Category: event

我最近聽說
Wǒ zuìjìn tīngshuō
I recently heard

分開后的你好像更快樂
Fēnkāi hòu de nǐ hǎoxiàng gèng kuàilè
That after we separated, you seem happier

本該替你開心為何又替自己難過
Běn gāi tì nǐ kāixīn, wèihé yòu tì zìjǐ nánguò
I should be happy for you, so why am I sad for myself again?

還給你解脫
Hái gěi nǐ jiětuō
I even gave you your freedom

又害怕你很快就忘了我
Yòu hàipà nǐ hěn kuài jiù wàng le wǒ
Yet I’m afraid you’ll soon forget me

這種感覺比中了槍還要痛徹
Zhè zhǒng gǎnjué bǐ zhòng le qiāng hái yào tòngchè
This feeling hurts even more than being shot

---

我明明知道
Wǒ míngmíng zhīdào
I clearly know

誰離開了誰都能好好過
Shéi líkāi le shéi dōu néng hǎohāo guò
That whoever leaves, both people can go on and live well

勸着自己放下偏偏回憶不放過我
Quànzhe zìjǐ fàngxià, piānpiān huíyì bù fàngguò wǒ
I tell myself to let go, but memories just won’t let me go

你說走就走
Nǐ shuō zǒu jiù zǒu
You said you were leaving, and you simply left

眼裡沒有一絲絲的不舍
Yǎn lǐ méiyǒu yì sī sī de bùshě
There wasn’t even the slightest trace of reluctance in your eyes

我該怎麼習慣沒有你的生活
Wǒ gāi zěnme xíguàn méiyǒu nǐ de shēnghuó
How am I supposed to get used to a life without you?

---

我發了瘋的愛你
Wǒ fā le fēng de ài nǐ
I loved you like I was crazy

又拼了命的忘記
Yòu pīn le mìng de wàngjì
And desperately tried to forget you

nga ni nion bai qu la ga so dui qie nai jie zi qie
(phonetic transcription as provided)

早知會痛
Zǎo zhī huì tòng
If I had known it would hurt

我就不該動情
Wǒ jiù bù gāi dòngqíng
I should never have fallen in love

na zu nga xie nga yi ze dung mi jin
(phonetic transcription as provided)

我奉上全部真心
Wǒ fèngshàng quánbù zhēnxīn
I gave you all of my true love

沒有回頭的餘地
Méiyǒu huítóu de yúdì
With no room to turn back

卻換來你不要我的結局
Què huànlái nǐ bú yào wǒ de jiéjú
Yet all I got in return was an ending where you didn’t want me

---

我發了瘋的愛你
Wǒ fā le fēng de ài nǐ
I loved you like I was crazy

又拼了命的回憶
Yòu pīn le mìng de huíyì
And desperately held onto the memories

nga ni nion bai qu la ga so dui qie nai qi zhen qie
(phonetic transcription as provided)

想過找你
Xiǎng guò zhǎo nǐ
I’ve thought about looking for you

我卻沒有勇氣
Wǒ què méiyǒu yǒngqì
But I don’t have the courage

qu cou sam yang nga la ning dob min du
(phonetic transcription as provided)

有一種方式死心
Yǒu yì zhǒng fāngshì sǐxīn
There is one way to finally give up

是找個人代替你
Shì zhǎo ge rén dàitì nǐ
And that is to find someone to replace you

可惜我生命里除了你
Kěxī wǒ shēngmìng lǐ chúle nǐ
Sadly, in my life, apart from you

誰也走不進去
Shéi yě zǒu bù jìnqù
No one else can enter

---

我明明知道
Wǒ míngmíng zhīdào
I clearly know

誰離開了誰都能好好過
Shéi líkāi le shéi dōu néng hǎohāo guò
That whoever leaves, both people can go on and live well

勸着自己放下偏偏回憶不放過我
Quànzhe zìjǐ fàngxià, piānpiān huíyì bù fàngguò wǒ
I tell myself to let go, but memories just won’t let me go

你說走就走
Nǐ shuō zǒu jiù zǒu
You said you were leaving, and you simply left

眼裡沒有一絲絲的不舍
Yǎn lǐ méiyǒu yì sī sī de bùshě
There wasn’t even the slightest trace of reluctance in your eyes

我該怎麼習慣沒有你的生活
Wǒ gāi zěnme xíguàn méiyǒu nǐ de shēnghuó
How am I supposed to get used to a life without you?

---

我發了瘋的愛你
Wǒ fā le fēng de ài nǐ
I loved you like I was crazy

又拼了命的忘記
Yòu pīn le mìng de wàngjì
And desperately tried to forget you

nga ni nion bai qu la ga so dui qie nai jie zi qie
(phonetic transcription as provided)

早知會痛
Zǎo zhī huì tòng
If I had known it would hurt

我就不該動情
Wǒ jiù bù gāi dòngqíng
I should never have fallen in love

na zu nga xie nga yi ze dung mi jin
(phonetic transcription as provided)

我奉上全部真心
Wǒ fèngshàng quánbù zhēnxīn
I gave you all of my true love

沒有回頭的餘地
Méiyǒu huítóu de yúdì
With no room to turn back

卻換來你不要我的結局
Què huànlái nǐ bú yào wǒ de jiéjú
Yet all I got in return was an ending where you didn’t want me

---

我發了瘋的愛你
Wǒ fā le fēng de ài nǐ
I loved you like I was crazy

又拼了命的回憶
Yòu pīn le mìng de huíyì
And desperately held onto the memories

nga ni nion bai qu la ga so dui qie nai qi zhen qie
(phonetic transcription as provided)

想過找你
Xiǎng guò zhǎo nǐ
I’ve thought about looking for you

我卻沒有勇氣
Wǒ què méiyǒu yǒngqì
But I don’t have the courage

qu cou sam yang nga la ning dob min du
(phonetic transcription as provided)

有一種方式死心
Yǒu yì zhǒng fāngshì sǐxīn
There is one way to finally give up

是找個人代替你
Shì zhǎo ge rén dàitì nǐ
And that is to find someone to replace you

可惜我生命里除了你
Kěxī wǒ shēngmìng lǐ chúle nǐ
Sadly, in my life, apart from you

誰也走不進去
Shéi yě zǒu bù jìnqù
No one else can enter

Tidak semua yang kita inginkan akan terwujud dan tidak semua yang kita takutkan akan terjadi.

High And Low Movie Order

For those of you who like fierce battle scenes involving a group of men with a vibrant youth, High And Low is a film that deserves to be recommended to you.

This isn’t just a story about a bunch of young people hitting other people because they find it fun, it’s so corny, but it’s so much more than that. This is a story about a group of young people who fight against each other because they think that’s the only way for them to go.

You will see how friendship, betrayal, search for identity, revenge, hope, dreams, and peace build the entire focus of the story in the film.

Ok, now you are getting more interested and immediately want to see it for yourself, so I will guide you how to watch High And Low in chronological order.

A legend begins in a town. Mugen, a powerful gang managed to control the area with extraordinary combat power. But a pair of men who call themselves the Amamiya Brothers refuse to submit to Mugen and they fight without a winner.

The story that became a legend in the city grew when Mugen disbanded after a mysterious incident and the powerful and invincible Amamiya Brothers also disappeared. Five new groups emerged and quickly took control of the areas Mugen had left behind.

They are Sannoh Rengokai, White Rascals, Oya Koukou, Rude Boys, and Daruma Ikka. The five groups are tasked with maintaining balance and are known as S.W.O.R.D.

1. High and Low: The Story of S.W.O.R.D (2015)
2. High and Low Season 2 (2016)
3. Road To High & Low (2016)
4. High and Low: The Movie (2016)
5. High and Low: The Red Rain (2016)
6. High and Low The Movie 2: End of The Sky (2017)
7. High and Low The Movie 3: Final Mission (2017)
8. DTC Yukemuri Junjo hen From High & Low (2018)
9. High and Low The Worst Episode 0 (2019)
10. High and Low The Worst (2019)
11. 6 From High & Low The Worst

血债必须要血偿

Blood debt must be paid in blood

Hutang darah harus dibayar dengan darah

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

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

Cycle	Halving	ATH Lag (months)	ATH Date	ATH Price	% from Halving
1	Nov 2012	~12	Nov 2013	$1.1k	+9,000 %
2	Jul 2016	~17	Dec 2017	$19.6k	+2,400 %
3	May 2020	~18	Nov 2021	$69k	+250 %
4	Apr 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

Scenario	Expected ATH Year	Time from Halving	Reasoning
Base Case (historical average)	Late 2025 → Early 2026	16–18 months	Mirrors 2016/2020 pattern; supply shock from 2024 halving peaks mid-2026
Extended Cycle (ETF & institutional adoption)	Mid 2026 → Late 2027	20–28 months	Slower but longer bull due to capital inflow pacing
Aggressive Case (compressed FOMO)	Aug 2025 → Dec 2025	12–16 months	Continuation of 2025’s parabolic run if liquidity surges fast
Next-Cycle ATH (after 2028 halving)	Late 2029 → 2030	16–20 months	For Cycle 5; long-term 8-year super-cycle potential

---

💰 Price Range Forecast (Conservative to Bullish)

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

---

🧭 Summary

Phase	Year Range	Cycle Behavior	Expected Trend
Accumulation	2023 – Apr 2024	Pre-halving consolidation	Neutral to slightly bullish
Bull Run	Apr 2024 – 2026	Post-halving expansion	🚀 Major price appreciation
Peak + Distribution	Late 2025 – 2026	Parabolic top formation	Potential $200 k–$250 k ATH
Bear Market	2026 – 2027	Cooling, 60–80 % drawdown	Return to ~$80 k–$100 k
Recovery → Next Halving	2027 – 2028	Slow rebuild	Prepares for next run
Next ATH	2029 – 2030	Cycle 5 climax	Possible $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

Year	Phase	Description	Halving	ATH	Notes
2012	⚙️ Accumulation	Bitcoin emerging market, price <$10	✅ Nov 2012	—	Start of first halving cycle
2013	🟩 Bull Run	Price rises from ~$13 → $1,163	—	Nov 2013	+9,000% gain; first major mania
2014–2015	🔴 Bear	83% drawdown; Mt. Gox crash	—	—	Bottom near $150
2016	⚙️ Accumulation → Bull Start	Recovery begins	✅ Jul 2016	—	Entry to second cycle
2017	🟩 Bull Run	Price $1k → $19.6k	—	Dec 2017	+2,400% gain
2018	🔴 Bear	84% drop	—	—	Crypto Winter
2019	⚙️ Accumulation	Sideways 3k–10k	—	—	Pre-halving buildup
2020	🟩 Bull Run Start	COVID bottom → strong rally	✅ May 2020	—	Supply shock begins
2021	🟩 Bull Run Peak	$69k ATH	—	Nov 2021	+250% cycle gain
2022	🔴 Bear	FTX/LUNA collapse; bottom ~$15k	—	—	77% drawdown
2023	⚙️ Accumulation	Recovery 20k → 40k	—	—	ETF anticipation builds
2024	🟩 Bull Run Start	Post-halving rally begins	✅ Apr 2024	—	Cycle 4 active
2025	🟩 Bull Run Peak	ATH ~$124k	—	Aug 2025	+80% from prev. ATH
2026	🟥 Transition → Bear	Cooling, distribution phase	—	—	Top formation year
2027	🔴 Bear	Retest ~80–100k zone	—	—	60–70% correction expected
2028	⚙️ Accumulation	Rebuild phase	✅ Mar 2028 (est.)	—	Start of Cycle 5
2029–2030	🟩 Bull Run	Massive liquidity + adoption	—	2030 (est.)	Predicted ATH $500k–750k

---

📈 Summary Statistics

Metric	Historical Avg	Future Expectation
Cycle Length	4 years (≈48 months)	May extend to 5 years (60 mo)
Time from Halving → ATH	16–18 months	18–24 months (extended cycle)
Bull Run Green Months	~10–12	10–14 (expected)
Bearish Red Months	~9–10	9–12 (expected)
Avg Bull Gain	20–60×	2–4× from last ATH

---

🔮 Forecast Summary

Scenario	Expected ATH Year	Price Range	Confidence
Base Case (historical)	Late 2025 – Early 2026	$180k – $250k	⭐⭐⭐⭐
Extended Cycle	Mid 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.)

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.

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.”