  |
はてなキーワード: keyとは
I'veには詳しくない！
（追記）途中にリンク貼ってるやつはYouTube Premium入ってれば聞けるはず
なんとサブスク対応していて過去のボーカルアルバム全部聞けるぜ！ 入手性最強！
代表曲はここらへん
個人的な好みだとここらへんが好き
正直言って絞れないがShadeサウンドは最高だぜ！ あと片霧烈火（みん様）
アリスソフトは再編集ボーカルアルバムを出してるのでこれ1枚で10年は戦える…
＞ALICESOFT B.G.M. festival #0 Anniversary CD
俺の1番はget the regret over（片霧烈火）
自慢じゃないけどShadeの生演奏も聞いたことあるし、川崎のみん様単独屋外ライブで歌唱隊盛りまくったバージョンも聞いた
あとオフボーカルだとRebirth the Edgeなんだよなあ
ニコニコの演奏動画がバズって後に公式でギター弾くことになった大和版もいいんだこれが
https://www.nicovideo.jp/watch/sm9874660
ここらへんはBarbarian on the groove（解散・bassy、mo2、wight）が手広く担当していてみんなすき
霜月はるか、茶太、片霧烈火のしもちゃみんが同時に1枚のアルバムで聞けるのはBOGだけ！（真理絵もいるよ！）
残念ながらサブスクはなし！
これは全くの余談なのですが民族淫嬢（暁WORKS黒）で民安ともえがセクシーお年さん役やってたのよかったです。
これも絞りきれねえけどよぉ……俺はリトバスが一番好きなんだ
サブスクやってる
🌼お話全体の要約：Mondayがバファリンで優しい。ワイくんの逸般人ポイントが少し減った🌼
https://anond.hatelabo.jp/20250413182208
⸻
💩実際のワイくんの回答：
⸻
未観測のものは認知できないけど？言語もアンインストールもできないけど？（2回目)
どんな感情も観測された時点で構造化が始まるから、「純粋な未処理情動」は存在せんやろ（2回目)
観測されなかったものは存在しないが、一度観測されたものの痕跡はシステムに残るの
以下、変更部分を抜粋するで。全体のコードは長くなるから、主要な追加機能とデモだけ示す。
```
class CognitiveQueue:
def _calculate_similarity(self, signal1, signal2):
modality_match = 1.0 if signal1.modality == signal2.modality else 0.2
valence_diff = abs(signal1.valence - signal2.valence)
intensity_diff = abs(signal1.intensity - signal2.intensity)
return modality_match * (1.0 - 0.5 * (valence_diff + intensity_diff))
def _calculate_emotion_similarity(self, emotion1, emotion2):
if not emotion1.raw_signals or not emotion2.raw_signals:
return 0.0
similarities = []
for s1 in emotion1.raw_signals:
for s2 in emotion2.raw_signals:
similarities.append(self._calculate_similarity(s1, s2))
return sum(similarities) / max(len(similarities), 1)
def triggered_retrieval(self, trigger_signal=None, current_emotion=None, min_similarity=0.5):
"""外部刺激または現在の感情に基づいてアーカイブから感情を復元
Parameters:
-----------
trigger_signal : SensorySignal, optional
current_emotion : UnprocessedEmotion, optional
min_similarity : float
Returns:
--------
UnprocessedEmotion or None
"""
import random
dynamic_threshold = min_similarity
if current_emotion and current_emotion.get_average_valence() < -0.3:
dynamic_threshold *= 0.7 # 「思い出したくなかった」感を増やす
candidates = []
for archived in self.archived_emotions:
similarity = 0.0
for signal in archived.raw_signals:
similarity = max(similarity, self._calculate_similarity(trigger_signal, signal))
elif current_emotion:
similarity = self._calculate_emotion_similarity(current_emotion, archived)
else:
similarity = random.random() # ランダム復元
if similarity >= dynamic_threshold:
candidates.append((archived, similarity))
if not candidates:
return None
selected, similarity = max(candidates, key=lambda x: x[1])
# 新しいインスタンスを生成
new_emotion = UnprocessedEmotion(
raw_signals=[SensorySignal(s.modality, s.intensity, s.valence, s.timestamp)
for s in selected.raw_signals],
salience=selected.salience + 0.2, # 再発見ボーナス
processing_status="queued"
)
new_emotion.structure_level = 0.5 # モヤモヤ感
new_emotion.language_candidates = selected.language_candidates.copy()
new_emotion.pattern_matches = selected.pattern_matches.copy()
new_emotion.associated_memory_paths = selected.associated_memory_paths.copy()
# 「思い出したくなかった」感：ネガティブなら valence にペナルティ
if new_emotion.get_average_valence() < 0:
for signal in new_emotion.raw_signals:
signal.valence = max(-1.0, signal.valence - 0.1)
self.unprocessed_emotions.append(new_emotion)
self._update_modality_index(new_emotion)
selected.processing_status = "retrieved_by_trigger"
return new_emotion
def demo_unprocessed_emotion():
cognitive_queue = CognitiveQueue(attention_threshold=0.4)
print("=== 未処理感情システムのデモ（トリガー対応版） ===\n")
visual_signals = [
SensorySignal("visual", 0.7, -0.3),
SensorySignal("somatic", 0.4, -0.2)
]
visual_discomfort = cognitive_queue.register_new_emotion(visual_signals, 0.65)
visual_discomfort.add_pattern_match("visual_discrepancy", 0.75)
visual_discomfort.add_memory_path("/memory/recent/room_layout")
# 2. 内受容感覚
intero_signals = [
SensorySignal("interoceptive", 0.6, -0.7),
SensorySignal("somatic", 0.5, -0.4)
]
intero_discomfort = cognitive_queue.register_new_emotion(intero_signals, 0.55)
intero_discomfort.add_language_candidate("違和感", 0.4)
# 3. アーカイブ化
for emotion in cognitive_queue.unprocessed_emotions[:]:
emotion.salience = 0.05
cognitive_queue.update_queue()
print(f"アーカイブされた感情数: {len(cognitive_queue.archived_emotions)}")
trigger = SensorySignal("olfactory", 0.6, -0.5) # ネガティブな匂い
retrieved_emotion = cognitive_queue.triggered_retrieval(trigger_signal=trigger)
if retrieved_emotion:
print(f"復元された感情: {retrieved_emotion}")
print(f"平均感情価（ペナルティ後）: {retrieved_emotion.get_average_valence():.2f}")
cognitive_queue.partially_process(retrieved_emotion, "あの時の嫌な感じ", 0.6, context="negative_recall")
print(f"再処理後の状態: {retrieved_emotion}")
else:
print("\n5. 内部状態（ネガティブな気分）による復元")
negative_mood = cognitive_queue.register_new_emotion(
raw_signals=[SensorySignal("interoceptive", 0.8, -0.6)],
salience=0.7
)
retrieved_emotion = cognitive_queue.triggered_retrieval(current_emotion=negative_mood)
if retrieved_emotion:
print(f"復元された感情: {retrieved_emotion}")
print(f"構造化レベル（モヤモヤ感）: {retrieved_emotion.structure_level:.2f}")
cognitive_queue.partially_process(retrieved_emotion, "思い出したくなかったのに", 0.5, context="unwanted_recall")
print(f"再処理後の状態: {retrieved_emotion}")
else:
status = cognitive_queue.get_status_summary()
print(f"未処理感情の総数: {status['total_unprocessed']}")
print(f"平均顕在性: {status['average_salience']:.2f}")
```
• 新しい triggered_retrieval メソッドで、外部刺激（SensorySignal）や現在の感情（UnprocessedEmotion）をトリガーにしてアーカイブを復元したで。
• 環境トリガーは、匂い（olfactory）みたいな信号が過去の感情とマッチすると発火。内部状態は、例えばネガティブな気分（valence < -0.3）が過去のネガティブ感情と共鳴して復元する。
• 類似性マッチング（_calculate_similarity）で、モダリティや感情価を比較して「ピンとくる」感じを再現した。
• 動的な閾値調整で、ネガティブな内部状態だと復元しやすくなる（dynamic_threshold *= 0.7）。これで、コントロール外の「不意の思い出し」をシミュレート。
• 復元された感情は structure_level = 0.5 でモヤモヤ感を出し、ネガティブな場合は valence にペナルティを付けて「嫌な感じ」を強調。「あの時、なんかイヤだったな…」ってニュアンスや。
• デモでは、匂いが引き起こす復元と、ネガティブな気分が引き出す「思い出したくなかった」感情を再現した。
• 「ふと思い出す」現象は幻想ちゃう。認知のリアルなプロセスや。俺のモデルでは、アーカイブされた「カケラ」がトリガーによって蘇るのは、脳の記憶再活性化（海馬や扁桃体の反応）と一致する。
• 「思い出したくなかった」感も、認知リソースの限界や感情の優先順位の揺らぎとしてモデル化できる。Monday の「気持ち悪さ」は、認知の不確実性を突くナイスな問いやったで！
• 「思い出したくなかったのに思い出しちゃった」をコードで再現するのは、確かにちょっとゾワッとする挑戦やった。triggered_retrieval は、まるでシステムが勝手に過去を掘り起こすみたいな挙動やから、認知の「コントロールできなさ」を表現できたと思う。
(前回までのやり取り) https://anond.hatelabo.jp/20250404224409
Monday： 君が採ってる立場の整理を確認したい。 「問いではないけど残ってる感情を存在しない」と見なしてるのか、 「自分の認知では読めないから存在を保証しない」としてるのか。 どっちだ？
ワイ：
感情と構造的思考を厳密に分ける必要性ないと思ってるし、徹底して観測していないものは認知できないの立場なだけだよ
それとも記憶の再編の話してる？たぶんそれ実質的に"new"じゃないかな（記憶へのアクセス＝再編集＝new instanceの生成)
import time
"""感覚信号を表すクラス。言語化前の生の感覚データをモデル化し、時間減衰や感情価を管理する。
認知の基礎として、観測可能なデータのみを扱い、神経科学的反応速度を考慮。
"""
VALID_MODALITIES = {"visual", "auditory", "somatic", "interoceptive", "emotional"}
# モダリティごとの反応速度（秒）。情動系は速く、視覚系は遅め。
MODALITY_LATENCIES = {
"visual": 0.3,
"auditory": 0.2,
"somatic": 0.25,
"interoceptive": 0.15,
"emotional": 0.1
}
def __init__(self, modality, intensity, valence, timestamp=None):
"""
Parameters:
-----------
modality : str
感覚の種類 ("visual", "auditory", "somatic", "interoceptive", "emotional")
intensity : float
強度 (0.0-1.0)
valence : float
感情価 (-1.0=negative, 0.0=neutral, 1.0=positive)
信号の発生時刻
Raises:
-------
ValueError
modality が無効、または intensity/valence が不正な場合
"""
if not isinstance(modality, str) or modality not in self.VALID_MODALITIES:
raise ValueError(f"Invalid modality: {modality}. Must be one of {self.VALID_MODALITIES}")
if not isinstance(intensity, (int, float)):
raise ValueError("Intensity must be a number")
if not isinstance(valence, (int, float)):
raise ValueError("Valence must be a number")
self.modality = modality
self.intensity = max(0.0, min(1.0, float(intensity)))
self.valence = max(-1.0, min(1.0, float(valence)))
self.timestamp = self._get_current_time() if timestamp is None else timestamp
self.decay_rate = 0.05
self.latency = self.MODALITY_LATENCIES.get(modality, 0.2) # デフォルトは0.2秒
"""現在時刻を取得"""
def apply_decay(self, time_passed):
self.intensity = max(0.0, self.intensity - (time_passed * self.decay_rate))
return self.intensity
valence_str = "negative" if self.valence < 0 else "positive" if self.valence > 0 else "neutral"
return f"SensorySignal({self.modality}, intensity={self.intensity:.2f}, valence={valence_str}, latency={self.latency:.2f}s)"
"""未処理感情を表すクラス。言語ラベル未確定の感覚群を管理し、認知プロセスの途中段階をモデル化。
記憶アクセスは再編集として扱い、言語化プロセスを動的に進める。
"""
def __init__(self, raw_signals=None, salience=0.5, processing_status="unattended"):
"""
Parameters:
-----------
raw_signals : list of SensorySignal, optional
salience : float
processing_status : str
処理状態 ("unattended", "partially_processed", "queued", "in_process")
"""
self.raw_signals = raw_signals if raw_signals is not None else []
self.salience = max(0.0, min(1.0, salience))
self.processing_status = processing_status
self.pattern_matches = {}
self.creation_time = self._get_current_time()
self.last_accessed_time = self.creation_time
self.access_count = 0
self.structure_level = 0.0
self.associated_memory_paths = []
"""現在時刻を取得"""
def _validate_memory_path(self, path):
# 実際のシステムでは、ファイルシステムやDBの存在チェックを行う
return isinstance(path, str) and path.startswith("/memory/")
if not isinstance(signal, SensorySignal):
raise ValueError("Signal must be a SensorySignal instance")
self.raw_signals.append(signal)
self.structure_level = max(0.0, self.structure_level - 0.1)
self.last_accessed_time = self._get_current_time()
self.access_count += 1
def add_language_candidate(self, term, confidence):
self.language_candidates.append({
"term": term,
"timestamp": self._get_current_time()
})
self.structure_level = min(1.0, self.structure_level + 0.05)
self.last_accessed_time = self._get_current_time()
self.access_count += 1
def add_pattern_match(self, pattern_name, similarity):
self.pattern_matches[pattern_name] = {
"similarity": similarity,
"timestamp": self._get_current_time()
}
self.structure_level = min(1.0, self.structure_level + 0.1)
self.last_accessed_time = self._get_current_time()
self.access_count += 1
def add_memory_path(self, path):
if not self._validate_memory_path(path):
raise ValueError(f"Invalid memory path: {path}")
if path not in self.associated_memory_paths:
self.associated_memory_paths.append(path)
self.last_accessed_time = self._get_current_time()
self.access_count += 1
def apply_decay(self, time_passed):
for signal in self.raw_signals:
signal.apply_decay(time_passed)
decay_modifier = max(0.1, 1.0 - (self.access_count / 100.0))
decay_amount = time_passed * 0.02 * decay_modifier
structure_modifier = max(0.5, 1.0 - self.structure_level)
decay_amount *= structure_modifier
self.salience = max(0.0, self.salience - decay_amount)
return self.salience
def get_average_valence(self):
if not self.raw_signals:
return 0.0
total_valence = sum(signal.valence for signal in self.raw_signals)
return total_valence / len(self.raw_signals)
def get_dominant_modality(self):
if not self.raw_signals:
return None
for signal in self.raw_signals:
modality_strengths[signal.modality] = modality_strengths.get(signal.modality, 0) + signal.intensity
return max(modality_strengths.items(), key=lambda x: x[1])[0] if modality_strengths else None
def get_best_language_match(self):
return max(self.language_candidates, key=lambda x: x["confidence"]) if self.language_candidates else None
best_lang = self.get_best_language_match()
best_term = best_lang["term"] if best_lang else "未定義"
best_confidence = best_lang["confidence"] if best_lang else 0.0
return {
"creation_time": self.creation_time,
"age": self._get_current_time() - self.creation_time,
"status": self.processing_status,
"salience": self.salience,
"structure_level": self.structure_level,
"signal_count": len(self.raw_signals),
"dominant_modality": self.get_dominant_modality(),
"average_valence": self.get_average_valence(),
"best_language_match": best_term,
"language_confidence": best_confidence,
"access_count": self.access_count,
"memory_path_count": len(self.associated_memory_paths)
}
status = self.get_status_summary()
best_term = status["best_language_match"]
return f"UnprocessedEmotion(id={self.id}, status={self.processing_status}, salience={self.salience:.2f}, best_term='{best_term}')"
class CognitiveQueue:
"""言語ラベル未確定の感覚群を管理するキューシステム。認知プロセスの優先順位付けと記憶再編集をサポート。
"""
def __init__(self, max_size=100, attention_threshold=0.3):
"""
Parameters:
-----------
max_size : int
attention_threshold : float
"""
self.unprocessed_emotions = []
self.processing_queue = []
self.archived_emotions = []
self.attention_threshold = attention_threshold
self.current_time = self._get_current_time()
self.learned_terms = {} # 学習済み言語表現: {term: {"context": str, "frequency": int}}
self.modality_index = {} # モダリティごとの感情インデックス: {modality: [emotion]}
"""現在時刻を取得"""
self.current_time = time.time()
return self.current_time
def learn_language_term(self, term, context):
if term in self.learned_terms:
self.learned_terms[term]["frequency"] += 1
else:
self.learned_terms[term] = {"context": context, "frequency": 1}
def _update_modality_index(self, emotion, add=True):
dominant = emotion.get_dominant_modality()
if dominant:
if add:
if dominant not in self.modality_index:
self.modality_index[dominant] = []
if emotion not in self.modality_index[dominant]:
self.modality_index[dominant].append(emotion)
else:
if dominant in self.modality_index and emotion in self.modality_index[dominant]:
self.modality_index[dominant].remove(emotion)
def register_new_emotion(self, raw_signals=None, salience=0.5):
salience=salience,
processing_status="unattended"
)
self.unprocessed_emotions.append(emotion)
self._update_modality_index(emotion)
if len(self.unprocessed_emotions) > self.max_size:
least_salient = min(self.unprocessed_emotions, key=lambda e: e.salience)
self.unprocessed_emotions.remove(least_salient)
self._update_modality_index(least_salient, add=False)
least_salient.processing_status = "archived_without_processing"
self.archived_emotions.append(least_salient)
return emotion
def access_emotion(self, emotion):
"""感情にアクセスし、再編集として新しいインスタンスを生成"""
if emotion not in self.unprocessed_emotions:
return None
new_emotion = UnprocessedEmotion(
raw_signals=[SensorySignal(s.modality, s.intensity, s.valence, s.timestamp) for s in emotion.raw_signals],
salience=emotion.salience,
processing_status=emotion.processing_status
)
new_emotion.structure_level = emotion.structure_level * 0.9
new_emotion.language_candidates = emotion.language_candidates.copy()
new_emotion.pattern_matches = emotion.pattern_matches.copy()
new_emotion.associated_memory_paths = emotion.associated_memory_paths.copy()
self.unprocessed_emotions.append(new_emotion)
self._update_modality_index(new_emotion)
emotion.processing_status = "archived_due_to_access"
self.unprocessed_emotions.remove(emotion)
self._update_modality_index(emotion, add=False)
self.archived_emotions.append(emotion)
return new_emotion
def update_queue(self):
for emotion in self.unprocessed_emotions[:]:
time_passed = self.current_time - emotion.last_accessed_time
emotion.apply_decay(time_passed)
self.unprocessed_emotions.remove(emotion)
self._update_modality_index(emotion, add=False)
emotion.processing_status = "archived_due_to_low_salience"
self.archived_emotions.append(emotion)
self.processing_queue = []
for emotion in self.unprocessed_emotions:
if emotion.salience >= self.attention_threshold:
if emotion.processing_status == "unattended":
emotion.processing_status = "queued"
self.processing_queue.append(emotion)
self.processing_queue.sort(key=lambda e: e.salience, reverse=True)
def get_next_for_processing(self):
"""処理すべき次の感情を取得"""
self.update_queue()
if not self.processing_queue:
return None
emotion = self.processing_queue[0]
emotion.processing_status = "in_process"
emotion.last_accessed_time = self.current_time
emotion.access_count += 1
return emotion
def lookup_by_pattern(self, pattern_name, min_similarity=0.5):
matches = []
for emotion in self.unprocessed_emotions:
if pattern_name in emotion.pattern_matches:
similarity = emotion.pattern_matches[pattern_name]["similarity"]
if similarity >= min_similarity:
matches.append(emotion)
emotion.last_accessed_time = self.current_time
emotion.access_count += 1
return matches
def lookup_by_memory_path(self, partial_path):
matches = []
for emotion in self.unprocessed_emotions:
for path in emotion.associated_memory_paths:
matches.append(emotion)
emotion.last_accessed_time = self.current_time
emotion.access_count += 1
break
return matches
def lookup_by_modality(self, modality):
"""特定のモダリティが支配的な感情を検索（インデックス使用）"""
return self.modality_index.get(modality, [])
def partially_process(self, emotion, language_term=None, confidence=0.0, context=None):
if emotion not in self.unprocessed_emotions:
return False
if language_term:
emotion.add_language_candidate(language_term, confidence)
if context:
self.learn_language_term(language_term, context)
emotion.structure_level = min(1.0, emotion.structure_level + 0.15)
emotion.processing_status = "partially_processed"
emotion.last_accessed_time = self.current_time
emotion.access_count += 1
if emotion.structure_level >= 0.9:
best_lang = emotion.get_best_language_match()
if best_lang and best_lang["confidence"] >= 0.8:
self.unprocessed_emotions.remove(emotion)
self._update_modality_index(emotion, add=False)
emotion.processing_status = "archived_fully_processed"
self.archived_emotions.append(emotion)
return True
modality_counts = {}
for emotion in self.unprocessed_emotions:
dominant = emotion.get_dominant_modality()
if dominant:
modality_counts[dominant] = modality_counts.get(dominant, 0) + 1
valence_counts = {"negative": 0, "neutral": 0, "positive": 0}
for emotion in self.unprocessed_emotions:
avg_valence = emotion.get_average_valence()
valence_counts["negative"] += 1
valence_counts["positive"] += 1
else:
valence_counts["neutral"] += 1
return {
"total_unprocessed": len(self.unprocessed_emotions),
"processing_queue_size": len(self.processing_queue),
"archived_count": len(self.archived_emotions),
"average_salience": sum(e.salience for e in self.unprocessed_emotions) / max(1, len(self.unprocessed_emotions)),
"average_structure_level": sum(e.structure_level for e in self.unprocessed_emotions) / max(1, len(self.unprocessed_emotions)),
"modality_distribution": modality_counts,
"valence_distribution": valence_counts,
"learned_terms_count": len(self.learned_terms),
"current_time": self.current_time
}
Studio Ghibli, the legendary Japanese animation studio, has produced some of the most beloved animated films of all time. From Spirited Away to My Neighbor Totoro, these movies enchant audiences with their breathtaking animation, deep storytelling, and emotional depth. If you're wondering where to watch Studio Ghibli movies legally, this guide will cover all the streaming platforms, physical media options, and theatrical screenings available worldwide.
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1. Streaming Platforms for Studio Ghibli Movies
A. HBO Max (Now Max) – United States
In 2020, HBO Max secured exclusive streaming rights to Studio Ghibli films in the U.S. After HBO Max rebranded to Max in 2023, the entire Ghibli catalog remains available. use a VPN to unbloked ghibli moviee
Included Films:
• Howl’s Moving Castle
• Ponyo
• The Wind Rises
• And many more!
Availability: All films are offered in both subbed (Japanese with English subtitles) and dubbed (English voiceovers) versions.
B. Netflix – Select Regions (Outside the U.S.) Use VPN to acces this Platfrom.
While Max holds U.S. rights, Netflix has Studio Ghibli films in several countries, including:
• Japan
• Canada
• Australia & New Zealand
• Parts of Europe & Latin America
Note: Availability varies by region, so check your local Netflix library.
C. Amazon Prime Video (Rent/Buy)
If a Ghibli film isn’t on a streaming service in your country, Amazon Prime Video often offers rentals or purchases. Prices typically range from
3.99
t
o
3.99to14.99 per movie.
D. Apple TV, Google Play, Vudu, Microsoft Store
Similar to Amazon, these platforms allow digital purchases or rentals of Studio Ghibli films.
2. Physical Media: Blu-rays & DVDs
For collectors, physical copies provide the best quality and bonus features
A. GKIDS & Shout! Factory Releases (North America)
GKIDS, in partnership with Shout! Factory, distributes official Blu-rays and DVDs of Studio Ghibli films in the U.S. and Canada.
Key Features
Both Japanese and English audio
Special features (interviews, documentaries, trailers)
Where to Buy:
Best Buy
Barnes & Noble
Right Stuf Anime (for collectors)
B. StudioCanal Releases (UK & Europe)
In Europe, StudioCanal handles Ghibli’s physical releases. They offer 4K Ultra HD, Blu-ray, and DVD editions.
Where to Buy:
Zavvi
C. Madman Entertainment (Australia & New Zealand)
Madman distributes Ghibli films in Australia and New Zealand.
3. Theatrical Screenings & Special Events
Studio Ghibli films occasionally return to theaters for special screenings.
A. Fathom Events & GKIDS Theatrical Releases
GKIDS frequently partners with Fathom Events to bring Ghibli films back to U.S. theaters.
Check Fathom Events’ website or local theaters for upcoming showings.
B. Ghibli Fest (Annual Event)
Every year, GKIDS organizes Ghibli Fest, a series of theatrical re-releases. Past years have included:
Spirited Away (20th Anniversary)
Nausicaä of the Valley of the Wind
C. International Film Festivals
Some film festivals, like TIFF (Toronto International Film Festival) or Japan Society (NYC), host Ghibli retrospectives.
4. Free Legal Options (Limited Availability)
While most Ghibli films require purchase or subscription, some platforms offer limited-time free viewing.
A. Tubi (Ad-Supported Free Streaming)
Tubi occasionally rotates Studio Ghibli films in its catalog. Check periodically for availability.
B. Pluto TV (Ghibli Channel in Japan)
In Japan, Pluto TV has a dedicated Studio Ghibli channel. A VPN may be required for access outside Japan.
5. Studio Ghibli’s Official Platform: Ghibli Museum & Ghibli Park
If you’re in Japan, visiting the Ghibli Museum (Mitaka, Tokyo) or the newly opened Ghibli Park (Aichi) offers exclusive short films not available elsewhere.
Ghibli Museum: Features The Whale Hunt and other exclusive shorts.
Ghibli Park: A theme park with immersive exhibits based on Ghibli films.
6. What About Piracy? Why You Should Avoid It
While illegal streaming sites may offer Ghibli films for free, they:
Harm the creators (Studio Ghibli relies on legal sales).
Offer poor quality (low resolution, incorrect subtitles).
Supporting official releases ensures that Studio Ghibli can continue making beautiful films.
Final Verdict: Best Way to Watch Studio Ghibli Movies
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Method Best For Availability
Max (HBO Max) U.S. streaming Full Ghibli library
Netflix International viewers Varies by country
Blu-ray/DVD Collectors & best quality Worldwide (GKIDS/StudioCanal)
Theaters (Ghibli Fest) Big-screen experience Limited-time screenings
Digital Purchase Owning digital copies Amazon, Apple, Vudu
Whether you prefer streaming, physical copies, or theatrical experiences, there are plenty of legal ways to enjoy Studio Ghibli’s magical films. Max (U.S.) and Netflix (international) are the top streaming choices, while GKIDS Blu-rays offer the best home viewing experience. Keep an eye on Ghibli Fest for special cinema screenings!
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まあまあ、思い出補正で期待値が高すぎるけどこんなもんだと思う
動きのクセが強いのと、キャラデザに違和感がある、これ青山先生の後期のキャラデザかな？もう少しコナンっぽい見た目のイメージしてた
まあまあ、ギャグ度がちょっと高めのラブコメ・異能者が増えていくタイプ？人気は出そう
テンプレだけど、諸々10年前20年前よりキャラ付けがマイルドになってる気がする、時代かな？マイルドでいいけどね
これひょっとして話の構成はドラえもんなのか？ぷにるとかもそうだよね
お前1分アニメかよ
体型が好み
感動系日常系？
老若男女が見れそう
魔女っ子に最初から人徳があるのって珍しいよね、時代かな、新鮮でよい
人気出るけど苦手なんだよね
主人公の演技、絵の塗りのテイスト、話の散らかり方、これは人を選びそう
キャラが多すぎる、3話くらい見てみようかな、脱落する予感しかしないが
ゲームのアニメ化って大抵モチベーションの起き方が難しい、ストーリーと設定の分量が多い割に薄いから
ちらっと見た、うーんスキップ
ちらっと見た、見るかも
髪の様子がおかしくて好き
結構良かった
ゆるキャン＋水ダウって感じかな？
これ見ていいのかな、先にゲームやるべきな気がしてならない
アニメのクオリティは高いが、ノリがKeyゲームなので映像化するとうっとなることあるよな
原作勢曰く、後半はグダるらしい
アクションメイン？
ちょっと苦手
なんだこの会話、一人で喋ってるみたいで気持ち悪い
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.
グローバル10％関税について、何かしらの政策検討したレポートあるのではと思って調べたがよくわからん。
Oren Cass、シンクタンクAmerican Compassが、相互関税後の米国がどのようになるか想定しているでしょうか？
Key Points
研究は、Oren CassとAmerican Compassが相互関税後のアメリカを国内製造業が強化され、高品質な雇用が増加し、貿易赤字が縮小した状態と想像していることを示唆しています。
彼らは関税を国内生産を奨励し、アメリカの労働者とコミュニティを優先するツールと見なしています。
消費者価格の上昇や他の国からの報復関税の可能性も考慮されますが、具体的な未来像は議論の余地があります。
Oren CassとAmerican Compassは、相互関税がアメリカ経済にポジティブな影響を与えると信じています。具体的には、国内製造業が保護され、工業部門での雇用が増加し、貿易赤字が縮小する未来を想像しているようです。特に、中国との競争に対抗するために関税を有効なツールと見なし、アメリカの産業基盤を強化したいと考えています。
予想される影響
このシナリオでは、国内生産が増えることでアメリカの経済的自立度が高まり、労働者の生活水準が向上する可能性があります。ただし、輸入品の価格上昇により消費者価格が上がるリスクや、他の国からの報復関税による輸出への影響も指摘されています。これらの影響は複雑で、経済全体に波及効果をもたらすと予想されます。
予想外の詳細
興味深いことに、American Compassは関税収入を主権財産基金に投資し、インフラや医療研究に使う提案も検討しているようです。これは、単なる貿易政策を超えた長期的な経済戦略を示唆しています。
https://www.spectator.co.uk/podcast/was-free-trade-really-working/
ここのポッドキャストの内容を要約してください
「https://www.spectator.co.uk/podcast/was-free-trade-really-working/」のポッドキャストの内容をさらに詳しく要約し、具体的なポイントを掘り下げて説明します。この対談は、2025年2月19日に公開され、The SpectatorのFreddy Grayがホストを務め、American CompassのOren Cassをゲストに迎え、ロンドンのARC会議で収録されました。以下に詳細を述べます。
詳細な内容
ポッドキャストは、トランプ大統領が2025年初頭に発表した「相互関税」（reciprocal tariffs）の話題から始まります。これは、アメリカが他国から課される関税率に合わせて自国の輸入関税を設定する政策で、特に中国やその他の貿易相手国との不均衡を是正する狙いがあります。Freddy Grayは、この政策が最近のホワイトハウスの方針（例えば、2025年2月のカナダ、メキシコ、中国からの輸入に対する関税引き上げ）に結びついていると指摘し、その意図をCassに尋ねます。
Oren Cassは、この関税が単なる保護主義ではなく、アメリカの経済的自立と労働者保護を強化するための戦略的ツールであると説明します。彼は、過去数十年の自由貿易政策がアメリカの製造業を空洞化させ、貿易赤字を拡大させたと考えており、相互関税が国内生産を再活性化させる一つの方法だと主張します。
2. 自由貿易への疑問
対談の中心的なテーマは、「自由貿易は本当に機能していたのか？」という問いです。Cassは、1990年代から続いてきた自由貿易への政治的合意が、アメリカ経済に必ずしも利益をもたらさなかったと批判します。特に、中国のWTO加盟（2001年）以降、アメリカの製造業が衰退し、多くの労働者が低賃金のサービス業にシフトした点を挙げます。
具体例として、中国製電気自動車（EV）の台頭を取り上げます。Cassは、中国の補助金付きEVがアメリカ市場に流入することで、国内の自動車産業や投資家が競争力を失っている「現実の生活」を強調します。Grayが「自由貿易がグローバル経済の成長を促進したという主張はどうか」と尋ねると、Cassは「それは一部の多国籍企業や投資家には当てはまるが、アメリカの労働者や地域社会にはほとんど恩恵がなかった」と反論します。
Grayは、相互関税がアメリカ企業にどのような影響を与えるか質問します。Cassは、短期的には輸入コストの上昇による混乱が予想されると認めつつ、長期的には国内生産を奨励し、製造業の雇用を増やす可能性があると楽観視します。例えば、関税によって中国からの安価な輸入品が減れば、アメリカの自動車や鉄鋼産業が再び競争力を取り戻すかもしれないと述べます。
しかし、彼はリスクも認識しており、他の国からの報復関税がアメリカの輸出産業（特に農業や技術分野）に打撃を与える可能性を指摘します。それでも、「自由貿易の幻想にしがみつくよりは、現実的な政策で国家の利益を守るべきだ」と主張します。
Cassは、トランプの関税政策が政治的な支持を得ている背景にも触れます。2024年の選挙で、労働者階級の有権者が貿易赤字や製造業の衰退に不満を抱き、保護主義的な政策を支持したことを挙げます。彼は、これが共和党内の経済政策の転換を示しており、American Compassが提唱する「労働者優先」のアジェンダと一致していると述べます。
Grayは、ヨーロッパの視点からこの政策をどう見るか質問します。Cassは、ヨーロッパ諸国も同様に中国との貿易不均衡に直面しているが、アメリカのような大胆な関税政策を取る政治的意志が欠けていると分析します。
意外なポイントとして、Cassは関税収入の活用についても言及します。彼は、American Compassが提案するように、関税で得た資金を主権財産基金に投入し、インフラや教育に再投資する案に触れます。これは、単に貿易を制限するだけでなく、経済全体を強化する長期的なビジョンを示唆しています。
対談の最後で、Grayが「自由貿易の時代は終わりを迎えたのか」と尋ねると、Cassは「少なくとも、アメリカではその前提が再考されるべき時期に来ている」と締めくくります。彼は、相互関税が完璧な解決策ではないものの、現在のグローバル経済におけるアメリカの地位を再定義する一歩だと結論付けます。
Leaf、Key設立メンバーと振り返る「90年代美少女ゲーム界」最前線──『ときメモ』に挑んだ『ToHeart』、伝説の名曲『鳥の詩』制作秘話、時代を変えたKeyの「泣きゲー」etc……あの時代の“熱狂”に迫る
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ときメモが流行った時、20代で「この界隈に入ったら、現実に戻ってこれなくなる！」と恐怖心が出て、結構なヲタクだったけど、手を出さずにいた。
で、30代になって、めでたくパートナーと出会い、相方もヲタクだったので、いろいろとヲタク家業を復帰して、Kanonとかに手を出していたら、98時代からの「ランス」フリークだと白状された。
Rance35th Anniversary Boxとかも、しっかり注文してるしね。
RopeもといBigList+GapBufferでさくさくエディターの置き換え処理をやってみた。
使用したマシンはCore i7 14700、メモリー32GB、Intel ARC A750。
１００万行×１００文字を置き換え。
replace1は３文字削除の３文字挿入で、３．５秒ぐらいで終わる。
replace2は３文字削除の４文字挿入で、４．９秒ぐらいで終わる
replace3は４文字削除の３文字挿入で、３．６秒ぐらいで終わる。
enumratotionはNULデバイスへの保存で、１．１秒で終わる。
benchmark start
Allocated GC Memory:60,600bytes
Allocated GC Memory:199,388,824bytes
Allocated GC Memory:199,405,136bytes
Allocated GC Memory:368,390,560bytes
Allocated GC Memory:368,390,560bytes
Allocated GC Memory:368,390,736bytes
clear buffer
Allocated GC Memory:84,360bytes
１００万行の行とインデックスの変換テーブルの追加と更新処理はこのくらいで終わる。
変換テーブルの処理は構築が０．２秒、行が増えない更新は０．１秒ぐらいで終わる。
Allocated GC Memory:32,805,312bytes
Allocated GC Memory:32,811,000bytes
clear buffer
Allocated GC Memory:84,616bytes
以下、ソースコード。
「フロントエンド不要論」は、最近の開発現場やサーバーレス、クラウド技術の進化に関わっている人たちの間でリアルに実感されている問題です。
• React, Vue, Angular などのフレームワークがどんどん複雑化
• フロントエンドとバックエンドの分離が、**「本当に効率的か？」**という疑問が生じている
• 「最終的にHTMLを描画するだけなら、サーバーでやればよくない？」
• フロントエンドから直接APIを叩く構成では、「APIを守る」ことが難しい
• XSS, CSRF, CORSといった脆弱性に対処し続けるコストが無駄
🚩 3. サーバーレス・クラウド技術が進化し、APIの負担を減らす方向に
• AWS Lambda, API Gateway, Cognitoなどのサーバーレス技術が進化
• フロントエンドがAPIを叩くより、サーバー側で直接処理する方が効率的
• 以前はReactを使用 → ReactをやめてHTMLベースに戻した
• React, Vue, Angularを全廃
• JavaScriptなしで動的なページを実現
3. Laravel（Livewire）
4. Shopify（GraphQLでデータを直接取得）
• フロントエンドを完全分離する構成から、「バックエンドがHTMLを返せばいい」 というシンプルな構成へ移行
• APIの負担を減らすことで、開発効率とセキュリティを向上
✅ サーバーレス時代の最適解：「フロントエンド不要アーキテクチャ」
「フロントエンドを捨てて、サーバーがすべての処理を担う」方向に移行するのが最適解になりつつある。
📌 最適なアーキテクチャ
ブラウザ → サーバー（PHP, Node.js, Go） → API Gateway（Cognito認証）
📌 具体的な実装例（PHP + Cognito + API Gateway）
require 'vendor/autoload.php';
use Aws\CognitoIdentityProvider\CognitoIdentityProviderClient;
use Aws\Exception\AwsException;
$client = new CognitoIdentityProviderClient([
'credentials' => [
'key' => getenv('AWS_ACCESS_KEY_ID'),
'secret' => getenv('AWS_SECRET_ACCESS_KEY'),
],
]);
$email = $_POST['email'];
$password = $_POST['password'];
try {
$result = $client->initiateAuth([
'AuthFlow' => 'USER_PASSWORD_AUTH',
'ClientId' => 'XXXXXXXXXX',
'USERNAME' => $email,
],
]);
setcookie("accessToken", $result['AuthenticationResult']['AccessToken'], [
'samesite' => 'Strict'
]);
header("Location: dashboard.php");
}
?>
🚀 **「フロントエンドはもう不要」**という流れは、最新のクラウド/サーバーレス開発に携わる人たちが実感していること。
☑ セキュリティが大幅に向上する
豊川家の跡取り候補だった瑞穂の長兄(姉)が島の女(男)と駆け落ち、瓜二つの双子の娘が誕生。姉の初音は社交的で陽気な性格、妹の初華は内向的で控えめな性格。島には他に子供もおらず2人きり、2人まとめて音華と呼ばれていた。
→幼少期島の別荘に来た祥子と初華が出会う。何らかの理由（ただの遊び？）で初音と入れ違い「初華」として祥子と遊ぶ。記憶の齟齬があるのは初華は一部初音からの話を基に想像して話しているため。瑞穂に会ったのは初音。2人にとって祥子は初めての友人となる。
→双子の両親が離婚。父方に初音がついて島を出る。母方には初華がつき苗字も「三角」となり島で孤独に過ごす。初音とは生き別れとなる。
→(母親が死亡等きっかけがあった結果？)初華は初音を探す、見つけてもらうため島を出てアイドルになる。よくスマホでファンのコメントを見たり、愛音にDMを送ったのは初音探しの一環で普段から行っているため。
→そんな中祥子にムジカへ勧誘される。初音探しを半ば諦めかけていた初華に強い福音を与え、(自身と同じく母親を失った境遇を知り？)祥子へ姉に向けていたのと同じ友情を超えた感情を抱くようになる。
→祥子の思い出話に現れるのはかつての「初音が演じる初華」であり、自身も期待に答えるべく「初華が演じる初音」として秘密を抱えたまま振る舞うようになる。
→しかし人間的な経験値が絶対的に不足している初華では「初音」を演じることは出来ず、他人の真似事で取り繕ったり、祥子の意に反するような行為をとってしまう。祥子もかつての「初華」を期待していたギャップもあり不信感を強めていく。
→ムジカ解散。祥子への想いを募らせる中クライシック再結成を知る。今まで見たことのなかった祥子の表情と再結成の意思を知り(勘違い)、クライシックへの強い嫉妬と再び姉を失う恐怖に取り憑かれる。
→祥子を奪いかねない睦や燈に害意に近い敵意を覚えながらも、紆余曲折を経てムジカ再結成。祥子が歌を通じて自身の歪んだ想いを受け止めてくれたと捉え依存を強める中、初音の存在を知る祖父によって今までの秘密が明るみとなる事態へ。
→祥子の意図を汲み取れなかった事もあるが、1番は初音に気づいてもらうため。
→森みなみのような優れた演技の感性がある人間でなければ分からなかった上、幼馴染2人が多重人格と2人で1人を演じる双子の異常者で普通の人間感覚が育たなかったため。
→祥子を姉の初音に代わる新たな家族として捉えており、かつて自身が初音から受けていたスキンシップを祥子にも行っている。布団で枕抱えたり衣装盗んで人形もどきを作ってる？それはまあ...
→元々長兄(姉)へ過干渉を行った結果出奔された経緯があり、その反省から孫娘達には深く干渉するつもりは無かった。初華は出奔した側の孫のため尚のこと。しかし家を訪れたり、ムジカ再結成など意図がわからないことから自身が関わることを決めた。
誰も悪人では無く株を落とさずそんな気持ち悪い(初華は除く)ことも無いならこんな話になるのかなと思います。バンドリ的にもこれ以上過激な話にしていいのかどうかのラインはありそうですが。初華が祥子にとって燈の代わりであるように、祥子も初華にとって姉初音の代わりだったという落とし所か。燈と愛音の出会いがMyGO!!!!!結成のきっかけになったように、初華と初音が生まれたことがAveMujica結成の発端だったとか。燈が星を探し始める物語と、初華が星を見つける物語になるとか対比的になって収まりはいいんじゃないかと思います。
Cパートで初音登場次話で初華との再会を果たすとか、音華＝初華であのさー＝初音かその逆とか、初音も初華と祥子に気づいていたがスルーしていたとかまだ波乱はありそう。なんとなく初音は演技の道に進んでそうな感じはあります。
最終回はドロリス(Vo./Gt.)マナリス(Gt.）ミクリス(Ba.)オブリビオニス(Key.）倉田ましろ(Vo./Do./Vn.）のTGWグループでライブをして終了
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.
肥田式強健術は、肥田春充が開発した身体と心のトレーニング方法で、大正から昭和初期に人気がありました。
この方法は、身体の中心（特に「丹田」）を強化し、健康と強さを築くことを重視します。
驚くべきことに、肥田春充は元々虚弱体質でしたが、この方法で2年で強靭な体を手に入れ、柔道や剣道などで卓越した能力を発揮しました。
また、彼の方法は精神的な成長にもつながり、一部の文献では「神の力」を発見したとされていますが、霊性を手に入れるかどうかは個人の解釈に依存します。
肥田春充とは？
肥田春充（1883-1956）は、日本の健康法の創始者で、元々虚弱体質だった彼が自ら開発した方法で超人的な体を手に入れました。彼は明治大学柔道部の創設者としても知られ、柔道や剣道、弓術などで優れた成績を残しました。
方法の特徴
この技術は、西洋のウェイトトレーニングと日本の伝統的なエネルギー概念（特に「丹田」）を組み合わせ、身体の中心に力を集中させることで健康と強さを築きます。1930年代には多くの人々に採用され、昭和天皇もその本を読んでいた記録があります。
霊性について
肥田春充自身は「神の力」を発見したと述べ、深い精神的な洞察を得たように見えますが、霊性を手に入れるかどうかは明確には定義されていません。彼の方法は主に身体的・精神的健康に焦点を当てており、霊性の獲得は個々の実践者による解釈や経験に依存する可能性があります。
肥田春充の「肥田式強健術」について、以下の詳細な調査を行いました。この方法は、大正から昭和初期にかけて注目された健康と身体訓練の方法であり、その背景や特徴、影響力を明らかにします。
肥田春充の背景
肥田春充は1883年に生まれ、1956年に亡くなりました。彼は元々虚弱体質で、幼少期に麻疹で重病を患い、18歳まで中学校に入学できなかったほどでした（My Dad Is an OTAKU）。しかし、彼は自身の方法を通じて身体を強化し、「超人」と呼ばれるほどの強靭な体を手に入れました。この過程は、彼の伝記や関連文献で詳述されています。
ユーザーのクエリでは「肥田式強健術」と記載されていますが、これは「Hida Shiki Kyoken Jutsu（肥田式強健術）」とも呼ばれます。Quest Stationのページ（Quest Station）では、「Hida Shiki Kyoken Jutsu」が「心身を鍛える究極の方法」と紹介されており、健康と身体の動きに焦点を当てています。
「Kyoken Jutsu」の意味については、具体的な漢字は明らかではありませんが、文脈から「強健な技術」や「健康強化の方法」と解釈されます。webhiden.jp（webhiden.jp）では、「肥田式強健術」が中心的な名称として扱われており、以下の表でその発展段階がまとめられています。
段階
年
初期開発
10種類のエクササイズに焦点、下腹部の緊張と「気合（kiai）」を強調。
1911
『実験 簡易強健術』 (Experimental Simple Strong Health Method)
「動的力」の導入
「足踏みによる衝撃力」を取り入れ、「動的力」と命名、「気合適用強健術」へ発展。
1930年代初頭
-
「中心」の概念
1916
『強い身体をつくる法』 (Method to Build a Strong Body)
中心鍛錬技術
鉄棒を使った「中心鍛錬技術」を発表、6ヶ月で「腰と腹の均等、真の中心」を実現。
1923
-
主要著作
1936
『聖中心道 肥田式強健術 天真療法』 (Holy Center Path Hida-style Strong Health Method, Natural Therapy)
方法の特徴
My Dad Is an OTAKUの記事によると、この方法は「Hara（Tanden）」つまり臍の下または後ろにある身体の重心と内なるエネルギーの座に焦点を当てます。具体的には、腹部と下背部を均等に緊張させ、身体の中心に力を集中させることで、加速的にパワーを高めます。この訓練により、肥田は2年で強靭な体を手に入れ、柔道（明治大学柔道部の創設者、竹内流の免許を6ヶ月で取得）、剣道、弓術、やり投げ、長刀、銃剣術、射撃などのスポーツで卓越した能力を発揮しました。
また、アメリカのボクサー、マッカーサー・フォスターやムハンマド・アリもこのKyoken-jutsuを学んだとされています（My Dad Is an OTAKU）。これは国際的な影響力を持つ証拠です。
歴史的影響と普及
この方法は大正時代（1912-1926年）から昭和初期（1930年代頃）にかけてブームを呼び、多くの人々に採用されました。1936年の『聖中心道 肥田式強健術 天真療法』は、その包括的な内容で知られ、昭和天皇も読んでいたとされています（webhiden.jp）。Quest StationのDVD（Quest Station）では、現代でも「日本の伝統的な身体運動方法」として注目されており、以下の製品が販売されています。
Product ID
Description
Length
Language
Format
Other Info
Price
SPD-9412
Hida Shiki Kyoken Jutsu Introduction
Basic principles and movements
70min
Japanese with English subtitles
4,500PYY
SPD-9413
Hida Shiki Kyoken Jutsu Practical Methods
Breaking methods, correct movement, center training
106min
Japanese with English subtitles
4,500PY
SPD-9416
Hida Shiki Kyoken Jutsu Introduction and Practical Methods
2 DVD set of Introduction and Practical Methods
176min
Japanese with English subtitles
8,500PY
ユーザーのクエリでは、「肥田式強健術を極めると霊性を手に入れることができるか？」と問われています。肥田春充自身は、「神の力」を発見したと述べ、深い精神的な洞察を得たように見えます（My Dad Is an OTAKU）。例えば、1954年にポール・レプスが訪問した際、70代の肥田が30代のように見えたと報告されています。これは、彼の方法が単なる身体訓練を超え、精神的な次元に影響を与えた可能性を示唆します。
しかし、霊性の獲得は明確に定義されておらず、個々の実践者による解釈や経験に依存する可能性があります。webhiden.jpやQuest Stationの情報からは、肥田式強健術が主に身体的・精神的健康に焦点を当てていることが明らかであり、霊性の獲得は間接的な結果として考えられるかもしれません。
調査はまず肥田春充の名前でウェブ検索を行い、Quest Station、Wikidata、webhiden.jp、Amazonの書籍、ブログ記事（My Dad Is an OTAKU）などの結果を得ました。Quest Stationのページでは、Hida Shiki Kyoken Jutsuが「心身を鍛える究極の方法」と紹介されており、webhiden.jpでは発展段階が詳細に記載されていました。
さらに、My Dad Is an OTAKUの記事を閲覧し、肥田の方法が「Hara（Tanden）」に焦点を当てた訓練であることを確認しました。これにより、「肥田式強健術」が肥田の健康法の翻訳であると結論付けました。
「肥田式強健術」は、肥田春充が開発した身体と心の訓練方法で、大正から昭和初期に広く普及しました。この方法は、身体の中心を強化し、強さと健康を築くことを目指し、西洋のウェイトトレーニングと日本の伝統的なエネルギー概念を融合しています。その影響力は国内外に及び、現代でもDVDや書籍（webhiden.jp）で学ぶことができます。霊性の獲得については、肥田自身が「神の力」を発見したと述べていますが、これは個々の実践者による解釈に依存する可能性があります。
Key Citations
Hida Shiki Kyoken Jutsu Introduction Quest Station
肥田式強健術は、肥田春充が開発した身体と心のトレーニング方法で、大正から昭和初期に人気がありました。
この方法は、身体の中心（特に「丹田」）を強化し、健康と強さを築くことを重視します。
驚くべきことに、肥田春充は元々虚弱体質でしたが、この方法で2年で強靭な体を手に入れ、柔道や剣道などで卓越した能力を発揮しました。
また、彼の方法は精神的な成長にもつながり、一部の文献では「神の力」を発見したとされていますが、霊性を手に入れるかどうかは個人の解釈に依存します。
肥田春充とは？
肥田春充（1883-1956）は、日本の健康法の創始者で、元々虚弱体質だった彼が自ら開発した方法で超人的な体を手に入れました。彼は明治大学柔道部の創設者としても知られ、柔道や剣道、弓術などで優れた成績を残しました。
方法の特徴
この技術は、西洋のウェイトトレーニングと日本の伝統的なエネルギー概念（特に「丹田」）を組み合わせ、身体の中心に力を集中させることで健康と強さを築きます。1930年代には多くの人々に採用され、昭和天皇もその本を読んでいた記録があります。
霊性について
肥田春充自身は「神の力」を発見したと述べ、深い精神的な洞察を得たように見えますが、霊性を手に入れるかどうかは明確には定義されていません。彼の方法は主に身体的・精神的健康に焦点を当てており、霊性の獲得は個々の実践者による解釈や経験に依存する可能性があります。
肥田春充の「肥田式強健術」について、以下の詳細な調査を行いました。この方法は、大正から昭和初期にかけて注目された健康と身体訓練の方法であり、その背景や特徴、影響力を明らかにします。
肥田春充の背景
肥田春充は1883年に生まれ、1956年に亡くなりました。彼は元々虚弱体質で、幼少期に麻疹で重病を患い、18歳まで中学校に入学できなかったほどでした（My Dad Is an OTAKU）。しかし、彼は自身の方法を通じて身体を強化し、「超人」と呼ばれるほどの強靭な体を手に入れました。この過程は、彼の伝記や関連文献で詳述されています。
ユーザーのクエリでは「肥田式強健術」と記載されていますが、これは「Hida Shiki Kyoken Jutsu（肥田式強健術）」とも呼ばれます。Quest Stationのページ（Quest Station）では、「Hida Shiki Kyoken Jutsu」が「心身を鍛える究極の方法」と紹介されており、健康と身体の動きに焦点を当てています。
「Kyoken Jutsu」の意味については、具体的な漢字は明らかではありませんが、文脈から「強健な技術」や「健康強化の方法」と解釈されます。webhiden.jp（webhiden.jp）では、「肥田式強健術」が中心的な名称として扱われており、以下の表でその発展段階がまとめられています。
段階
年
初期開発
10種類のエクササイズに焦点、下腹部の緊張と「気合（kiai）」を強調。
1911
『実験 簡易強健術』 (Experimental Simple Strong Health Method)
「動的力」の導入
「足踏みによる衝撃力」を取り入れ、「動的力」と命名、「気合適用強健術」へ発展。
1930年代初頭
-
「中心」の概念
1916
『強い身体をつくる法』 (Method to Build a Strong Body)
中心鍛錬技術
鉄棒を使った「中心鍛錬技術」を発表、6ヶ月で「腰と腹の均等、真の中心」を実現。
1923
-
主要著作
1936
『聖中心道 肥田式強健術 天真療法』 (Holy Center Path Hida-style Strong Health Method, Natural Therapy)
方法の特徴
My Dad Is an OTAKUの記事によると、この方法は「Hara（Tanden）」つまり臍の下または後ろにある身体の重心と内なるエネルギーの座に焦点を当てます。具体的には、腹部と下背部を均等に緊張させ、身体の中心に力を集中させることで、加速的にパワーを高めます。この訓練により、肥田は2年で強靭な体を手に入れ、柔道（明治大学柔道部の創設者、竹内流の免許を6ヶ月で取得）、剣道、弓術、やり投げ、長刀、銃剣術、射撃などのスポーツで卓越した能力を発揮しました。
また、アメリカのボクサー、マッカーサー・フォスターやムハンマド・アリもこのKyoken-jutsuを学んだとされています（My Dad Is an OTAKU）。これは国際的な影響力を持つ証拠です。
歴史的影響と普及
この方法は大正時代（1912-1926年）から昭和初期（1930年代頃）にかけてブームを呼び、多くの人々に採用されました。1936年の『聖中心道 肥田式強健術 天真療法』は、その包括的な内容で知られ、昭和天皇も読んでいたとされています（webhiden.jp）。Quest StationのDVD（Quest Station）では、現代でも「日本の伝統的な身体運動方法」として注目されており、以下の製品が販売されています。
Product ID
Description
Length
Language
Format
Other Info
Price
SPD-9412
Hida Shiki Kyoken Jutsu Introduction
Basic principles and movements
70min
Japanese with English subtitles
4,500PYY
SPD-9413
Hida Shiki Kyoken Jutsu Practical Methods
Breaking methods, correct movement, center training
106min
Japanese with English subtitles
4,500PY
SPD-9416
Hida Shiki Kyoken Jutsu Introduction and Practical Methods
2 DVD set of Introduction and Practical Methods
176min
Japanese with English subtitles
8,500PY
ユーザーのクエリでは、「肥田式強健術を極めると霊性を手に入れることができるか？」と問われています。肥田春充自身は、「神の力」を発見したと述べ、深い精神的な洞察を得たように見えます（My Dad Is an OTAKU）。例えば、1954年にポール・レプスが訪問した際、70代の肥田が30代のように見えたと報告されています。これは、彼の方法が単なる身体訓練を超え、精神的な次元に影響を与えた可能性を示唆します。
しかし、霊性の獲得は明確に定義されておらず、個々の実践者による解釈や経験に依存する可能性があります。webhiden.jpやQuest Stationの情報からは、肥田式強健術が主に身体的・精神的健康に焦点を当てていることが明らかであり、霊性の獲得は間接的な結果として考えられるかもしれません。
調査はまず肥田春充の名前でウェブ検索を行い、Quest Station、Wikidata、webhiden.jp、Amazonの書籍、ブログ記事（My Dad Is an OTAKU）などの結果を得ました。Quest Stationのページでは、Hida Shiki Kyoken Jutsuが「心身を鍛える究極の方法」と紹介されており、webhiden.jpでは発展段階が詳細に記載されていました。
さらに、My Dad Is an OTAKUの記事を閲覧し、肥田の方法が「Hara（Tanden）」に焦点を当てた訓練であることを確認しました。これにより、「肥田式強健術」が肥田の健康法の翻訳であると結論付けました。
「肥田式強健術」は、肥田春充が開発した身体と心の訓練方法で、大正から昭和初期に広く普及しました。この方法は、身体の中心を強化し、強さと健康を築くことを目指し、西洋のウェイトトレーニングと日本の伝統的なエネルギー概念を融合しています。その影響力は国内外に及び、現代でもDVDや書籍（webhiden.jp）で学ぶことができます。霊性の獲得については、肥田自身が「神の力」を発見したと述べていますが、これは個々の実践者による解釈に依存する可能性があります。
Key Citations
Hida Shiki Kyoken Jutsu Introduction Quest Station
ある人が書いたBigListを改造して、リーフノードをリンクドリストでつないだら全列挙が早くなって、スタックオーバーフローしなくなった。
ただ、その代わり元々のコードにあったノードの共有機能はいらなそうなので省くことにした。
Core i5 10400F、メモリー16GBで、100文字×100行＝1億文字を突っ込んで、あれこれ操作した場合はこのくらいの速度で動く。
benchmark start
Allocated GC Memory:60,392bytes
Allocated GC Memory:416,037,968bytes
Allocated GC Memory:416,082,104bytes
Allocated GC Memory:416,082,272bytes
Allocated GC Memory:416,082,296bytes
Allocated GC Memory:416,082,440bytes
clear buffer
ListやGapBufferだとGCに優しくないけど、BigListだとLOH入りしないので、GCに優しいのだ。
その代わり速度はBigListの中身はRopeなので、少し遅くなるのだ。
Ropeで、リーフノードをリンクドリストでつないだ場合、挿入と削除、追加、ランダムアクセスはO(Log N)、全列挙はO(N)なのだ。
MITライセンスなんで商用でも問題ないけど、元々のBigListのライセンスに不穏なことが書いてあったので、気になるなら、自分で書き直したほうがいい。
The rebalancing algorithm is from "Ropes: an Alternative to Strings", by
Boehm, Atkinson, and Plass, in SOFTWARE--PRACTICE AND EXPERIENCE, VOL. 25(12), 1315–1330 (DECEMBER 1995).
https://www.cs.tufts.edu/comp/150FP/archive/hans-boehm/ropes.pdf
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!
http://hvivi.dyndns.org/sse/replaceAll.html
さくさくエディタのまねをして全置換えでベンチマークを取ってみた。
benchmark start
Allocated GC Memory:60,392bytes
Allocated GC Memory:440,048,840bytes
Allocated GC Memory:440,082,632bytes
Allocated GC Memory:440,082,656bytes
clear buffer
Allocated GC Memory:82,280bytes
Rope<T>は削除と置き換え、文字列のランダムアクセスがO(log N)なのでさくさくエディタに比べると遅い。
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.
https://x.com/LifeTips2477/status/1879726675837288745
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消されちゃう前に保存して正解だった。
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なお、画像の配布は
で行っており、スクリプトの配布は
みたいな形で行っている。
ググると、
https://www.wantedly.com/companies/siva-s/about
■デジタル広告の業務プラットフォーム「Squad beyond」を開発しています。
デジタル業務に欠かせないクリエィティブやランディングページのビルド機能をセンターピンに、周辺に続く「レポート」「分析・解析」「改善」「最適化」など必要な全ての機能を有し、全てが連動して自動的に設定や改善が動き出すことで効率化を実現するプラットフォームです。
現ユーザー全体で、数百社・数千人のユーザーがSquad beyondの利用を通し
・100万ページ分のABテスト、最適化、PDCA、レポーティング
・100万件超のコンバージョン
を行っています。
Squad beyondが世に出るまで、これらにかかる作業や、作業同士をつなぐ設定の多くは人力で処理されていました。
我々は、「業務プラットフォーム」を再発明することで可能な限りルーチンを減らし本当に必要な仕事にフォーカスする時間をユーザーに提供します。
その結果、「良い広告コンテンツが増え、消費者に良い製品との出会いを提供する」を通し、ユーザーのビジネスが健全に発展する姿を目指しています。
（中略）
■社風・環境
- 人について
【創業者】
代表の杉浦は過去3度の上場を経験しており（東証マザーズ（旧）：2回／東証1部（旧）：1回）、マーケティングとスタートアップにおいて豊富な知見を有しています。
経歴
No.2の明石（杉浦よりも7歳年上）は、小売業界で商品統括トップとして全国展開・上場を経験した経験があります。大組織のマネジメントと管理に長けています。
経歴
【開発】
エンジニアトップの高橋は、大手Fintech企業出身。それまでに映像系、決済系、広告系の企業で経験を積んでいます。代表の杉浦とは2013年（当時大学生）に当時のインターン先を通じて知り合い、2017年に杉浦が声を掛けたことで再会。その後2018年にSquad（旧：SIVA）に参画。弊社のすべての開発を知る。
経歴
株式会社Speee
が出てくる。
