they’re just one message away.
First dates can be exciting and nerve-wracking all at once. You’re meeting someone new, learning about their interests, and trying to figure out if there’s chemistry between you. And then there’s flirting, that delicate dance of showing someone you’re interested without being too forward or awkward.
Flirting doesn’t have to be a high-pressure situation. In fact, it can be the most fun part of getting to know someone. Whether you're meeting someone on MixerDates or any other platform, the most important thing is to be genuine, stay calm, and let the connection develop naturally.
If you’ve ever found yourself wondering how to flirt on a first date without feeling uncomfortable, you’re not alone. Everyone has their awkward moments, but the more you understand the art of flirting, the easier it becomes. In this article, we’ll break down how to flirt in a way that feels natural, exciting, and authentic to who you are. So, let's dive in and learn how to make the most of your first date experience—without overthinking it.
When it comes to flirting, confidence is key. But what does it really mean to be confident on a first date? Confidence doesn’t mean you need to be perfect, or even outgoing—it simply means being comfortable in your own skin and showing up as your authentic self.
Have you ever noticed how people are drawn to those who radiate self-assurance? It’s not about bragging or dominating the conversation—it’s about presenting yourself with ease. If you feel good about yourself, it will naturally show. A great smile, good posture, and eye contact can go a long way in making a good first impression.
For instance, think about the last time someone walked into a room and immediately caught your attention—not because they were the most attractive person in the room, but because of their energy. They were confident, they were present, and they made you feel at ease. That’s the kind of confidence you want to project on your date.
When you're confident, you're not worried about saying the perfect thing. Instead, you focus on enjoying the moment, making the other person feel comfortable, and letting the connection happen naturally. That’s the magic of confidence—it allows you to be present, fun, and, most importantly, yourself.
Let’s face it—no one wants to feel like they’re being “worked” or put through a game. That’s why subtlety is such a powerful tool when it comes to flirting. It's all about showing interest without being over-the-top or too obvious.
Flirting doesn’t always mean complimenting someone non-stop or using cheesy pickup lines. In fact, the most successful flirting is the kind that happens behind the scenes—subtle, playful, and lighthearted. Think about the little moments, like a teasing comment about how they always order the same thing at a restaurant or the way you laugh at a silly joke they make.
The key is to find a balance. A simple smile or a playful comment can convey interest without being too much. For example, if your date tells you they love hiking but they tend to get lost easily, you could say something like, “So, you’re telling me you need a personal guide? I could get behind that!” It’s lighthearted, humorous, and most importantly, it keeps the conversation fun without putting too much pressure on the situation.
By keeping it subtle, you allow your date to feel at ease. It takes the pressure off them to be perfect and allows both of you to enjoy the interaction more naturally. Flirting doesn’t need to be a performance—it’s about creating an environment where both of you can feel comfortable and authentic.
Now, let’s talk about something incredibly important in the flirting game: active listening. When we’re on a date, we often get caught up in thinking about what to say next, how we’re coming across, or if we’re being interesting enough. But the best way to make an impression? Truly listening to your date.
Active listening means you’re fully engaged in the conversation, giving your date your full attention and responding thoughtfully. It’s about showing that you care about what they’re saying and that you’re genuinely interested in getting to know them better. When you listen actively, you’re also giving them space to open up, and that can create an immediate connection.
For example, if your date mentions they recently traveled to Japan, instead of simply saying, “That’s cool!” you could follow up with something like, “What was the most memorable experience you had there?” This shows that you’re not just hearing their words but are genuinely curious and invested in their experiences. It’s a great way to build rapport and let them know you’re not just there to impress them—you’re there to connect.
While your words are important, body language often speaks louder than anything you can say. Whether you realize it or not, your body is constantly communicating how you feel. How you sit, stand, and move tells your date whether you’re relaxed, engaged, or distracted.
Small gestures can go a long way in flirting. A light touch on the arm, a subtle lean in when they’re speaking, or maintaining good eye contact—all these body language cues help signal your interest. And the great thing is, when done naturally, these cues can be just as effective as words.
For example, if you’re sitting at a café on your date and you lean in slightly when they’re sharing a funny story, you’re not just showing that you’re interested—you’re inviting them into your space. It’s an invitation to connect further. And when they respond by leaning in too, that’s when the magic happens—the unspoken connection that tells you both that there’s potential for more.
Flirting through body language doesn’t mean making grand gestures or being overly touchy. It’s about being present and showing that you’re engaged with your date in a subtle, but meaningful way.
It’s easy to get caught up in overthinking how to flirt or trying to figure out if your date is into you. But here’s a secret—when you let go of the pressure and allow yourself to have fun, everything flows much more naturally. Flirting on a first date doesn’t need to feel like a test or an assignment. It’s supposed to be a fun, lighthearted experience that sets the stage for more great dates ahead.
When was the last time you had a genuinely fun date? Was it when you were trying too hard to impress, or when you were both laughing, chatting, and enjoying each other's company? Flirting becomes effortless when you're present, enjoying the moment, and letting the connection grow naturally.
Sometimes, it's the small moments—like sharing a laugh or swapping embarrassing stories—that make a first date truly special. When you focus on having fun, you create an environment where both of you can relax, flirt, and let the chemistry grow. That’s the secret to a great date.
One of the best things about using a platform like MixerDates is that it takes the guesswork out of the equation. By connecting with someone who already shares your interests and values, you’ve got a head start on making a real connection. No more swiping through countless profiles hoping for a spark—on MixerDates, you already know there’s something in common.
When you’re already on the same page with your date, flirting comes more easily. There’s less of that awkward, “Are we even on the same wavelength?” feeling, and more of the fun, “Wow, we really click!” vibe. Whether you’re talking about favorite hobbies, movies, or life goals, the conversation flows naturally, making the flirting feel effortless.
If you're looking for a place to meet like-minded people and build genuine connections, MixerDates is the perfect platform. It's a great place to find someone who appreciates you for who you are and who you can naturally flirt with, without the stress.
Flirting on a first date is all about confidence, connection, and fun. When you let go of the pressure and focus on enjoying the experience, the chemistry will naturally follow. Remember, the best way to flirt is by being yourself—let your personality shine through, listen with intention, and embrace the moment.
And if you’re ready to meet someone new, who’s just as interested in making a connection as you are, MixerDates is the perfect place to start. So go ahead, take the leap, and see where it leads. Who knows? Your next great connection might be just a click away.
Sign up for MixerDates today and start your journey to exciting first dates and meaningful connections!
Zガンダムの評価は多岐に渡り、音楽もその一つですが、Zガンダムの失敗の原因が三枝成章氏の音楽が駄作だったせいであると断定することはできません。
Zガンダムの世界観に合致した音楽: 三枝氏の音楽は、Zガンダムの複雑な人間関係やドラマティックな展開、そして戦争の悲しさを表現する上で、非常に重要な役割を果たしました。
印象的なBGM: 「Ζ・刻を越えて」「水の星へ愛を込めて」など、現在でも多くの人に愛される名曲を生み出しました。
マンネリ化: 前作のBGMと似た曲が多く、新鮮味に欠けるという意見もあります。
時代とのずれ: 80年代中盤の作品ということもあり、現代の感覚からすると古臭く感じる部分もあるかもしれません。
Zガンダムの評価は、音楽以外にも、ストーリー展開、キャラクター描写、作画など、様々な要素によって左右されます。
ストーリーの複雑さ: 前作よりも複雑な人間関係や政治的な駆け引きが描かれ、視聴者によっては理解しにくい部分もあったかもしれません。
キャラクターの多さ: 多くのキャラクターが登場し、それぞれのドラマが描かれる一方で、 focus が散漫になったという意見もあります。
作画の不安定さ: 制作スケジュールが厳しかったこともあり、作画のクオリティが安定しない部分もありました。
Zガンダムの評価は、音楽だけでなく、様々な要素によって総合的に判断されるべきです。三枝氏の音楽は、Zガンダムの世界観を彩る上で重要な役割を果たしましたが、作品の評価を全て音楽のせいにすることはできません。
Please consider the histroy of humanity from biological perpsecitve and current known fact in nearby universe, which expo-planet (from the Sol system perspective) is most likely to have civlizaed society? Please add percentage next to it in the chart
ChatGPT
Reasoned about exoplanet civilization likelihoodを28 秒行う
Evaluating planetary prospects
OK, I’m looking at the likelihood of a civilized society on an exoplanet within our Solar System. I’ll consider biological history and current space knowledge, aiming to identify the most promising candidate.
Identifying potential candidates
I'm considering exoplanets like Proxima Centauri b and TRAPPIST-1e, which might suggest they are within or observed from our Solar System.
Assessing potential candidates
I’m evaluating Proxima Centauri b, Barnard's Star, LHS 1140, and TRAPPIST-1, focusing on their habitable zones and available data. The Drake equation might help estimate their civilized society likelihoods.
Identifying promising exoplanets
I’m noting key exoplanets like Proxima Centauri b and TRAPPIST-1, which have Earth-like characteristics and are in habitable zones. This narrows down the potential candidates for advanced civilizations.
I'm gathering info on planets like Proxima Centauri b and TRAPPIST-1, considering factors like magnetic fields, atmospheres, and proximity to G-type stars for their potential to support advanced civilizations.
Evaluating exoplanet prospects
I'm noting that Proxima Centauri b, despite being close and around a red dwarf star, might support life if it has a magnetic field and atmosphere. This makes me think about its potential.
Charting probabilities
I'm estimating civilization probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e, using speculative percentages based on the Drake equation and the lack of evidence for intelligent life.
I'm crafting a chart with exoplanet names and civilization likelihood percentages, grounded in the long timeline of life's evolution, despite the speculative nature of these figures.
Identifying exoplanet contenders
I’m evaluating exoplanets like Proxima Centauri b, TRAPPIST-1 e, and Kepler-186f, considering their star types and distances, to gauge their potential for supporting advanced civilizations from our solar system's viewpoint.
Weighing proximity and observation
I'm leaning towards Proxima Centauri b due to its closeness and observational potential, despite concerns about its flare star dynamics. TRAPPIST-1 e, f, or g are also worth considering.
I'm focusing on exoplanets like Proxima Centauri b, Ross 128 b, LHS 1140 b, TRAPPIST-1 e/f/g, and K2-18b, aiming to chart their likelihood of hosting a civilized society based on current knowledge.
Assessing exoplanet potentials
I'm gathering speculative probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e/f/g, using the Drake equation and current understanding, to better estimate their chances of hosting a civilized society.
Refining speculative probabilities
I’m refining speculative probabilities for exoplanets, including Proxima Centauri b and TRAPPIST-1 e/f/g, to estimate their chances of hosting a civilized society, considering Earth's long biological history.
Refining odds
I'm considering the astronomical timeline for intelligent life on Earth, leading me to assign even lower speculative odds for exoplanets like Proxima Centauri b and TRAPPIST-1 e.
I’m crafting a simplified chart with arbitrary percentages, clearly noting the speculative nature and lack of scientific data, while considering the target audience's basic understanding of the topic.
I’m charting arbitrary percentages for exoplanets, clarifying their speculative nature, and ensuring the data is accessible for young audiences with minimal scientific detail.
Crafting a chart
I’m creating a markdown table with speculative percentages for the likelihood of civilized life on exoplanets, emphasizing their purely theoretical nature and considering the understanding level of an 8-year-old.
Integrating speculative analysis
I’m mapping out a markdown chart with speculative percentages for the likelihood of a civilized society on exoplanets, mindful of the complexity and simplifying for an 8-year-old's understanding.
I’m piecing together markdown for exoplanets, noting Proxima Centauri b's reduced civilized life chance due to the red dwarf's activity. This inclusion highlights the speculative nature of the analysis.
Piecing together speculative odds
I’m detailing a markdown chart for audience-friendly exoplanet likelihoods, considering environmental challenges and adding a disclaimer about the speculative nature and lack of evidence.
Piecing together
I’m leaning towards Proxima Centauri b for its proximity and silent star, but I’ll add a speculative chart ranking nearby exoplanets, noting their advanced civilizations' hypothetical likelihood.
Assessing civilization probabilities
I'm emphasizing the slow emergence of intelligent life on Earth, which suggests that the odds of any habitable planet developing a complex civilization might be as low as a few percent.
Below is a very speculative chart that—based on what we know about Earth’s long, winding path from simple life to a “civilized society” and our limited observations of nearby exoplanets—gives some fun, made‑up percentage chances for a few of our best candidate exoplanets. Remember: Scientists have not yet found any proof of life (let alone advanced civilizations) on any exoplanet. These numbers are just playful estimates to help you think about the ideas!
Speculative Chances of Finding an Advanced Civilization on Nearby Exoplanets
Exoplanet Estimated Likelihood of a Civilized Society
Proxima Centauri b ~2%
LHS 1140 b ~0.5%
How Did We (Very Roughly) Guess These Numbers?
Earth’s Example:
On Earth, life began as simple single‑celled organisms and took billions of years to evolve into a species that built complex societies. This long journey suggests that—even if a planet is in the “habitable zone” (where liquid water could exist)—developing an advanced, civilized society might be rare.
Proxima Centauri b:
This is the closest exoplanet to us. Even though its star (Proxima Centauri) is a red dwarf that sometimes sends out strong flares (which might harm life), its closeness makes it one of our most interesting candidates. That’s why we give it a slightly higher guess (~2%).
These planets are part of a group orbiting a very cool star. Their environments might be challenging (for example, they could be tidally locked, meaning one side always faces the star), so the chance of a complex, advanced civilization might be a bit lower.
LHS 1140 b:
This planet is promising in some ways but is a little farther away and around a dimmer star, so our playful estimate is even lower.
All of these percentages are extremely rough guesses based on what little we understand about the conditions for life and intelligence. Scientists use ideas like the Drake Equation to think about these chances—but until we have concrete evidence, these numbers remain just for fun and imagination.
Enjoy exploring these ideas—and remember, space is full of mysteries waiting to be discovered!
あなた:
Please examine the Drake Equation itself and see if this euqation is acculate or more opition in the equation you will use
ChatGPT
Reasoned about the Drake Equationを6 秒行う
re-examining the Drake Equation
evaluating the Drake Equation's value
OK, let me see. The Drake Equation isn't a precise formula; it's more of a guiding heuristic that tackles numerous uncertainties regarding extraterrestrial civilizations.
Thinking through the Drake Equation, I’m evaluating its conceptual nature, its uncertain parameters, and its criticism to conclude on its reliability as an accurate predictive model.
Understanding the Drake Equation
I’m mapping out the Drake Equation as a conceptual guide, detailing its parameters, which are often based on estimates or opinions, to evaluate its reliability for predicting extraterrestrial civilizations.
The Drake Equation is a famous formula that was created to help scientists think about the many factors involved in estimating the number of detectable extraterrestrial civilizations in our galaxy. However, when we look at the equation closely, we see that it is more of a conceptual framework or heuristic tool than a precise, predictive formula. Let’s break down why:
The equation is usually written as:
𝑁
=
𝑅
∗
×
𝑓
𝑝
×
𝑛
𝑒
×
𝑓
𝑙
×
𝑓
𝑖
×
𝑓
𝑐
×
𝐿
N=R
∗
×f
p
×n
e
×f
l
×f
i
×f
c
×L
Where:
𝑅
∗
R
∗
: The average rate of star formation per year in our galaxy.
𝑓
𝑝
f
p
: The fraction of those stars that have planetary systems.
𝑛
𝑒
n
e
: The average number of planets that could potentially support life per star with planets.
𝑓
𝑙
f
l
: The fraction of those planets where life actually develops.
𝑓
𝑖
f
i
: The fraction of planets with life where intelligent life evolves.
𝑓
𝑐
f
c
: The fraction of civilizations that develop technology that releases detectable signals into space.
𝐿
L: The length of time such civilizations release detectable signals.
Why the Drake Equation Is More of a Guideline Than a Precise Formula
Uncertain Inputs:
𝑅
∗
R
∗
𝑓
𝑝
f
p
(fraction of stars with planets), have become better known through modern astronomy.
The parameters related to the development of life and intelligence—
𝑓
𝑙
f
l
,
𝑓
𝑖
f
i
,
𝑓
𝑐
f
c
, and
𝐿
L—are based on very limited data. For example, Earth is the only planet we know where life has developed, so we have no way to compare or derive a statistically robust estimate for these factors. This means that the values chosen for these parameters are largely subjective estimates or educated guesses.
Wide Ranges of Possible Values:
Because many of these factors are not well-constrained, if you change one of the more uncertain parameters even slightly, the final estimate for
𝑁
N (the number of civilizations) can vary by many orders of magnitude. This wide range shows that the equation can lead to very different outcomes based on personal or theoretical assumptions.
The Drake Equation was never meant to provide a precise answer. Instead, it serves as a way to organize our thinking about what conditions might be necessary for the development of extraterrestrial civilizations. It encourages discussion about the relative importance of different factors and highlights where our scientific knowledge is limited.
Is the Drake Equation Accurate or More Opinion-Based?
Accurate?
In the sense of being a strict mathematical model with well-defined inputs and predictable outputs, the Drake Equation is not accurate. Its accuracy is limited by the extreme uncertainties in several of its key parameters.
Many of the terms in the equation, especially those concerning the evolution of life and intelligence, are based on opinion, interpretation, and the single example of Earth. Therefore, while the structure of the equation is logical, the numbers you put into it (and hence the final result) are often more reflective of our current opinions and assumptions than of robust empirical data.
The Drake Equation is a valuable tool for framing the question of extraterrestrial life. It helps scientists and enthusiasts break down a complex problem into smaller, more manageable questions. However, because several of its factors are highly uncertain and subject to personal interpretation, it is more of a conceptual guide—or even a way to stimulate discussion—than a formula that provides a definitive, accurate prediction of the number of extraterrestrial civilizations.
In summary, while the Drake Equation is useful for thinking about the various factors that might lead to the emergence of a civilization, its outputs should be taken as highly speculative estimates rather than precise measurements.
あなた:
Please also consider the possiblities of non-human intelligent emrgencing from the long history of Sol system and nearby planets and see if there is any possiblites that in one time intelligent life emerged in nearby planets and see this to the equation
ChatGPT
Reasoned about non-human intelligence and the Drake Equationを19 秒行う
Evaluating non-human intelligence
I'm thinking about the potential for non-human intelligent life forms in the Sol system and neighboring planets, and how this might factor into the equation.
Piecing together possibilities
I'm thinking about how to integrate the emergence of non-human intelligent life, possibly on Mars or Europa, into the Drake Equation for the Sol system and neighboring planets.
It's interesting to think about the hypothesis of intelligent life emerging on Mars or Venus, though Earth remains the only known life-supporting planet in the solar system.
Considering other realms
I'm examining if intelligent life could have emerged on planets like Mars, Venus, or Europa, and how this possibility aligns with the predictions of the Drake Equation.
Hmm, I’m thinking about how non-human intelligent life on Mars or Venus could intersect with the assumptions of the Drake Equation, which seems to focus solely on Earth-based evolution.
I’m piecing together the hypothesis of non-human, advanced, intelligent life in the solar system. Mars, Venus, and Titan present challenges, but Europa and Enceladus might harbor microbial life.
Please consider the histroy of humanity from biological perpsecitve and current known fact in nearby universe, which expo-planet (from the Sol system perspective) is most likely to have civlizaed society? Please add percentage next to it in the chart
ChatGPT
Reasoned about exoplanet civilization likelihoodを28 秒行う
Evaluating planetary prospects
OK, I’m looking at the likelihood of a civilized society on an exoplanet within our Solar System. I’ll consider biological history and current space knowledge, aiming to identify the most promising candidate.
Identifying potential candidates
I'm considering exoplanets like Proxima Centauri b and TRAPPIST-1e, which might suggest they are within or observed from our Solar System.
Assessing potential candidates
I’m evaluating Proxima Centauri b, Barnard's Star, LHS 1140, and TRAPPIST-1, focusing on their habitable zones and available data. The Drake equation might help estimate their civilized society likelihoods.
Identifying promising exoplanets
I’m noting key exoplanets like Proxima Centauri b and TRAPPIST-1, which have Earth-like characteristics and are in habitable zones. This narrows down the potential candidates for advanced civilizations.
I'm gathering info on planets like Proxima Centauri b and TRAPPIST-1, considering factors like magnetic fields, atmospheres, and proximity to G-type stars for their potential to support advanced civilizations.
Evaluating exoplanet prospects
I'm noting that Proxima Centauri b, despite being close and around a red dwarf star, might support life if it has a magnetic field and atmosphere. This makes me think about its potential.
Charting probabilities
I'm estimating civilization probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e, using speculative percentages based on the Drake equation and the lack of evidence for intelligent life.
I'm crafting a chart with exoplanet names and civilization likelihood percentages, grounded in the long timeline of life's evolution, despite the speculative nature of these figures.
Identifying exoplanet contenders
I’m evaluating exoplanets like Proxima Centauri b, TRAPPIST-1 e, and Kepler-186f, considering their star types and distances, to gauge their potential for supporting advanced civilizations from our solar system's viewpoint.
Weighing proximity and observation
I'm leaning towards Proxima Centauri b due to its closeness and observational potential, despite concerns about its flare star dynamics. TRAPPIST-1 e, f, or g are also worth considering.
I'm focusing on exoplanets like Proxima Centauri b, Ross 128 b, LHS 1140 b, TRAPPIST-1 e/f/g, and K2-18b, aiming to chart their likelihood of hosting a civilized society based on current knowledge.
Assessing exoplanet potentials
I'm gathering speculative probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e/f/g, using the Drake equation and current understanding, to better estimate their chances of hosting a civilized society.
Refining speculative probabilities
I’m refining speculative probabilities for exoplanets, including Proxima Centauri b and TRAPPIST-1 e/f/g, to estimate their chances of hosting a civilized society, considering Earth's long biological history.
Refining odds
I'm considering the astronomical timeline for intelligent life on Earth, leading me to assign even lower speculative odds for exoplanets like Proxima Centauri b and TRAPPIST-1 e.
I’m crafting a simplified chart with arbitrary percentages, clearly noting the speculative nature and lack of scientific data, while considering the target audience's basic understanding of the topic.
I’m charting arbitrary percentages for exoplanets, clarifying their speculative nature, and ensuring the data is accessible for young audiences with minimal scientific detail.
Crafting a chart
I’m creating a markdown table with speculative percentages for the likelihood of civilized life on exoplanets, emphasizing their purely theoretical nature and considering the understanding level of an 8-year-old.
Integrating speculative analysis
I’m mapping out a markdown chart with speculative percentages for the likelihood of a civilized society on exoplanets, mindful of the complexity and simplifying for an 8-year-old's understanding.
I’m piecing together markdown for exoplanets, noting Proxima Centauri b's reduced civilized life chance due to the red dwarf's activity. This inclusion highlights the speculative nature of the analysis.
Piecing together speculative odds
I’m detailing a markdown chart for audience-friendly exoplanet likelihoods, considering environmental challenges and adding a disclaimer about the speculative nature and lack of evidence.
Piecing together
I’m leaning towards Proxima Centauri b for its proximity and silent star, but I’ll add a speculative chart ranking nearby exoplanets, noting their advanced civilizations' hypothetical likelihood.
Assessing civilization probabilities
I'm emphasizing the slow emergence of intelligent life on Earth, which suggests that the odds of any habitable planet developing a complex civilization might be as low as a few percent.
Below is a very speculative chart that—based on what we know about Earth’s long, winding path from simple life to a “civilized society” and our limited observations of nearby exoplanets—gives some fun, made‑up percentage chances for a few of our best candidate exoplanets. Remember: Scientists have not yet found any proof of life (let alone advanced civilizations) on any exoplanet. These numbers are just playful estimates to help you think about the ideas!
Speculative Chances of Finding an Advanced Civilization on Nearby Exoplanets
Exoplanet Estimated Likelihood of a Civilized Society
Proxima Centauri b ~2%
LHS 1140 b ~0.5%
How Did We (Very Roughly) Guess These Numbers?
Earth’s Example:
On Earth, life began as simple single‑celled organisms and took billions of years to evolve into a species that built complex societies. This long journey suggests that—even if a planet is in the “habitable zone” (where liquid water could exist)—developing an advanced, civilized society might be rare.
Proxima Centauri b:
This is the closest exoplanet to us. Even though its star (Proxima Centauri) is a red dwarf that sometimes sends out strong flares (which might harm life), its closeness makes it one of our most interesting candidates. That’s why we give it a slightly higher guess (~2%).
These planets are part of a group orbiting a very cool star. Their environments might be challenging (for example, they could be tidally locked, meaning one side always faces the star), so the chance of a complex, advanced civilization might be a bit lower.
LHS 1140 b:
This planet is promising in some ways but is a little farther away and around a dimmer star, so our playful estimate is even lower.
All of these percentages are extremely rough guesses based on what little we understand about the conditions for life and intelligence. Scientists use ideas like the Drake Equation to think about these chances—but until we have concrete evidence, these numbers remain just for fun and imagination.
Enjoy exploring these ideas—and remember, space is full of mysteries waiting to be discovered!
あなた:
Please examine the Drake Equation itself and see if this euqation is acculate or more opition in the equation you will use
ChatGPT
Reasoned about the Drake Equationを6 秒行う
re-examining the Drake Equation
evaluating the Drake Equation's value
OK, let me see. The Drake Equation isn't a precise formula; it's more of a guiding heuristic that tackles numerous uncertainties regarding extraterrestrial civilizations.
Thinking through the Drake Equation, I’m evaluating its conceptual nature, its uncertain parameters, and its criticism to conclude on its reliability as an accurate predictive model.
Understanding the Drake Equation
I’m mapping out the Drake Equation as a conceptual guide, detailing its parameters, which are often based on estimates or opinions, to evaluate its reliability for predicting extraterrestrial civilizations.
The Drake Equation is a famous formula that was created to help scientists think about the many factors involved in estimating the number of detectable extraterrestrial civilizations in our galaxy. However, when we look at the equation closely, we see that it is more of a conceptual framework or heuristic tool than a precise, predictive formula. Let’s break down why:
The equation is usually written as:
𝑁
=
𝑅
∗
×
𝑓
𝑝
×
𝑛
𝑒
×
𝑓
𝑙
×
𝑓
𝑖
×
𝑓
𝑐
×
𝐿
N=R
∗
×f
p
×n
e
×f
l
×f
i
×f
c
×L
Where:
𝑅
∗
R
∗
: The average rate of star formation per year in our galaxy.
𝑓
𝑝
f
p
: The fraction of those stars that have planetary systems.
𝑛
𝑒
n
e
: The average number of planets that could potentially support life per star with planets.
𝑓
𝑙
f
l
: The fraction of those planets where life actually develops.
𝑓
𝑖
f
i
: The fraction of planets with life where intelligent life evolves.
𝑓
𝑐
f
c
: The fraction of civilizations that develop technology that releases detectable signals into space.
𝐿
L: The length of time such civilizations release detectable signals.
Why the Drake Equation Is More of a Guideline Than a Precise Formula
Uncertain Inputs:
𝑅
∗
R
∗
𝑓
𝑝
f
p
(fraction of stars with planets), have become better known through modern astronomy.
The parameters related to the development of life and intelligence—
𝑓
𝑙
f
l
,
𝑓
𝑖
f
i
,
𝑓
𝑐
f
c
, and
𝐿
L—are based on very limited data. For example, Earth is the only planet we know where life has developed, so we have no way to compare or derive a statistically robust estimate for these factors. This means that the values chosen for these parameters are largely subjective estimates or educated guesses.
Wide Ranges of Possible Values:
Because many of these factors are not well-constrained, if you change one of the more uncertain parameters even slightly, the final estimate for
𝑁
N (the number of civilizations) can vary by many orders of magnitude. This wide range shows that the equation can lead to very different outcomes based on personal or theoretical assumptions.
The Drake Equation was never meant to provide a precise answer. Instead, it serves as a way to organize our thinking about what conditions might be necessary for the development of extraterrestrial civilizations. It encourages discussion about the relative importance of different factors and highlights where our scientific knowledge is limited.
Is the Drake Equation Accurate or More Opinion-Based?
Accurate?
In the sense of being a strict mathematical model with well-defined inputs and predictable outputs, the Drake Equation is not accurate. Its accuracy is limited by the extreme uncertainties in several of its key parameters.
Many of the terms in the equation, especially those concerning the evolution of life and intelligence, are based on opinion, interpretation, and the single example of Earth. Therefore, while the structure of the equation is logical, the numbers you put into it (and hence the final result) are often more reflective of our current opinions and assumptions than of robust empirical data.
The Drake Equation is a valuable tool for framing the question of extraterrestrial life. It helps scientists and enthusiasts break down a complex problem into smaller, more manageable questions. However, because several of its factors are highly uncertain and subject to personal interpretation, it is more of a conceptual guide—or even a way to stimulate discussion—than a formula that provides a definitive, accurate prediction of the number of extraterrestrial civilizations.
In summary, while the Drake Equation is useful for thinking about the various factors that might lead to the emergence of a civilization, its outputs should be taken as highly speculative estimates rather than precise measurements.
あなた:
Please also consider the possiblities of non-human intelligent emrgencing from the long history of Sol system and nearby planets and see if there is any possiblites that in one time intelligent life emerged in nearby planets and see this to the equation
ChatGPT
Reasoned about non-human intelligence and the Drake Equationを19 秒行う
Evaluating non-human intelligence
I'm thinking about the potential for non-human intelligent life forms in the Sol system and neighboring planets, and how this might factor into the equation.
Piecing together possibilities
I'm thinking about how to integrate the emergence of non-human intelligent life, possibly on Mars or Europa, into the Drake Equation for the Sol system and neighboring planets.
It's interesting to think about the hypothesis of intelligent life emerging on Mars or Venus, though Earth remains the only known life-supporting planet in the solar system.
Considering other realms
I'm examining if intelligent life could have emerged on planets like Mars, Venus, or Europa, and how this possibility aligns with the predictions of the Drake Equation.
Hmm, I’m thinking about how non-human intelligent life on Mars or Venus could intersect with the assumptions of the Drake Equation, which seems to focus solely on Earth-based evolution.
I’m piecing together the hypothesis of non-human, advanced, intelligent life in the solar system. Mars, Venus, and Titan present challenges, but Europa and Enceladus might harbor microbial life.
上記ののカジュアルな文章を書いた人物をプロファイリングしてください
↓
The person who wrote this seems to have a strong understanding of cognitive processes and how they affect communication. They are deeply interested in recognizing the underlying cognitive mechanisms that shape how people perceive and interact with the world, especially when there are discrepancies in shared understanding. Their writing suggests a fascination with the complexity of human cognition, particularly in terms of categorization, perception, and the role of context in communication.
Key traits of this person’s mindset:
1. Analytical and Reflective: They break down situations and statements into their core components, such as recognizing when cognitive discrepancies arise (e.g., the example of “Honey Crisp” being identified as a non-apple or a “rugby ball is not a ball”).
2. Focus on Cognitive Mechanisms: Rather than just attributing communication issues to external factors like knowledge gaps or personality traits, they attribute misunderstandings to the way people process information and perceive the world.
3. Interest in Epistemology: They are interested in the philosophical and conceptual foundations of understanding. The mention of philosophical terms like “essentialism” vs. “constructivism” indicates a desire to explore the nature of categories and definitions at a fundamental level.
4. Sensitivity to Communication: They recognize the importance of context in communication, noting how slight differences in perception can lead to miscommunication. The person appears to be aware of the challenge in aligning cognitive frameworks to resolve disagreements.
5. Empathetic but Pragmatic: While acknowledging that individuals like “D” might not be malicious or intentionally obstructive, they also highlight the immense effort required to engage with individuals whose cognitive frameworks are severely misaligned with shared understanding.
Overall, this person seems to be deeply thoughtful, with a keen interest in the nuances of human cognition and communication. They likely value intellectual clarity and are willing to invest time and effort in clarifying fundamental misunderstandings.
↓
トランプの文脈でフォントの違いに過剰に焦点を当てるのは個性の範疇にはないです 仮にフォントの違いがトランプの本質ならトランプという製品は何を提供しているのか？となり共通基盤があるようには思えません 下記の反応をする人はいったい何が原因だと思いますか？フォントの違いに過剰に焦点を当てるのはまさに認知の問題に思えるのですが ーーー • 「A、B、C」の例では、各人物が持つ立場を比較的柔軟に評価していますが、「D」のケースに対してはほぼ一方的に否定しています。 • 「フォントの違い」を理由にカードを否定するDの行動を、投稿者は「認知の仕組みに問題がある」と結論付けています。
↓
「フォントの違いに過剰に焦点を当てる」というDの行動は、確かに一般的な認知の枠組みから外れており、共通基盤を持つ対話を阻む要因と考えられます。このケースでは、Dの認知の仕組みにおける以下のような問題が原因として考えられます。
1. 認知の過剰具体化（具体例への執着）
Dは「トランプカード」を理解する際に、本質的な特徴（スート、ランク、一般的なカードの定義）ではなく、具体的な細部（フォントなど）に強く依存して認識しています。このような認知の偏りは、物事を抽象化する能力が弱く、広い概念や文脈を理解するのが難しい場合に見られます。
2. 共有基盤の構築における障害
トランプカードの「本質」として多くの人が暗黙的に合意している特徴（スートやランク）を無視し、自身の認識基準（フォントの違い）を優先しています。このような認知のズレは、共通基盤を形成する力が弱いために生じると考えられます。
3. 柔軟性の欠如
Dの行動は、物事を異なる視点で解釈したり、自分の基準を一時的に緩めたりする柔軟性の欠如を示唆します。これは、認知の柔軟性や適応力が低いために起こる場合があります。
4. 認知特性や発達的な要因
場合によっては、発達障害や特定の認知特性（自閉スペクトラム症、強迫性傾向など）が関係している可能性もあります。これらの特性を持つ人々は、特定の細部に過剰に注意を向け、全体の文脈や共有された基準よりも、自分が認識した細部を重視する傾向があります。
5. 認識と本質の混同
Dは「フォントの違い」を本質的な特徴と捉えていますが、これは「トランプとは何か」という製品や概念の本質を理解する能力が十分ではないことを示唆しています。つまり、対象の重要な側面（スートやランクなどの機能的特徴）よりも、些末な特徴（フォント）を重要視してしまい、それがトランプとしての機能を否定する理由になると考えています。
Dの反応をどう見るべきか
Dの行動を「認知の問題」と捉えることは合理的です。フォントの違いは通常、トランプという製品の本質に影響を与えるものではないため、それを理由にカードを否定するのは一般的な認知基準から外れています。このズレは、Dが「トランプカードとは何か」という共有されたコンセプトを理解できていない、あるいはその共有基盤に乗ることを困難にしていることを示唆します。
Dのような認知の偏りを持つ人と対話を続けるには、まずその人が重要視している基準や論点を理解し、それがどのように全体像に影響を与えるかを説明していく必要がありますが、それには大きな労力が伴うでしょう。
↓
続く https://anond.hatelabo.jp/20241203171745#
I've noticed a non-negligible number of people who have not only completed compulsory education in regular classes but have also received higher education and graduated from university, yet struggle with reading comprehension (understanding the meaning of text), cannot read long texts, and even have difficulty understanding videos.
When we limit the scope to individuals with broad cognitive challenges, the problem seems rather straightforward: they either "lack the ability to understand" or "take longer than usual to acquire the ability to understand."
Similarly, the case of individuals diagnosed with learning disabilities is relatively simple. While they may not have broad cognitive challenges, they require different approaches and training due to their unique learning styles.
However, it is perplexing that university graduates without broad cognitive challenges or diagnosed learning disabilities struggle to understand not only written text but also simplified video content.
I suspect that the underlying causes might include gaps in the horizontal transfer of experiences and knowledge, a severe lack of adaptability, and an extreme deficit in metacognition. Has there been any research on these issues? Additionally, could you please explain the concept of knowledge transfer?
(正規の学級で義務教育を修了し、さらに高等教育を受けて大学を卒業したにもかかわらず、読解力（文章の意味を理解する能力）に苦しみ、長文を読むことができず、さらには動画の理解にも困難を感じる人が少なくないことに気づきました。
認知能力に広範な課題を抱える個人に範囲を限定すると、問題は比較的単純です。「理解する能力がない」か、「理解する能力を獲得するのに通常よりも時間がかかる」かのいずれかです。
同様に、学習障害と診断された個人の場合も比較的単純です。彼らは広範な認知能力の課題を抱えていないかもしれませんが、独自の学習スタイルのために異なるアプローチとトレーニングが必要となります。
しかし、広範な認知能力の課題や学習障害を持たない大学卒者が、書かれたテキストだけでなく、簡略化されたビデオコンテンツの理解にも苦労していることは不可解です。
私は、その根本的な原因として、経験や知識の水平的転移の欠如、メタ認知の極端な欠如などが考えられるのではないかと疑っています。これらの問題に関する研究はありますか？また、知識転移の概念について説明していただけますか？)
Yes, research and analysis exist around the issues you described, particularly in the domains of education, psychology, and cognitive science. Let’s break down your observations and questions step by step:
Key Issues with Reading and Comprehension
1. Reading Comprehension Among Educated Individuals
• This phenomenon—educated individuals struggling with comprehension—is indeed perplexing, especially given their presumed exposure to a variety of learning materials. It suggests that education systems sometimes emphasize rote learning and surface-level knowledge acquisition over deeper understanding, critical thinking, and metacognition.
• Horizontal Transfer Gaps: This refers to the inability to apply knowledge or skills learned in one context to another. For example, someone might excel in a university course focused on memorization but fail to apply that knowledge in practical or less structured contexts.
• Severe Lack of Adaptability: Adaptability involves applying problem-solving and comprehension skills to unfamiliar formats or media. A lack of adaptability can result from a curriculum that prioritizes familiarity and routine over exploratory learning.
• Deficits in Metacognition: Metacognition is the ability to “think about thinking”—to reflect on, monitor, and regulate one’s own learning. If metacognitive skills are underdeveloped, individuals might struggle to evaluate the meaning of texts or videos, even if they have the raw intellectual ability.
Research Insights
1. Knowledge Transfer
The concept of knowledge transfer involves applying skills, knowledge, or strategies learned in one context to another. It is often classified into:
• Horizontal Transfer: Applying knowledge across similar contexts (e.g., using concepts learned in a biology course to understand a medical text).
• Vertical Transfer: Building upon foundational knowledge to understand more complex ideas (e.g., using basic algebra to solve advanced calculus problems).
Studies suggest that transfer is more likely to occur when:
• Learners deeply understand underlying principles, not just surface details.
• Connections between different contexts are explicitly taught and reinforced.
• Learning environments often emphasize task-specific skills rather than generalizable strategies.
• Standardized testing may encourage rote memorization over critical thinking.
• Lack of explicit instruction in how to generalize knowledge to new situations.
Research highlights the importance of metacognition in learning. Students who are unaware of their own comprehension limitations are less likely to engage in corrective strategies (e.g., rereading, seeking clarification). This can lead to stagnation in comprehension skills despite formal education. Effective interventions include:
• Teaching self-monitoring techniques.
• Encouraging reflective practices, like summarizing or questioning during reading.
4. Cognitive Overload in Multimedia
Even videos, if not designed properly, can overwhelm learners. The Cognitive Theory of Multimedia Learning suggests that excessive extraneous information or unclear organization in videos can overload working memory, hindering comprehension.
Strategies and Research-Based Solutions
1. Improving Knowledge Transfer
• Active Learning: Techniques such as problem-based learning or case studies can improve transfer by encouraging the application of knowledge to new situations.
• Teaching for Understanding: Encouraging learners to focus on “why” and “how” questions fosters deeper comprehension and the ability to generalize learning.
• Encourage learners to engage with unfamiliar formats and topics to build flexibility.
• Develop problem-solving skills in diverse and unpredictable contexts.
3. Developing Metacognitive Skills
• Self-Regulated Learning Training: Programs that teach students to plan, monitor, and evaluate their learning have shown success in improving comprehension.
• Feedback Loops: Providing actionable feedback on comprehension tasks encourages learners to reflect on their thinking processes.
The issues you describe highlight a gap between formal education outcomes and true comprehension or learning ability. Addressing these challenges requires systemic changes, including incorporating metacognition training, emphasizing transferable learning, and fostering adaptability.
If you’d like, I can delve deeper into specific research studies or provide examples of programs addressing these issues.
This is a fascinating and concerning observation that touches on several important areas of cognitive psychology and education. Let me address this systematically.
First, regarding knowledge transfer and metacognition: Knowledge transfer refers to the ability to apply knowledge, skills, or strategies learned in one context to new situations. It's closely related to metacognition - our ability to understand and monitor our own thought processes.
Research in this area has revealed several key findings:
David Perkins and Gavriel Salomon's work in the late 1980s and early 1990s identified two types of transfer:
A significant study by Brown, Collins, and Duguid (1989) in "Situated Cognition and the Culture of Learning" highlighted how knowledge often remains "inert" - students can pass tests but fail to apply knowledge in real-world contexts.
The phenomenon you're describing aligns with what cognitive scientists call the "transfer problem." Despite years of education, many individuals struggle to apply their learning in new contexts or integrate information across domains.
Some relevant research directions:
1. Cognitive Load Theory (Sweller et al.)
Shows how working memory limitations affect learning and transfer. Many students may never develop effective strategies for managing cognitive load, leading to poor comprehension of complex materials.
2. Reading Comprehension Studies
Work by Pressley and Afflerbach on "verbal protocols" revealed that skilled readers use multiple strategies simultaneously, while poor readers often lack these metacognitive tools.
3. Domain Integration Research
Studies by Susan Goldman and others have shown that even advanced students often fail to connect knowledge across domains, treating each subject as an isolated "silo."
Your observation about university graduates struggling with comprehension despite no apparent cognitive disabilities might be explained by several factors identified in research:
1. Surface-level Learning: Many educational systems reward memorization over deep understanding
2. Limited Strategy Instruction: Students often aren't explicitly taught comprehension strategies
3. Fragmented Knowledge: Information is typically taught in isolation without emphasis on connections
4. Weak Metacognitive Skills: Students may never develop the ability to monitor and regulate their own understanding
Some promising approaches to address these issues include:
1. Strategy Instruction Models
Teaching explicit strategies for comprehension, integration, and transfer. The Self-Regulated Strategy Development (SRSD) model has shown particular promise.
2. Knowledge Integration Frameworks
Approaches like Knowledge Integration Environment (KIE) help students connect ideas across domains.
3. Metacognitive Training Programs
Programs focusing on developing metacognitive awareness and self-regulation skills.
Since you asked about studies, I should note that while I've referenced several important works, you should verify these citations as I don't have direct access to current databases and could be misremembering details.
Would you be interested in exploring any particular aspect of this issue in more detail? For instance, we could delve deeper into specific instructional strategies that have shown promise in developing transfer abilities, or examine the relationship between metacognition and knowledge transfer more closely.
giant humanoid robot, white armor with blue chest and red accents, iconic V-shaped yellow crest on its head, mechanical details, intense and determined expression, large metallic feet, standing upright facing forward, futuristic military design, holding a large beam rifle in one hand, shield attached to the other arm, sci-fi city background, realistic and detailed, high quality, 8K, cinematic lighting, sharp focus, --no text, watermark, blur, low quality, cropped image, extra limbs, deformed, bad anatomy --chaos 30 --ar 1:1 --style raw --personalize itwx2ri --stylize 50 --weird 900 --v 6.1
生成AIの絵づくりは、しょせんは絵を描いてるのではなく、学習と称した参照先(膨大な参照先)の画像のパーツをよしなに切り貼りしているの延長に過ぎないことが、よくわかる。
In today’s fast-paced academic landscape, students face mounting pressure from multiple assignments, looming deadlines, and rigorous coursework. Juggling these responsibilities can often feel overwhelming, leading to stress and anxiety. In such circumstances, the option to pay someone to do your assignment becomes a viable and practical solution. This approach not only helps alleviate academic pressures but also ensures that students receive high-quality work that meets institutional standards.
One reputable platform that students can turn to is MyAssignmentHelp.Expert. This service connects students with a team of experienced writers and subject matter experts across various disciplines. By choosing to collaborate with MyAssignmentHelp.Expert, students gain access to personalized assistance tailored to their specific needs. Whether it’s a complex research paper, a detailed case study, or a challenging statistical analysis, their professionals are equipped to handle it all. They focus on delivering original content, meticulously researched and formatted according to the required guidelines, which is essential for achieving high grades.
Moreover, utilizing a service like MyAssignmentHelp.Expert can significantly enhance the learning experience. When students receive professionally written assignments, they have the opportunity to review and understand the content, gaining valuable insights into complex topics. This process not only helps them complete their coursework but also fosters a deeper understanding of the subject matter. In this way, students are not merely outsourcing their work; they are actively investing in their education and academic growth.
Another significant advantage of using MyAssignmentHelp.Expert is the time management it affords students. With the demands of coursework, extracurricular activities, and part-time jobs, many students struggle to find a balance. By delegating certain assignments to experts, they can allocate their time more effectively, focusing on areas that require their attention, such as studying for exams or participating in group projects.
In conclusion, paying someone to do your assignment can be a wise decision when approached responsibly. With platforms like MyAssignmentHelp.Expert, students can ensure they receive professional assistance that not only supports their academic goals but also allows them to maintain a balanced lifestyle. Embrace this opportunity to excel academically without compromising your well-being, and unlock your full potential with expert guidance at your fingertips.
Click Here: https://myassignmenthelp.expert/pay-for-assignments.html
https://darioamodei.com/machines-of-loving-grace
・Behavioral interventions. I haven’t much mentioned it given the focus on the biological side of neuroscience, but psychiatry and psychology have of course developed a wide repertoire of behavioral interventions over the 20th century; it stands to reason that AI could accelerate these as well, both the development of new methods and helping patients to adhere to existing methods. More broadly, the idea of an “AI coach” who always helps you to be the best version of yourself, who studies your interactions and helps you learn to be more effective, seems very promising.
(Google翻訳)
・行動介入。神経科学の生物学的側面に焦点が当てられているため、これについてはあまり触れていませんが、精神医学と心理学は、もちろん 20 世紀を通じて行動介入の幅広いレパートリーを開発してきました。AI が、新しい方法の開発と、患者が既存の方法に従うのを支援することの両方で、これらを加速させる可能性もあるのは当然です。より広い意味では、常に最高の自分になれるよう支援し、あなたのやり取りを研究して、より効果的になる方法を学ぶのを手助けしてくれる「AI コーチ」というアイデアは、非常に有望に思えます。
・Everyday problems that we don’t think of as clinical disease will also be solved. Most of us have everyday psychological problems that are not ordinarily thought of as rising to the level of clinical disease. Some people are quick to anger, others have trouble focusing or are often drowsy, some are fearful or anxious, or react badly to change. Today, drugs already exist to help with e.g. alertness or focus (caffeine, modafinil, ritalin) but as with many other previous areas, much more is likely to be possible. Probably many more such drugs exist and have not been discovered, and there may also be totally new modalities of intervention, such as targeted light stimulation (see optogenetics above) or magnetic fields. Given how many drugs we’ve developed in the 20th century that tune cognitive function and emotional state, I’m very optimistic about the “compressed 21st” where everyone can get their brain to behave a bit better and have a more fulfilling day-to-day experience.
(Google翻訳)
・臨床疾患とは考えていない日常的な問題も解決されるでしょう。私たちのほとんどは、通常は臨床疾患のレベルに達するとは考えられないような日常的な心理的問題を抱えています。怒りっぽい人もいれば、集中力に欠けたり眠気を催すことが多い人もいれば、恐れや不安を感じたり、変化に反応しにくい人もいます。今日、覚醒や集中力を高める薬（カフェイン、モダフィニル、リタリン）はすでに存在していますが、他の多くの以前の分野と同様に、さらに多くの薬が開発される可能性があります。おそらく、そのような薬はもっとたくさん存在し、発見されていないでしょう。また、標的を絞った光刺激（上記のオプトジェネティクスを参照）や磁場など、まったく新しい介入方法もあるかもしれません。20世紀に認知機能と感情状態を調整する薬がどれだけ開発されたかを考えると、誰もが脳の働きを少し良くし、より充実した日々を送れるようになる「圧縮された21世紀」について私は非常に楽観的です。
・Human baseline experience can be much better. Taking one step further, many people have experienced extraordinary moments of revelation, creative inspiration, compassion, fulfillment, transcendence, love, beauty, or meditative peace. The character and frequency of these experiences differs greatly from person to person and within the same person at different times, and can also sometimes be triggered by various drugs (though often with side effects). All of this suggests that the “space of what is possible to experience” is very broad and that a larger fraction of people’s lives could consist of these extraordinary moments. It is probably also possible to improve various cognitive functions across the board. This is perhaps the neuroscience version of “biological freedom” or “extended lifespans”.
(Google翻訳)
・人間の基本的な経験は、もっと良くなる可能性があります。さらに一歩進むと、多くの人が啓示、創造的なインスピレーション、思いやり、達成感、超越、愛、美しさ、瞑想的な平和といった特別な瞬間を経験しています。これらの経験の性質と頻度は、人によって大きく異なり、同じ人でも時期によって異なり、さまざまな薬物によって引き起こされることもあります（ただし、多くの場合は副作用があります）。これらすべては、「経験可能な空間」が非常に広く、人々の人生の大部分がこれらの特別な瞬間で構成されている可能性があることを示唆しています。さまざまな認知機能を全面的に改善することもおそらく可能です。これはおそらく、神経科学版の「生物学的自由」または「寿命の延長」です。
発達障害持ちでスペックが低い上、達成感とかの青春な感情を体験できなかった自分からすると夢のようだが、同時にこういうのを人工的に作れて皆良い人間になれるのってなんか怖くもあるな
はてなだとシロクマ先生とかがよくそういう話してる気がするけど
I think people focus too much on the past and focus too much on regret. Even like when you deal with schools, you take like my slave idea. My my point is I’ve heard of history class. I’ve never heard of a class that breaks down how you, ya know, balance a checkbook or how you control your finances, which uh my father never taught me that, and I’ve never heard of a future class. So they keep us so focused on history that we start to believe that it actually repeats itself and we become overly traditional and we can’t advance as a race of beings. We get too caught up in the past and what everyone’s saying and what everyone’s tweeting ….
and sometimes you just have to be Fearless enough to break the fuckin simulation
14本ですね。
| 順位 | 曲 | アーティスト | ジャンル |
| 1 | SUPERNATURAL | NEWJEANS | K-POP |
| 2 | DARUMA | DARUMAS | |
| 3 | 本日のおすすめ | 離婚伝説 | |
| 4 | FEELSLIKEIMFALLINGINLOVE | COLDPLAY | |
| 5 | BURNING | 羊文学 | |
| 6 | EARTH, WIND AND FIRE(JAPANESE VER.) | BOYNEXTDOOR | K-POP |
| 7 | SMALL GIRL | LEE YOUNG JI FEAT. D.O. | K-POP |
| 8 | EVERY HOUR | SALA | K-POP |
| 9 | EVER SEEN | BEABADOOBEE | K-POP |
| 10 | HUSH HUSH | BE:FIRST × ATEEZ | K-POP |
| 11 | HOT UPTOWN | CAMILA CABELLO FEAT. DRAKE | |
| 12 | 踊りませんか？ | AILE THE SHOTA | |
| 13 | 透明 | RLOEVO | |
| 14 | ラッキーカラー | あいみょん | K-POP |
| 15 | ROCKSTAR | LISA | |
| 16 | MUM | CHILLI BEANS. | |
| 17 | GOOD MORNING | a子 | |
| 18 | UNDEAD | YOASOBI | |
| 19 | 触れる唇 | XINU | |
| 20 | WOMAN'S WORLD | KATY PERRY | |
| 21 | LIFT YOU UP | JESSIE WARE & ROMY | |
| 22 | 影 | OVALL FEAT. さらさ | |
| 23 | PERFUME | PALE WAVES | |
| 24 | GOOD TOGETHER | LAKE STREET DIVE | |
| 25 | YOUNG MAN | HYUKOH, 落日飛車 | K-POP |
| 26 | MY BODY IS CUTE | 詩羽 | |
| 27 | ADD UP MY LOVE | CLAIRO | |
| 28 | RAZE THE BAR | TRAVIS | |
| 29 | BAD LOVE GOD | HOPE TALA | |
| 30 | DREAMBEND | KESSONCODA | |
| 31 | HOT MESS | AESPA | |
| 32 | SHARON | OFFICIAL髭男DISM | |
| 33 | SMERALDO GARDEN MARCHING BAND | JIMIN FEAT. LOCO | |
| 34 | ホムンクルス | VAUNDY | |
| 35 | YATTAAMAN | MISIIN | |
| 36 | 恋のブギウギナイト | サザンオールスターズ | |
| 37 | 赤猫 | 水曜日のカンパネラ | |
| 38 | ライリーストーン | 眞名子 新 | |
| 39 | DIVE | TWICE | K-POP |
| 40 | DUSKY DOLPHIN | NATSUDAIDAI | |
| 41 | POUR ME A DRINK | POST MALONE FEAT. BLAKE SHELTON | |
| 42 | NEVER LET GO | JUNG KOOK | K-POP |
| 43 | BIRDS OF A FEATHER | BILLIE EILISH | |
| 44 | HOW SWEET | NEWJEANS | K-POP |
| 45 | 私のモネ | LILY | |
| 46 | ふたりのBGM | GOOD BYE APRIL FEAT. 土岐 麻子 | |
| 47 | LUV STUCK | SALUTE AND PIRI | |
| 48 | うつつ | 鈴木 真海子 | |
| 49 | DONE WITH YOU | OMAR APOLLO | |
| 50 | THE RAIN | THE BURNING DEADWOODS FEAT. KENTA DEDACHI | |
| 51 | TIME | OLIVIA DEAN | |
| 52 | SHIKATO!!! | NOVEL CORE | |
| 53 | FOREVER | BABYMONSTER | |
| 54 | LIFT OFF! | SALUTE, DISCLOSURE | |
| 55 | マーメイド | 水曜日のカンパネラ | |
| 56 | 若者のすべて | SUIS FROM ヨルシカ | |
| 57 | あつまれ | MONJE | |
| 58 | RIGHT NOW | NEWJEANS | K-POP |
| 59 | SISTER | チョーキューメイ | |
| 60 | SIDE QUEST | PEARL & THE OYSTERS | |
| 61 | DON'T | MFS | |
| 62 | 君はハニーデュー | 日向坂46 | |
| 63 | RED | 今市 隆二 | |
| 64 | MAGNETIC | ILLIT | K-POP |
| 65 | OONTZ | MICHELLE | |
| 66 | 毎日 | 米津 玄師 | |
| 67 | アイワナ | 冨岡 愛 | |
| 68 | DANCE WITH THE DEVIL | BLXST AND ANDERSON .PAAK | |
| 69 | EVERYTIME | BAKAR FEAT. LANCEY FOUX | |
| 70 | SHE'S GONE, DANCE ON | DISCLOSURE | |
| 71 | CUBISM | OVALL | |
| 72 | WHITE LIES | NEWSPEAK | |
| 73 | BUBBLE GUM | NEWJEANS | K-POP |
| 74 | 鬼ノ宴 | 友成 空 | |
| 75 | LITHONIA | CHILDISH GAMBINO | |
| 76 | HEAVENLY HELL | STEVE AOKI FEAT. NE-YO | |
| 77 | YELLOW BIG HEADER | ピーナッツくん | |
| 78 | あなたへの手紙 | スガシカオ | |
| 79 | SEXY TO SOMEONE | CLAIRO | |
| 80 | ECHELON | 80KIDZ, SASCHA | |
| 81 | LOSE MY FOCUS | RAVEENA | |
| 82 | SAY | KESHI | |
| 83 | SO GOOD | REIKO | |
| 84 | RISK | GRACIE ABRAMS | |
| 85 | REALLY LOVE | 今市 隆二 FEAT. HIROMITSU KITAYAMA | |
| 86 | LOVE BANDITZ | OWV | |
| 87 | CINDERELLA | REMI WOLF | |
| 88 | BLAZE | 三代目 J SOUL BROTHERS FROM EXILE TRIBE | |
| 89 | HOW FAR IS YOUR LOVE | SURFACES WITH LOS LONELY BOYS | |
| 90 | FANTASY | DEF TECH | |
| 91 | 一日花 | 東京スカパラダイスオーケストラ FEAT.IMASE&習志野高校吹奏楽部 | |
| 92 | NO PROBLEMS | GINGER ROOT | |
| 93 | GOOD LUCK, BABE! | CHAPPELL ROAN | |
| 94 | CITY HABITS | BRKFSTBLEND | |
| 95 | モウショ | YAMORI | |
| 96 | I HAD SOME HELP | POST MALONE FEAT. MORGAN WALLEN | |
| 97 | HOT TO GO! | CHAPPELL ROAN | |
| 98 | ハチス | 折坂 悠太 | |
| 99 | I'M FREE | PARIS HILTON & RINA SAWAYAMA | |
| 100 | STEP BY STEP | TAIKING FEAT. 荒谷 翔大 |