Category: event

Bitcoin Future ATH Prediction

📅 Bitcoin Future ATH Prediction (Cycle 4–5 Projection)

Let’s first recap the pattern from all previous 4 cycles:

CycleHalvingATH Lag (months)ATH DateATH Price% from Halving
1Nov 2012~12Nov 2013$1.1k+9,000 %
2Jul 2016~17Dec 2017$19.6k+2,400 %
3May 2020~18Nov 2021$69k+250 %
4Apr 2024~16 (so far)Aug 2025 (so far)$124k+80 %
5 (future)~Mar 2028 (estimated)????

🔮 Cycle Pattern Logic

  • Every halving reduces new BTC supply by 50 %.
  • Historically, the ATH occurs 12–18 months post-halving.
  • But cycles are lengthening slightly due to institutional liquidity, ETFs, and slower retail FOMO phases.
  • Therefore, the next peak window shifts gradually later each cycle:
    • 2013 → 12 mo lag
    • 2017 → 17 mo lag
    • 2021 → 18 mo lag
    • 2025 → 16 mo lag (so far; could extend to 20+)

📈 Predicted ATH Windows

ScenarioExpected ATH YearTime from HalvingReasoning
Base Case (historical average)Late 2025 → Early 202616–18 monthsMirrors 2016/2020 pattern; supply shock from 2024 halving peaks mid-2026
Extended Cycle (ETF & institutional adoption)Mid 2026 → Late 202720–28 monthsSlower but longer bull due to capital inflow pacing
Aggressive Case (compressed FOMO)Aug 2025 → Dec 202512–16 monthsContinuation of 2025’s parabolic run if liquidity surges fast
Next-Cycle ATH (after 2028 halving)Late 2029 → 203016–20 monthsFor Cycle 5; long-term 8-year super-cycle potential

💰 Price Range Forecast (Conservative to Bullish)

CyclePredicted ATH RangeBasis
2025–2026 Bull Peak$180 k – $250 kFollows 2×–3× growth from previous $69 k ATH (Cycle 3 → 4 pattern)
2027 Extended Peak$250 k – $350 kIf cycle elongates + institutional ETFs keep absorbing supply
2030 Next-Cycle ATH$500 k – $750 kAssuming post-2028 halving and sustained macro adoption

🧭 Summary

PhaseYear RangeCycle BehaviorExpected Trend
Accumulation2023 – Apr 2024Pre-halving consolidationNeutral to slightly bullish
Bull RunApr 2024 – 2026Post-halving expansion🚀 Major price appreciation
Peak + DistributionLate 2025 – 2026Parabolic top formationPotential $200 k–$250 k ATH
Bear Market2026 – 2027Cooling, 60–80 % drawdownReturn to ~$80 k–$100 k
Recovery → Next Halving2027 – 2028Slow rebuildPrepares for next run
Next ATH2029 – 2030Cycle 5 climaxPossible $500 k +

🔍 Final Answer

📅 Most probable next ATH:
Between Aug 2025 – Mar 2026
📈 Expected range: $180 k – $250 k USD

If the cycle extends (ETF/slow FOMO scenario), ATH could delay to 2027, but less likely beyond that.

👉 The lag is consistently 12–18 months after halving — even as returns compress.
Thus, the statistically strongest window for the next peak is Apr 2025 → Oct 2025 → Mar 2026.

📊 Bitcoin 4-Year Cycle Timeline & ATH Forecast (2012 → 2030)

🟩 Overview

This timeline illustrates Bitcoin’s 4-year halving cycles, highlighting the bull (green), bear (red), and accumulation (gray) phases, along with ATH (All-Time High) milestones and future projections.

⏱️ Timeline Summary

YearPhaseDescriptionHalvingATHNotes
2012⚙️ AccumulationBitcoin emerging market, price <$10✅ Nov 2012—Start of first halving cycle
2013🟩 Bull RunPrice rises from ~$13 → $1,163—Nov 2013+9,000% gain; first major mania
2014–2015🔴 Bear83% drawdown; Mt. Gox crash——Bottom near $150
2016⚙️ Accumulation → Bull StartRecovery begins✅ Jul 2016—Entry to second cycle
2017🟩 Bull RunPrice $1k → $19.6k—Dec 2017+2,400% gain
2018🔴 Bear84% drop——Crypto Winter
2019⚙️ AccumulationSideways 3k–10k——Pre-halving buildup
2020🟩 Bull Run StartCOVID bottom → strong rally✅ May 2020—Supply shock begins
2021🟩 Bull Run Peak$69k ATH—Nov 2021+250% cycle gain
2022🔴 BearFTX/LUNA collapse; bottom ~$15k——77% drawdown
2023⚙️ AccumulationRecovery 20k → 40k——ETF anticipation builds
2024🟩 Bull Run StartPost-halving rally begins✅ Apr 2024—Cycle 4 active
2025🟩 Bull Run PeakATH ~$124k—Aug 2025+80% from prev. ATH
2026🟥 Transition → BearCooling, distribution phase——Top formation year
2027🔴 BearRetest ~80–100k zone——60–70% correction expected
2028⚙️ AccumulationRebuild phase✅ Mar 2028 (est.)—Start of Cycle 5
2029–2030🟩 Bull RunMassive liquidity + adoption—2030 (est.)Predicted ATH $500k–750k

📈 Summary Statistics

MetricHistorical AvgFuture Expectation
Cycle Length4 years (≈48 months)May extend to 5 years (60 mo)
Time from Halving → ATH16–18 months18–24 months (extended cycle)
Bull Run Green Months~10–1210–14 (expected)
Bearish Red Months~9–109–12 (expected)
Avg Bull Gain20–60×2–4× from last ATH

🔮 Forecast Summary

ScenarioExpected ATH YearPrice RangeConfidence
Base Case (historical)Late 2025 – Early 2026$180k – $250k⭐⭐⭐⭐
Extended CycleMid 2026 – Late 2027$250k – $350k⭐⭐⭐
Next Halving Cycle (Cycle 5)Late 2029 – 2030$500k – $750k⭐⭐⭐⭐

🧭 Key Takeaways

  • Bitcoin’s 4-year rhythm remains intact: Halving → Bull → ATH → Bear → Rebuild.
  • The ATH window for this cycle (Cycle 4) is most likely Aug 2025 – Mar 2026, with possible extension into 2027.
  • Next halving in 2028 could start the next major leg, leading to $500k+ ATH by 2030.
  • Green candle density (monthly) is the best early indicator of ongoing bullish momentum.

(Data derived from BTC historical monthly closes, halving events, and cycle averages from 2012–2025.)

Concept of Matchmaking: Types, Where They’re Used, and Why They Matter

Matchmaking isn’t just about dating apps or game lobbies—it’s any system that pairs people (or teams) with other people, tasks, or opportunities. Below is a compact, practical guide to the major matchmaking types you’ll find around the world and how each is used.

1) Romance & Partnering

a) Traditional / Community Matchmaking

  • Where: South Asia (arranged marriage brokers), Middle East/North Africa (family networks), Jewish communities (shadchanim), Japan (omiai), China (xiangqin/“marriage markets”), parts of Africa (elders).
  • How it works: Human matchmakers or families vet compatibility (values, religion, education, family ties).
  • Use: Long-term compatibility, social cohesion, shared expectations.

b) Event-Based (Speed-Dating, Mixers, Matchmaking Parties)

  • Where: Global cities.
  • How: Structured short meetings with curated pools; sometimes role- or interest-based.
  • Use: Efficient discovery with light screening.

c) Algorithmic Dating Apps

  • Where: Global (Tinder, Bumble, Hinge, Muzz, Dil Mil, Shaadi, etc.).
  • How: Profiles + preferences + behavioral signals (swipes, messages) → recommendations.
  • Use: Scale and reach; quick filtering; flexible to lifestyle and culture.

d) Matchmaking Agencies (Concierge Services)

  • Where: Worldwide in major metros.
  • How: Human-led intake interviews, background checks, coaching.
  • Use: High-touch, privacy, premium curation.

2) Games & Esports

a) Random / Casual Queue

  • How: Fast fill by availability.
  • Use: Low friction, quick fun.

b) Skill-Based Matchmaking (SBMM)

  • How: Ratings (ELO, MMR, TrueSkill) balance teams by skill.
  • Use: Fairness, competitive integrity.

c) Role-Queued Matchmaking

  • How: Players pre-select roles (tank/healer/DPS; IGL/entry).
  • Use: Team synergy, reduced role conflict.

d) Party / Clan / Custom Lobby

  • How: Pre-made squads, private lobbies, scrims.
  • Use: Social play, practice, community building.

e) Tournament / Bracket Systems

  • How: Single/double elimination, Swiss, round-robin.
  • Use: Clear winners, league structure, esports ops.

f) Engagement-Optimized Matchmaking (EOMM)

  • How: Considers retention/“fun curves” (e.g., avoiding long loss streaks).
  • Use: Player retention; controversial vs. pure competitive fairness.

3) Business, B2B & Careers

a) Conference & Trade-Show Matchmaking

  • How: Apps match buyers–sellers by interests, budgets, categories.
  • Use: Efficient deal-making, booked 1:1s, exhibitor ROI.

b) Startup–Investor / Accelerator Matchmaking

  • How: Thesis fit, stage, geography, sector tags.
  • Use: Fundraising efficiency, curated pipelines.

c) Vendor Sourcing & Procurement

  • How: RFP platforms match needs to certified suppliers.
  • Use: Compliance, price discovery, diversification.

d) Job & Talent Platforms

  • How: Skills, experience, assessments; sometimes psychometrics.
  • Use: Better candidate–role fit, reduced time to hire.

e) Mentorship & Advisory

  • How: Goals, expertise, availability, cultural/language fit.
  • Use: Career development, knowledge transfer.

4) Education & Learning

a) Tutor–Student Matching

  • How: Subject, level, schedule, pedagogy style.
  • Use: Learning outcomes, retention.

b) Study Buddy / Project Team Matching

  • How: Skills complement, time zones, collaboration styles.
  • Use: Productivity, peer learning.

c) Internship & Apprenticeship Placement

  • How: Academic background, interests, host org criteria.
  • Use: Work readiness, pipeline building.

5) Health, Wellbeing & Care

a) Therapist / Coach Matching

  • How: Modality (CBT, EMDR), language, specialization, availability.
  • Use: Therapeutic alliance, adherence, outcomes.

b) Patient–Provider Matching

  • How: Insurance, location, specialty, cultural/linguistic fit.
  • Use: Access, satisfaction, health equity.

c) Elder Care & Disability Support

  • How: Needs assessment vs. caregiver skills and reliability.
  • Use: Safety, quality of life.

6) Civic, Cultural & Social Impact

a) Volunteering & NGO Projects

  • How: Skills, cause areas, time commitment.
  • Use: Impact per volunteer hour, organizer efficiency.

b) Language Exchange & Cultural Pairing

  • How: Native-target language pair, availability, goals.
  • Use: Fluency, intercultural competence.

c) Housing & Roommate Matching

  • How: Budget, location, lifestyle norms.
  • Use: Reduced conflict, tenant retention.

7) Platforms & Marketplaces (General Patterns)

a) Algorithmic (Data-Driven)

  • Inputs: Preferences, constraints, performance/behavioral data.
  • Pros: Scale, personalization, measurable KPIs.
  • Cons: Bias, opacity; requires data governance.

b) Human-Led (Expert/Concierge)

  • Inputs: Interviews, references, judgment, networks.
  • Pros: Nuance, trust, context sensitivity.
  • Cons: Costly, less scalable, variable consistency.

c) Hybrid (Human + Algorithm)

  • How: AI narrows; humans curate and override.
  • Use: Best of both: efficiency + judgment.

Cultural Notes & Regional Nuance

  • South Asia & Middle East: Family and faith-aligned matchmaking remains influential alongside modern apps.
  • East Asia: Formalized processes (omiai, xiangqin) coexist with dating apps; work culture/time constraints shape needs.
  • Europe & North America: App ecosystem is dominant; niche agencies thrive for premium privacy and values-based pairing.
  • Africa & Latin America: Community and church networks play strong roles; mobile-first platforms are accelerating reach.

Key Design Considerations (if you’re building a matcher)

  1. Objective clarity: Is your goal fairness, retention, conversion, or long-term success?
  2. Signals & constraints: What hard constraints (location, availability) vs. soft preferences (style, culture) matter?
  3. Quality metrics:
    • Dating: second-date rate, conversation depth, safety reports.
    • Gaming: queue time, match fairness (win prob ~50%), churn.
    • Business: meeting acceptance, follow-ups, deal value.
    • Health: adherence, satisfaction, outcomes.
  4. Feedback loops: Collect outcomes (NPS, wins/losses, session length, “was this helpful?”) to retrain models.
  5. Transparency & control: Let users set preferences and opt out of engagement-shaping mechanics if feasible.
  6. Fairness & bias: Audit for demographic skews, ranking bias, and disparate impact.
  7. Safety & trust: Verification, moderation, fraud prevention, clear appeals/override paths.
  8. Privacy: Minimize data, encrypt sensitive attributes, explain use clearly.

Quick Glossary

  • SBMM (Skill-Based Matchmaking): Matches by ability level.
  • EOMM (Engagement-Optimized Matchmaking): Tunes difficulty/opponents to keep users playing.
  • MMR/ELO/TrueSkill: Numerical ratings for competitive balance.
  • Cold-start: When a new user lacks data; use questionnaires or starter matches.
  • Constraints vs. objectives: “Must-have” rules vs. what the algorithm optimizes.

TL;DR

  • Matchmaking spans romance, games, business, education, health, civic life, and housing.
  • It can be algorithmic, human-led, or hybrid, tuned for fairness, speed, engagement, or outcomes.
  • Success depends on clear goals, robust signals, ethical safeguards, and feedback loops.

美秀集團 Amazing Show－心悶 (Sim-Būn) Sin Man TaiLo Mandarin Pinyin Lyric

等袂著思思念念最後一張批
Tán bē tio̍h sī-sī liām-liām tsuì-âu tsi̍t tiunn phue
Děng bù dào sī sī niàn niàn zuìhòu yī zhāng piān
Couldn’t wait for the last letter I longed for,

講好的山盟海誓毋知擱賰偌濟
Kóng hó ê suann-bông hái-sè m̄ tsai koh sǹg guā-tsē
Shuō hǎo de shān méng hǎi shì bù zhī gè shèng duō shǎo
The vows of love we swore—who knows how much still remain?

浮浮沉沉茫茫渺渺 夢想咧坱砂
Phû-phû tîm-tîm bông-bông biáu-biáu bōng-siúnn leh ióng-soa
Fú fú chén chén máng máng miǎo miǎo mèng xiǎng liè yǎng shā
Floating, sinking—dreams scatter like drifting sand,

係儼硬抑係拖磨
Sī giām-ngēng ia̍h sī thua-môa
Shì jiān yìng hái shì tuō mó
Is it stubborn strength or just delay?

凡勢成全體諒
Hoân-sè sîng-tsuân thé-liōng
Fán shì chéng quán tǐ liàng
Compromises and understanding,

一直藏在心中
It-ti̍t chhông tsāi sim-tiong
Yī zhí cáng zài xīn zhōng
Always hidden deep inside the heart.

當初離開的彼一工
Tang-tshoo lī-khui ê hit tsi̍t kang
Dāng chū líkāi de nà yī tiān
That day you left,

講袂出心酸 毋甘
Kóng bē tshut sim-sng, m̄ kam
Shuō bù chū xīn suān, bù gān
I couldn’t speak the sorrow, only unwillingness.

有誰來作伴
Ū se lâi tsò-puānn
Yǒu shéi lái zuò bàn
Who will be my companion?

窗外掖落來的月色
Thang-guā ia̍h-loh-lâi ê gue̍h-sik
Chuāng wài xià luò lái de yuè sè
The moonlight falling outside the window

毋但照映著我一個人
M̄-nā tio-ìng tio̍h guá tsi̍t ê lâng
Bù jǐn zhào yìng zhe wǒ yī gè rén
Shines not only on me alone.

希望會當有彼一工
Hi-bāng ē-tàng ū hit tsi̍t kang
Xī wàng néng yǒu nà yī tiān
I hope one day may come,

給願望遺憾 寫作
Hō guān-bōng ûi-hām siá tsok
Ràng yuàn wàng yí hàn xiě chéng
That regret and wishes can be rewritten

上媠的靠岸
Siōng suí ê khò-òaⁿ
Zuì měi de kào àn
Into the most beautiful harbor—

心悶你溫柔的聲
Sim-mn̄g lí un-jiû ê siann
Xīn mèn nǐ wēn róu de shēng
Where I hear your gentle voice in my heart.

行袂煞的路 係欲造 甲偌崎嶇
Kiân bē suah ê lōo sī beh tsō kah guā kî-khu
Xíng bù wán de lù shì yào zǒu duō qí qū
The road never-ending is steep and rough,

佇人生流浪 欣羨 係誰的虛華
Tī jîn-seng liû-lōng him-siān sī siâ ê hi-hua
Zài rén shēng liú làng xīn xiàn shì shéi de xū huá
Wandering through life, envying others’ false splendor.

啥物攏欲捎 天公伯咧看
Sann-mih lóng beh siau, Thian-kong-peh leh khuànn
Shén me dōu xiǎng jiāo, tiān gōng bó zài kàn
Everything we send—Heaven is watching.

我係 無才調去管待你自由自在自卑的死活
Guá sī bô tsâi-tiāu khì kuán-thāi lí tsū-iû tsū-tsāi tsū-pi ê sí-ua̍h
Wǒ shì méi cái liào qù guǎn dài nǐ zì yóu zì zài zì bēi de shēng huó
I lack the power to control your free, fragile life.

齷齪 騙家己擱有希望
O̍k-chhok phiàn ka-kī koh ū hi-bāng
Wò chuò piàn zì jǐ hái yǒu xī wàng
Foolishly I lie to myself there’s still hope,

看破 才係 事實的心聲
Khuànn-phuà tsiah sī si̍t-si̍t ê sim-siann
Kàn pò cái shì shí shí de xīn shēng
But seeing through it is the truest voice of the heart.

你的話 我咧講 你的戲 我咧搬
Lí ê uē guá leh kóng, lí ê hì guá leh puann
Nǐ de huà wǒ zài jiǎng, nǐ de xì wǒ zài yǎn
Your words, I repeat; your play, I perform,

哭 悲戀詩歌 無人願意聽
Khàu pe-liān si-koa bô-lâng uī-i thiaⁿ
Kū bēi liàn shī gē méi rén yuàn yì tīng
Crying songs of sorrow no one wants to hear.

愛著不該愛的人 牽起不該牽的手
Ài tio̍h m̄-kai ài ê lâng, khan khí m̄-kai khan ê tshiu
Ài zhe bù gāi ài de rén, qiān qǐ bù gāi qiān de shǒu
Loving the one I shouldn’t, holding the hand I shouldn’t.

衝碰浪子踏線以前背後總有人咧搝
Chhiong-phōng lòng-tsú tha̍h-suànn í-tsîng pē-āu tsióng ū lâng leh giú
Chōng pèng làng zǐ tà xiàn yǐ qián bèi hòu zǒng yǒu rén zài yāo
A reckless wanderer crossing the line, always pulled back by someone.

寄袂出的情批 規疊藏佇心底
Tsì bē tshut ê tsîng-phue kui ta̍p chhông tī sim-té
Jì bù chū de qíng piān guī dié cáng zài xīn dǐ
Unsent love letters stacked and hidden deep in the heart.

講好 咱只係無夠緣的朋友
Kóng hó lán tsiah sī bô-kàu iân ê pîng-iú
Shuō hǎo wǒmen zhǐ shì méi yǒu yuán fèn de péng yǒu
We said we were just friends without enough fate.

我哭我的你走你的 無人需要會失禮
Guá khàu guá ê, lí tsáu lí ê, bô-lâng su-iàu ē sit-lē
Wǒ kū wǒ de, nǐ zǒu nǐ de, méi rén xū yào gǎn dào shī lǐ
I cry my way, you walk your way—no need to apologize.

我用我的一切 懷念你的美麗
Guá iōng guá ê it-tshè, huâi-liām lí ê bí-lē
Wǒ yòng wǒ de yī qiè huái niàn nǐ de měi lì
I give all of myself remembering your beauty.

感情無法勤儉 後果只好承擔
Kám-tsiûnn bô-hoat khîn-kiám, āu-kò tsí-hó sing-tann
Gǎn qíng bù néng qín jiǎn, hòu guǒ zhǐ hǎo chéng dān
Feelings cannot be saved, so I must bear the result.

聽家己一句 歹勢
Thiaⁿ ka-kī tsi̍t-kù phái-sè
Tīng zì jǐ yī jù pái shè
Listening to myself whisper just one word: “Sorry.”

馬克SAVAGE.M【無路用的人】(Feat.滅火器＆阿跨面) Lyric Tai-Lo Pinyin English

妳離開那一天 我變成無路用欸人
Lí lī-khui hit tsi̍t-tn̄g, guá piàn-tsîng bô lōo-iōng ê lâng
The day you left, I became a useless person.

在沒妳的世界 做咱沒路用欸夢
Tsāi bô lí ê sè-kài, tsò lán bô lōo-iōng ê bāng
In a world without you, we make dreams that go nowhere.

妳離開那一天 我變作尚自由欸人
Lí lī-khui hit tsi̍t-tn̄g, guá piàn-tsò siōng tsū-iû ê lâng
The day you left, I turned into someone “freer.”

在沒妳欸世界 做咱沒做完欸夢
Tsāi bô lí ê sè-kài, tsò lán bô tsò-uân ê bāng
In a world without you, we chase dreams we never finish.

在這個城市無妳 我假作無意無意
Tsāi tsit ê siânn-chhī bô lí, guá ké-tsò bô-ì bô-ì
In this city without you, I pretend not to care.

生活無瞑無日 整天無吃啊無喝
Sing-ua̍h bô miâ bô ji̍t, tsíng-tn̄g bô tsia̍h a bô lim
Life has no night or day—whole days without eating or drinking.

阮去過什麼地方 吃過哪一間店
Guán khì-kuè sím-mi̍h tōng-bîng, tsia̍h-kuè tó tsi̍t kiann tiàm
Where did we go, which places did we eat at?

忙到三更半暝擱來面對我的軟弱
Bâng-kàu sam-king puàn-miâ koh lâi bīn-tuì guá ê núa-jiok
Busy till past midnight, then I face my weakness.

自妳離開之後 什麼都做不好
Tsū lí lī-khui tsí-āu, sím-mi̍h lóng tsò bô hó
Since you left, I can’t do anything right.

一個無路用的人 連戒菸都做不到
Tsi̍t ê bô lōo-iōng ê lâng, liân kài-ian to tsò bô-kàu
A useless man—can’t even quit smoking.

諾是我擱愛妳怎會做不到
Nā sī guá koh ài lí, tsánn ē tsò bô-kàu?
If I still love you, how can I not do it?

把車停好 我卻沒落車
Pah tshia thîng hó, guá khioh bô lo̍h-tshia
I park the car, but don’t get out.

菸也沒點 惦惦看 手機仔
Ian iā bô tiám, tiàm-tiàm khuànn siú-ki-á
Don’t even light a cigarette—just quietly stare at my phone.

外面沒落雨 思念像落雨聲
Guā-bīn bô lo̍h-hōo, si-liām tshiūnn lo̍h-hōo-siann
It isn’t raining outside, but missing you sounds like rain.

雨水還未乾 心內想著妳的影
Hōo-tsuí â-bē kuann, sim-lāi siūnn-tio̍h lí ê iánn
The rain hasn’t dried; in my heart I still see your shadow.

經過幾落冬 我猶原站底加
King-kuè kúi lo̍h-tang, guá iû-goân khiām té-ke
Many winters later, I’m still standing here at home.

你可以笑我憨 我是惜情欸人
Lí ē-kái tshiò guá gong, guá sī sioh-tsiânn ê lâng
You can call me a fool; I’m just sentimental.

不時夢到妳尬咱養欸貓阿子
Put-sî bāng-kàu lí kah lán iūnn ê niau-á
I often dream of you with the cat we raised.

天若光夢若醒 剩我一個人
Thian nā kng, bāng nā tshíng, tsiânn guá tsi̍t ê lâng
When day breaks and the dream ends, I’m the only one left.

妳離開那一天 我變成無路用欸人
Lí lī-khui hit tsi̍t-tn̄g, guá piàn-tsîng bô lōo-iōng ê lâng
The day you left, I became a useless man.

在沒妳的世界 做咱沒路用欸夢
Tsāi bô lí ê sè-kài, tsò lán bô lōo-iōng ê bāng
In a world without you, our dreams have no way forward.

妳離開那一天 我變作尚自由欸人
Lí lī-khui hit tsi̍t-tn̄g, guá piàn-tsò siōng tsū-iû ê lâng
The day you left, I became “so free.”

在沒妳欸世界 做咱沒做完欸夢 剩我一個人
Tsāi bô lí ê sè-kài, tsò lán bô tsò-uân ê bāng, tsiânn guá tsi̍t ê lâng
In your absence, our dreams stay unfinished—leaving me alone.

像妳這款美麗的姑娘是配不起我
Tshiūnn lí tsit-khuán bí-lē ê koo-niû, guá phuè-put-khí lí
A beauty like you—I don’t deserve you.

溫柔欸錯誤決定著我的死活
Un-jiû ê tshò-gōo kuat-tīng tio̍h guá ê sí-ua̍h
Gentle mistakes ended up deciding my fate.

等待冷天若到 飛走的燕子
Tán-thāi líng-thinn nā kàu, hui-tsáu ê ìn-tsú
Waiting for winter to come, like swallows that fly away.

這傷悲若過了請妳愛放不記我
Tsit siong-pi nā kuè-liáu, tshiánn lí ài pàng m̄-kì guá
When this sorrow passes, please forget me.

妳是風這麼快 追不過你
Lí sī hong tsiah-mā khuinn, tui bō kuè lí
You’re like the wind—so fast I can’t catch up.

心內受傷 流過看無的血水
Sim-lāi siū-siong, lâu-kuè khuànn-bô ê hueh-tsuí
My heart is wounded, bleeding tears no one can see.

說要陪妳一世人 嘛無影無價
Sioh beh pûi lí tsi̍t-sì-lâng, mā bô-iánn bô-kè
I said I’d be with you for life—empty words, worth nothing.

是我沒路用 才選擇故意逃避
Sī guá bô lōo-iōng, tsiah suán-ti̍k kòo-ì tâu-pī
It’s because I’m no good that I chose to run away.

我愛你 家徹底
Guá ài lí, kah thiat-té
I love you—completely.

你離開 家漂丿
Lí lī-khui, kah piâu-piah
Since you left, I’ve drifted aimlessly.

春風來 寒風吹
Tshun-hong lâi, hân-hong tshue
Spring winds come, cold winds blow.

天註定 咱無緣
Thian tsù-tīng, lán bô-iân
Heaven decided we have no fate together.

在花花欸世界
Tsāi hue-hue ê sè-kài
In this flowery, flashy world,

沒你的美麗我要怎麼去體會
Bô lí ê bí-lē, guá beh tsánn-ná khì thé-huī
Without your beauty, how am I supposed to feel anything?

離開那一天
Lī-khui hit tsi̍t-tn̄g
That day you left—

無你的世界
Bô lí ê sè-kài
A world without you—

是我沒路用希望你沒來過
Sī guá bô lōo-iōng, hi-bāng lí bô lâi-kuè
It’s on me; I just hope you never had to come into it.

妳離開那一天 我變成無路用欸人
Lí lī-khui hit tsi̍t-tn̄g, guá piàn-tsîng bô lōo-iōng ê lâng
The day you left, I became a useless man.

在沒妳的世界 做咱沒路用欸夢
Tsāi bô lí ê sè-kài, tsò lán bô lōo-iōng ê bāng
In a world without you, our dreams go nowhere.

妳離開那一天 我變作尚自由欸人
Lí lī-khui hit tsi̍t-tn̄g, guá piàn-tsò siōng tsū-iû ê lâng
The day you left, I became “so free.”

在沒妳欸世界 做咱沒做完欸夢 剩我一個人
Tsāi bô lí ê sè-kài, tsò lán bô tsò-uân ê bāng, tsiânn guá tsi̍t ê lâng
In your absence, our dream stays unfinished—and I’m left alone.

Karma Code

If you sow melons, you will reap melons, if you sow beans, you will reap beans.
種瓜得瓜 種豆得豆
Zhǒng guā dé guā zhǒng dòu dé dòu

Isn’t this cause and effect?
這不是因果嗎?
zhè bùshì yīnguǒ ma?

Cause and effect is science
因果是科學
Yīnguǒ shì kēxué

It definitely exists
絕對是存在的
juéduì shì cúnzài de

(Yep, what up, Jack?)
(Yep, what up, Jack?)
(Yep, what up, Jack?)

Lanlao
攬佬
Lǎn lǎo

God bless you, there are mountains of gold and silver on the road ahead
老天保佑金山銀山前路有
Lǎo tiān bǎoyòu jīnshān yínshān qián lù yǒu

God instructs you to ignore the fight between dragons and tigers.
老天教唆別管江湖龍虎鬥
lǎo tiān jiàosuō bié guǎn jiānghú lónghǔ dòu

God told me to never lose my kindness in this life
老天叮囑這輩子我善不丟
lǎo tiān dīngzhǔ zhè bèizǐ wǒ shàn bù diū

God is afraid that Thirty-Six Thunder will always hit autumn (yeah, ayy)
老天就怕三十六雷總打秋 (yeah, ayy)
lǎo tiān jiù pà sānshíliù léi zǒng dǎ qiū (yeah, ayy)

Monsters always appear after the autumn harvest (settling accounts)
魑魅魍魎總出現在秋收後 (算賬)
Chīmèiwǎngliǎng zǒng chū xiànzài qiūshōu hòu (suànzhàng)

Monsters always appear after the autumn harvest (destroyed)
魑魅魍魎總出現在秋收後 (消滅)
chīmèiwǎngliǎng zǒng chū xiànzài qiūshōu hòu (xiāomiè)

God allows me to eradicate rodents with one stick (number of days)
老天讓我一棍剷除齧齒類 (天數)
lǎo tiān ràng wǒ yī gùn chǎnchú nièchǐ lèi (tiānshù)

The second stick is enchanted and I penetrate the opponent’s building (huh)
第二棍附魔我打穿對家樓 (huh)
dì èr gùn fùmó wǒ dǎ chuān duì jiā lóu (huh)

cause and effect
因果
Yīnguǒ

cause and effect
因果
yīnguǒ

(God asked me to put down the butcher knife but this is his ayy)
(老天讓放下屠刀但這是他的 ayy)
(lǎo tiān ràng fàngxià túdāo dàn zhè shì tā de ayy)

cause and effect
因果
yīnguǒ

cause and effect
因果
yīnguǒ

(Should I put down my butcher knife? Ayy)
(該不該放下屠刀? Ayy)
(gāi bù gāi fàngxià túdāo? Ayy)

if put
如果放
Rúguǒ fàng

Hungry ghosts lurk in the corner and can be beaten every minute (beat)
角落潛伏餓鬼分分鐘一棒 (打)
jiǎoluò qiánfú èguǐ fēn fēnzhōng yī bàng (dǎ)

If mentioned
如果提
rúguǒ tí

If the evil thoughts in your heart outweigh the good ones, there is no way to calm them down
心中惡念超過善的話沒辦法平
xīnzhōng èniàn chāoguò shàn dehuà méi bànfǎ píng

To pay off debt
要還債
Yào huánzhài

The soldiers come to stop me because I am a Nephalem
兵來將擋因為我是個奈非天
bīngláijiāngdǎng yīnwèi wǒ shìgè nài fēi tiān

on wrist
手腕上
shǒuwàn shàng

The five-way God of Wealth hides in the void and blocks everything (ha)
五路財神虛空藏 擋一切 (ha)
wǔ lù cáishén xūkōng cáng dǎng yīqiè (ha)

Big waves on the sea
海面刮大浪
Hǎimiàn guā dàlàng

Do you want to settle accounts later?
要不要秋後算賬?
yào bùyào qiūhòusuànzhàng?

I’m not God (ayy)
我不是God (ayy)
Wǒ bùshì God (ayy)

I need to balance yin and yang
我需要平衡陰陽
wǒ xūyào pínghéng yīnyáng

Understand this cause and effect karma (Understand this cause and effect karma) (Lan Lao)
看懂這因果業障 (看懂這因果業障) (攬佬)
kàn dǒng zhè yīnguǒ yèzhàng (kàn dǒng zhè yīnguǒ yèzhàng) (lǎn lǎo)

There are rules in the world
世界上條條框框
Shìjiè shàng tiáotiáokuàngkuàng

It’s like playing cards and keeping accounts (right)
就好像打牌記賬 (對)
jiù hǎoxiàng dǎpái jì zhàng (duì)

Sooner or later it will happen to you
遲早會到你頭上
chízǎo huì dào nǐ tóushàng

Who doesn’t want to make a comeback (Who doesn’t want to make a comeback ayy)
誰不想打翻身仗 (誰不想打翻身仗 ayy)
shuí bùxiǎng dǎ fānshēn zhàng (shuí bùxiǎng dǎ fānshēn zhàng ayy)

There are always people selling miserably
總有人在賣著慘
Zǒng yǒurén zài màizhe cǎn

(There are always people who want to take advantage of goodness)
(總有人想利用善)
(zǒng yǒurén xiǎng lìyòng shàn)

Little do you know, God is watching
殊不知 天在看
shūbùzhī tiān zài kàn

The trouble is coming (miserable ha)
禍根找上門 (慘 ha)
huògēn zhǎo shàngmén (cǎn ha)

You are making fun of love
你把愛拿來鬧著玩
Nǐ bǎ ài ná lái nàozhe wán

You treat love like a bullet
你把愛當做子彈
nǐ bǎ ài dàngzuò zǐdàn

Little do you know, God is watching
殊不知 天在看
shūbùzhī tiān zài kàn

Clap your hands, it’s over (Lan Lao)
拍拍手 完蛋 (攬佬)
pāi pāishǒu wándàn (lǎn lǎo)

God bless you, there are mountains of gold and silver on the road ahead
老天保佑金山銀山前路有
Lǎo tiān bǎoyòu jīnshān yínshān qián lù yǒu

God instructs you to ignore the fight between dragons and tigers.
老天教唆別管江湖龍虎鬥
lǎo tiān jiàosuō bié guǎn jiānghú lónghǔ dòu

God told me to never lose my kindness in this life
老天叮囑這輩子我善不丟
lǎo tiān dīngzhǔ zhè bèizǐ wǒ shàn bù diū

God is afraid that Thirty-Six Thunder will always hit autumn (yeah, ayy)
老天就怕三十六雷總打秋 (yeah, ayy)
lǎo tiān jiù pà sānshíliù léi zǒng dǎ qiū (yeah, ayy)

Monsters always appear after the autumn harvest (settling accounts)
魑魅魍魎總出現在秋收後 (算賬)
Chīmèiwǎngliǎng zǒng chū xiànzài qiūshōu hòu (suànzhàng)

Monsters always appear after the autumn harvest (destroyed)
魑魅魍魎總出現在秋收後 (消滅)
chīmèiwǎngliǎng zǒng chū xiànzài qiūshōu hòu (xiāomiè)

God allows me to eradicate rodents with one stick (number of days)
老天讓我一棍剷除齧齒類 (天數)
lǎo tiān ràng wǒ yī gùn chǎnchú nièchǐ lèi (tiānshù)

The second stick is enchanted and I penetrate the opponent’s building (huh)
第二棍附魔我打穿對家樓 (huh)
dì èr gùn fùmó wǒ dǎ chuān duì jiā lóu (huh)

cause and effect
因果
Yīnguǒ

cause and effect
因果
yīnguǒ

(God asked me to put down the butcher knife but this is his ayy)
(老天讓放下屠刀但這是他的 ayy)
(lǎo tiān ràng fàngxià túdāo dàn zhè shì tā de ayy)

cause and effect
因果
yīnguǒ

cause and effect
因果
yīnguǒ

(Forget it, I’ll give you some face)
(算了 給你個面子)
(suànle gěi nǐ gè miànzǐ)

Difference of Red Ocean VS Blue Ocean Strategy

🌊 Red Ocean Strategy

Compete in an existing market space.

✅ Characteristics:

  • Industry is well-defined and crowded
  • Competition is fierce (a “bloody ocean” 🩸)
  • Companies fight for market share
  • Success = outperforming rivals
  • Focus on existing demand
  • Differentiation OR low cost (rarely both)

🔍 Example:

Fast food chains like McDonald’s vs Burger King. They sell similar products, compete on price, and location — all in the same market space.

🌊 Blue Ocean Strategy

Create a new, uncontested market space.

✅ Characteristics:

  • Focus on innovation & value creation
  • Make the competition irrelevant
  • Capture new demand
  • Break the value-cost trade-off
  • Create AND capture new market space

🔍 Example:

Cirque du Soleil redefined the circus experience by blending circus with theater — no animals, higher prices, artistic value — creating a new market.

FeatureRed OceanBlue Ocean
Market SpaceExistingNew / Uncontested
CompetitionBeat the competitionMake competition irrelevant
DemandExploit existing demandCreate new demand
FocusEither cost OR differentiationCost AND differentiation (value innovation)
Strategy GoalGain bigger slice of marketCreate a new market
Risk LevelCompetitive pressureInnovation risk, but high reward

The chart has boats in the colored sea. The Blue Ocean Strategy concept presentation is a vector infographic element of marketing. The red has bloody mass competition and the blue is a niche market.The chart has boats in the colored sea. The Blue Ocean Strategy concept presentation is a vector infographic element of marketing. The red has bloody mass competition and the blue is a niche market.

150 Years Periods When To Make Money

It sounds like you’re asking if a 150-year financial sheet period is real—meaning whether money, wealth, or financial records over that long of a timeframe hold real value.

Breaking It Down:

  1. 150-Year Financial Records Exist
    • Many banks, companies, and governments keep records spanning over a century (e.g., The Bank of England, New York Stock Exchange data).
    • Some companies (e.g., Coca-Cola, JP Morgan) have 150+ years of financial reports.
  2. Does Money Stay “Real” Over 150 Years?
    • Money itself changes (gold-backed currencies, fiat money, digital money).
    • Inflation reduces the value of old money (e.g., $1 in 1875 is worth much more than $1 today).
    • But assets like land, stocks, gold, and businesses retain value over time.
  3. Investment & Wealth Over 150 Years
    • If someone invested $1,000 in the stock market 150 years ago (1875), today it would be worth millions.
    • The long-term wealth-building strategy (stocks, real estate, gold) proves money grows over time.

Here’s a historical & predictive table from 1945 to 2025, highlighting major investment trends and whether the market was bullish (📈) or bearish (📉):

YearEconomic/Market ConditionBest Investment AreasBullish/Bearish
1945Post-WWII BoomManufacturing, Infrastructure📈 Bullish
1950Economic ExpansionIndustrial Stocks, Real Estate📈 Bullish
1955US Growth & Tech StartBlue-chip stocks, Early Tech📈 Bullish
1960Mild RecessionGold, Consumer Goods📉 Bearish
1965Vietnam War SpendingDefense Stocks, Commodities📈 Bullish
1970Stagflation BeginsGold, Energy (Oil Crisis)📉 Bearish
1975Oil Crisis RecoveryEnergy, Real Estate📈 Bullish
1980High Inflation & VolckerGold, Bonds, Tech Startups📉 Bearish
1985Tech & Global GrowthMicrosoft, IBM, Real Estate📈 Bullish
1990Early 90s RecessionBonds, Large-Cap Stocks📉 Bearish
1995Dot-com BoomTech Stocks, Internet Startups📈 Bullish
2000Dot-com CrashGold, Bonds, Real Estate📉 Bearish
2005Housing Bubble PeaksReal Estate, Commodities📈 Bullish
2010Post-2008 RecoveryTech, Crypto, Growth Stocks📈 Bullish
2015Tech DominanceFAANG Stocks, AI, Startups📈 Bullish
2020COVID Crash & RecoveryCrypto, EVs, AI, Green Energy📉📈 Bearish → Bullish
2025*Future AI & Green TechAI, Robotics, Clean Energy, BTC📈 Bullish (Predicted)

Key Observations & 2025 Predictions:

  • Bullish Eras → Post-war (1945), Dot-com (1995), Tech Rise (2010+), AI boom (2025?)
  • Bearish Periods → Stagflation (1970s), Dot-com burst (2000), 2008 Crash, COVID (2020)
  • 2025 Investment Prediction → AI, robotics, blockchain, and green energy will dominate.

Investing in Artificial Intelligence (AI) and Green Technology sectors offers exposure to industries poised for significant growth. Below is a curated list of notable U.S. companies in these domains:

Artificial Intelligence (AI) Stocks

Company NameTicker SymbolDescription
Nvidia CorporationNVDALeading designer of GPUs, essential for AI computing and machine learning applications.
Microsoft CorporationMSFTIntegrates AI across its products and services, including cloud computing and software solutions.
Alphabet Inc.GOOGLParent company of Google, heavily investing in AI research and development.
Upstart Holdings Inc.UPSTUtilizes AI to assess credit risk, offering alternative lending solutions.
SoundHound AI Inc.SOUNSpecializes in voice AI and conversational intelligence technologies.

Green Technology Stocks

Company NameTicker SymbolDescription
NextEra EnergyNEEMajor renewable energy company focusing on wind and solar power generation.
Brookfield Renewable PartnersBEPOperates renewable power assets, including hydroelectric and wind facilities.
Clearway Energy Inc.CWENEngages in renewable energy projects, particularly wind and solar.
First SolarFSLRManufactures solar panels and develops utility-scale photovoltaic power plants.
Tesla Inc.TSLAProduces electric vehicles and invests in solar energy solutions.

Note: Investing in these sectors carries inherent risks due to market volatility and technological advancements. It’s advisable to conduct thorough research or consult a financial advisor before making investment decisions.

Conclusion: Can a 150-Year Sheet Predict the Future?

✅ Yes, it shows long-term patterns in money, markets, inflation, and industries.
❌ Not 100% accurate because new events (wars, crises, tech shifts) change the game.

But history repeats itself in cycles—so studying the past helps you predict and prepare for the future.

Chinese Dynasties Ranking System For An Emperor’s Wives

The ranking system for an emperor’s wives and consorts was detailed and varied across different Chinese dynasties. Here’s a more comprehensive breakdown, including variations from different dynasties like the Tang (618–907), Song (960–1279), Ming (1368–1644), and Qing (1644–1912) Dynasties.

Factors influencing rank:

  • Family background: Women from prominent families often received higher ranks. 
  • Emperor’s favor: A concubine who gained the emperor’s favor could be elevated in rank. 
  • Ability to bear children: Concubines who gave birth to the emperor’s heirs often received higher status. 

1. Empress (皇后, Huánghòu) – First Rank

  • The highest-ranking wife of the emperor, his main consort.
  • She had authority over all the emperor’s consorts and the harem.
  • Wore the Phoenix Crown (凤冠, Fèngguān) as a symbol of power.
  • The Empress gave birth to the heir (Crown Prince) whenever possible.

2. Imperial Noble Consort (皇贵妃, Huáng Guìfēi) – Second Rank

  • Just below the empress but still held significant influence.
  • This rank was not always present in earlier dynasties but became common in the Ming and Qing Dynasties.

3. Noble Consort (贵妃, Guìfēi) – Third Rank

  • Higher than most other consorts but below the Imperial Noble Consort.
  • Usually, one or two women held this title.

4. Consort (妃, Fēi) – Fourth Rank

  • A mid-tier consort with noble status.
  • In the Qing Dynasty, there were typically four Consorts at a time.

5. Imperial Concubine (嫔, Pín) – Fifth Rank

  • A recognized consort but of lower status.
  • Had limited influence in court matters.

6. Noble Lady (贵人, Guìrén) – Sixth Rank

  • The lowest official rank in the imperial harem.
  • Some noble ladies could rise in rank if favored by the emperor.

7. First Attendant (常在, Chángzài) – Seventh Rank

  • Lower than a Noble Lady but still part of the emperor’s harem.
  • Had minimal influence but could be promoted.

8. Second Attendant (答应, Dāyìng) – Eighth Rank

  • The lowest recognized rank.
  • Mostly served the emperor without real power.

9. Palace Maids (宫女, Gōngnǚ) – Unranked

  • Not official consorts but worked in the inner palace.
  • Could sometimes be promoted if favored by the emperor.

Other Titles (Special Cases & Retired Consorts)

  • Empress Dowager (皇太后, Huáng Tàihòu) – The widow of a deceased emperor, often the mother of the new emperor.
  • Grand Empress Dowager (太皇太后, Tài Huáng Tàihòu) – The grandmother of the reigning emperor.
  • Virtuous Consorts (昭仪, Zhāoyí; 修仪, Xiūyí, etc.) – Titles used in some dynasties like the Tang, referring to special-ranked concubines with unique responsibilities.

Each dynasty had slight variations, but this structure provides a detailed view of the emperor’s wives’ ranks throughout Chinese history. Would you like information on a specific dynasty or any famous empresses? 😊

Mersenne Prime and Mersenne Twister Explaination

Mersenne Prime and Mersenne Twister are two distinct mathematical/computational concepts that share part of their name but serve completely different purposes. Here’s a breakdown of the differences:

1. Mersenne Prime

  • Definition: A Mersenne prime is a special type of prime number that can be expressed in the form:

Mn​=2n−1

  • where nnn is a positive integer and MnM_nMn​ is prime.
  • Example: For n=2n = 2n=2, M2=22−1=3M_2 = 2^2 – 1 = 3M2​=22−1=3 (which is prime). For n=3n = 3n=3, M3=23−1=7M_3 = 2^3 – 1 = 7M3​=23−1=7 (which is also prime). However, not all numbers of the form 2n−12^n – 12n−1 are prime (e.g., 24−1=152^4 – 1 = 1524−1=15, which is not prime).
  • Applications:
    • Mersenne primes are closely related to perfect numbers, which are integers equal to the sum of their proper divisors.
    • They are of interest in number theory and are used in cryptographic algorithms.

In summary, while Mersenne primes are a special class of numbers studied in mathematics, the Mersenne Twister is a practical tool for random number generation in computing, inspired by the mathematical properties of Mersenne primes.

Why Are Mersenne Primes Special?

  • Rare: Not all numbers of the form 2n−12^n – 12n−1 are prime; nnn must itself be prime for 2n−12^n – 12n−1 to have a chance of being prime, but even then, not all such numbers are prime.
  • Connection to Perfect Numbers: Every Mersenne prime is associated with an even perfect number (a number equal to the sum of its proper divisors).

2. Mersenne Twister

  • Definition: The Mersenne Twister is a pseudorandom number generator (PRNG) developed by Makoto Matsumoto and Takuji Nishimura in 1997 and designed for generating sequences of random numbers with a very long period. It is named “Mersenne” because its period length is a Mersenne prime, specifically 219937−12^{19937} – 1219937−1.
  • Purpose: To generate sequences of numbers that approximate true randomness for applications in simulations, games, statistical sampling, and more.
  • Key Characteristics:
    • Period: Extremely long period (219937−12^{19937} – 1219937−1).
    • Speed: Very fast and efficient for generating random numbers.
    • Quality: Produces numbers with a uniform distribution and passes many statistical tests for randomness.
  • Applications:
    • Widely used in simulations, cryptographic applications (though not cryptographically secure), and any situation requiring high-quality random number generation.
  • Efficiency:
    • It is computationally efficient, capable of generating random numbers quickly, making it suitable for applications requiring large volumes of random data.
  • Quality of Randomness:
    • The Mersenne Twister passes most standard statistical tests for randomness, ensuring the generated sequences appear random and unbiased.

How Does It Work?

The Mersenne Twister works by maintaining an internal state array of size N=624N = 624N=624, with each element being a 32-bit integer. The generator progresses by:

  1. Initialization:
    • The state array is initialized using a seed value (often a single integer).
  2. Recurrence Relation:
    • The generator uses a linear recurrence relation to update its state. At each step, a new value is computed by combining elements of the state array using bitwise operations and a carefully chosen set of constants.
  3. Tempering:
    • The output is “tempered” (processed further) to improve statistical properties and ensure the generated numbers are distributed uniformly.

Applications

  1. Simulations:
    • Widely used in Monte Carlo simulations and scientific modeling where high-quality random numbers are required.
  2. Games:
    • Randomness in video games, such as dice rolls, loot drops, or random events, often relies on the Mersenne Twister.
  3. Statistical Sampling:
    • Random sampling from datasets in statistics and machine learning.
  4. Randomized Algorithms:
    • Used in algorithms requiring randomness, such as quicksort or hash table probing.

Strengths

  • Extremely Long Period: The massive period ensures that the generator doesn’t repeat its sequence in realistic use cases.
  • Speed: Generates random numbers efficiently.
  • High Quality: It meets strict randomness requirements, making it suitable for most non-cryptographic applications.

Limitations

  1. Not Cryptographically Secure:
    • The Mersenne Twister is predictable if an attacker knows part of its internal state or a sequence of generated numbers. For cryptographic purposes, use secure PRNGs like Cryptographically Secure PseudoRandom Number Generators (CSPRNGs).
  2. Memory Usage:
    • The state array of size 624 integers (about 2.5 KB) is larger than simpler PRNGs like the Linear Congruential Generator (LCG).
  3. Initialization Time:
    • Initializing the state array can be slower compared to simpler generators.

Variants

Several variants of the Mersenne Twister have been developed to address specific use cases:

  1. MT19937:
    • The original 32-bit version of the Mersenne Twister.
  2. MT19937-64:
    • A 64-bit version of the Mersenne Twister, designed for 64-bit systems.
  3. TinyMT:
    • A smaller version with reduced state size, designed for embedded systems or applications with limited memory.

How to Use the Mersenne Twister in Programming

Most modern programming languages and libraries include the Mersenne Twister as the default or available PRNG:

  • Python
import random

random.seed(42)  # Initialize the generator with a seed
print(random.random())  # Generate a random float between 0 and 1
  • C++
#include <random>

std::mt19937 mt(42);  // Initialize with a seed
std::uniform_real_distribution<double> dist(0.0, 1.0);

double random_value = dist(mt);  // Generate a random number

Key Differences

AspectMersenne PrimeMersenne Twister
NatureMathematical concept (prime number).Algorithm for pseudorandom number generation.
Form2n−12^n – 12n−1, where nnn is a positive integer and 2n−12^n – 12n−1 is prime.Uses a long recurrence relation to generate random numbers.
PurposeStudied in number theory.Used in computational random number generation.
ApplicationsCryptography, pure math research.Simulations, games, machine learning, etc.
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