Kepolisian Inggris menangkap seorang penipu ulung bernama Hargobind Punjabi Tahilramani. Lelaki Indonesia keturunan India itu, dicokok pagi buta sekitar pukul 4.00 dari apartemennya di Church Street, Inggris, akhir November lalu dan akan diekstradisi ke AS.
Hargobind kelahiran Jakarta itu, dituduh memeras sejumlah korban yang berminat menjadi bintang film dan selebriti Hollywood. Mulai dari make-up artis, fotografer, pelatih kebugaran, pemeran pengganti, aktor dan lainnya. Dari hasil penipuan itu, Hargobind meraup uang sekitar $ 1,5 juta sampai $ 2 juta, selama dia menjalankan operasinya sejak tahun 2013.
Modus penipuannya: Hargobind yang lahir di Jakarta itu memiliki kemampuan menirukan suara seseorang, kebanyakan kaum wanita elite dan selebritas Hollywood. Seperti Kathleen Kennedy, presiden Lucasfilm AS, atau Amy Pascal, eksekutif produser Sony Pictures Television, dan banyak lagi. Termasuk Wendi Deng, mantan istri raja media Australia, Rupert Murdoch.
Dengan kebolehannya itu, Hargobind menelpon para korbannya untuk diorbitkan menjadi artis atau insan perfilman lainnya. Ia menjerat mereka agar berkunjung ke Indonesia untuk melakukan latihan akting atau apa saja. Mereka harus tinggal di hotel dan berkunjung ke Bali dan lainnya, untuk menjalani screening. Untuk itu, mereka harus membayar biaya ribuan dolar. Jika ada yang protes, karena tidak juga jadi bintang film, mereka akan diancam untuk dihabisi, oleh kaki tangan Hargobind yang mengatur seluruh rekayasa latihan tadi.
Majalah Vanity Fair, yang membongkar kasus ini pertama kali, mengungkapkan Hargobind pernah bekerjasama dengan Rudy Sutopo. Pria yang dikenal Gobind saat sama-sama berada di Lapas Cipinang, karena kasus penipuan tahun 2000-an itu, mendirikan perusahaan film yang akan memproduksi sebuah film berjudul ‘The Black Widow’. Mereka berhasil mengeruk untung miliaran rupiah.
Uniknya, Hargobind, pernah menggegerkan Kedutaan Besar AS di Jakarta. Menggunakan aksen Iran atau Afghanistan atau Rusia, ia menelepon Kedubes AS dan mengeluarkan ancaman bakal ada pengeboman. Tentu saja hal itu tidak pernah terjadi. ”Yang paling menggelikan, membuat keluarga saya frustrasi,” kata Hargobind, lulusan Gandhi Memorial Intercontinental School, Jakarta pada sebuah koran lokal.
Hargobind yang berdarah Pakistan dan Hong Kong itu , pernah menggelar Miss Teen Indonesia pageant, dan mengundang penyanyi Enrique Iglesias, untuk tampil di acara itu. Penyanyi kondang itu sempat tiba di Jakarta, tapi urung turun dari pesawat pribadinya setelah tahu bahwa ia dikibuli Hargobind.
Dengan menggunakan nama tengahnya Punjabi, Hargobind juga mengaku sebagai salah satu keluarga pengusaha film Indonesia kondang yang memproduksi banyak film dan sinetron itu. Belakangan, sebelum dijebloskan ke penjara, Hargobind juga mengaku sebagai pengusaha restoran, menciptakan sejumlah menu makanan, dan mengunggahnya di Instagram.
Hargobind Punjabi Tahilramani, juga mengaku tidak nyaman hidup di Indonesia sebagai seorang gay, karena selalu diolok-olok. ”Mungkin karena saya tidak suka sepakbola, mereka menertawakan saya dan memanggil saya Banci,” katanya dalam sebuah video September lalu.
Semua tingkah laku kebohongannya itu pun diungkap dan diadukan oleh para korbannya ke kepolisian AS. Biro Investigasi Federal, FBI, pun memantau seluruh kelakuan buruknya itu. Bekerjasama dengan K2 Integrity Agency, badan investigasi swasta yang sejak lama dipercaya sejumlah eksekutif Hollywood memantau sepak terjang penipu ulung itu, mereka pun menangkap Hargobind PunjabiTahilramani. ”Bekerjasama dengan pihak keamanan Indonesia, kami mampu membawa Con Queen of Hollywood to justice,” tutur Jules Kroll, co-founder K2 kepada The Independent. (DP)
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Your article helped me a lot, is there any more related content? Thanks!
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Your point of view caught my eye and was very interesting. Thanks. I have a question for you.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
Have you considered the impact of mobile-first indexing on these placements? We’ve noticed that some “desktop-safe” strategies are flagging on mobile crawls.
This is exactly why we moved away from automated PBNs. The risk/reward ratio just doesn’t make sense anymore compared to what you’re describing.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
Finally, someone said it. The old school “blast and pray” method is dead. Precision and camouflage are the new standard.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
Spot on about the indexing delays. It’s not just about building the link anymore; it’s about the “stickiness” of the placement. We’ve been focusing heavily on that metric lately.
This is the missing piece of the puzzle for us. We had the content and the technical SEO, but the off-page signal diversity was lacking. Thanks for the clarity.
I’d argue that the content relevance is even more critical now. We’ve seen perfectly good links get devalued just because the semantic match wasn’t tight enough.
Question: Have you tested this approach with expired domains? We’re running some experiments now and the results are… mixed. Your methodology seems safer.
I bookmarked this for my team. The section on avoiding footprints is crucial. We recently audited a site that got hit exactly because they ignored that principle. Good catch.
I’ve been following this topic for a while, and your analysis on the structural shifts really adds a new perspective. We’ve noticed similar patterns in our internal data at SignalLayer, specifically regarding the volatility timeline.
Spot on about the indexing delays. It’s not just about building the link anymore; it’s about the “stickiness” of the placement. We’ve been focusing heavily on that metric lately.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
Spot on about the indexing delays. It’s not just about building the link anymore; it’s about the “stickiness” of the placement. We’ve been focusing heavily on that metric lately.
One minor correction: the update rollout was actually 14 days, not 10. But that doesn’t change your main point—the volatility window is getting wider.
Thanks for the transparency. It’s refreshing to see a strategy that doesn’t rely on black-hat churn and burn. Sustainable growth is the only way forward.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
This is exactly why we moved away from automated PBNs. The risk/reward ratio just doesn’t make sense anymore compared to what you’re describing.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
Great read. It reminds me of the strategy we deployed last quarter. The focus on foundational stability really pays off when the algorithm shifts. Thanks for compiling this.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
Question: Have you tested this approach with expired domains? We’re running some experiments now and the results are… mixed. Your methodology seems safer.
Thanks for the transparency. It’s refreshing to see a strategy that doesn’t rely on black-hat churn and burn. Sustainable growth is the only way forward.
Thanks for the transparency. It’s refreshing to see a strategy that doesn’t rely on black-hat churn and burn. Sustainable growth is the only way forward.
This is the missing piece of the puzzle for us. We had the content and the technical SEO, but the off-page signal diversity was lacking. Thanks for the clarity.
This is the missing piece of the puzzle for us. We had the content and the technical SEO, but the off-page signal diversity was lacking. Thanks for the clarity.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.
This is exactly why we moved away from automated PBNs. The risk/reward ratio just doesn’t make sense anymore compared to what you’re describing.
One minor correction: the update rollout was actually 14 days, not 10. But that doesn’t change your main point—the volatility window is getting wider.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
Question: Have you tested this approach with expired domains? We’re running some experiments now and the results are… mixed. Your methodology seems safer.
The depth here is impressive. Most guides just skim the surface of link velocity, but your point about “natural variance” hits the nail on the head. It’s exactly what we preach to our clients.
This is the missing piece of the puzzle for us. We had the content and the technical SEO, but the off-page signal diversity was lacking. Thanks for the clarity.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between “diversity” and “randomness” is what saves you during a Core Update.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between “diversity” and “randomness” is what saves you during a Core Update.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
Great read. It reminds me of the strategy we deployed last quarter. The focus on foundational stability really pays off when the algorithm shifts. Thanks for compiling this.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
Thanks for the transparency. It’s refreshing to see a strategy that doesn’t rely on black-hat churn and burn. Sustainable growth is the only way forward.
The depth here is impressive. Most guides just skim the surface of link velocity, but your point about “natural variance” hits the nail on the head. It’s exactly what we preach to our clients.
One minor correction: the update rollout was actually 14 days, not 10. But that doesn’t change your main point—the volatility window is getting wider.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
This is the missing piece of the puzzle for us. We had the content and the technical SEO, but the off-page signal diversity was lacking. Thanks for the clarity.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
One minor correction: the update rollout was actually 14 days, not 10. But that doesn’t change your main point—the volatility window is getting wider.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
I bookmarked this for my team. The section on avoiding footprints is crucial. We recently audited a site that got hit exactly because they ignored that principle. Good catch.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
This is the missing piece of the puzzle for us. We had the content and the technical SEO, but the off-page signal diversity was lacking. Thanks for the clarity.
I’m sharing this with our content team. We’ve been struggling to explain why “quality over quantity” isn’t just a cliché, and this illustrates it perfectly.
This is exactly why we moved away from automated PBNs. The risk/reward ratio just doesn’t make sense anymore compared to what you’re describing.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
This aligns with the “Signal Noise” theory we’ve been developing. You need enough noise to mask the signal, but not so much that you lose authority. delicate balance.
Thanks for the transparency. It’s refreshing to see a strategy that doesn’t rely on black-hat churn and burn. Sustainable growth is the only way forward.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
Great read. It reminds me of the strategy we deployed last quarter. The focus on foundational stability really pays off when the algorithm shifts. Thanks for compiling this.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
This is exactly why we moved away from automated PBNs. The risk/reward ratio just doesn’t make sense anymore compared to what you’re describing.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
Spot on about the indexing delays. It’s not just about building the link anymore; it’s about the “stickiness” of the placement. We’ve been focusing heavily on that metric lately.
I’ve been following this topic for a while, and your analysis on the structural shifts really adds a new perspective. We’ve noticed similar patterns in our internal data at SignalLayer, specifically regarding the volatility timeline.
I’d love to see a follow-up post on how this integrates with social signals. We feel there’s a multiplier effect there that isn’t being fully utilized.
The depth here is impressive. Most guides just skim the surface of link velocity, but your point about “natural variance” hits the nail on the head. It’s exactly what we preach to our clients.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
Finally, someone said it. The old school “blast and pray” method is dead. Precision and camouflage are the new standard.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
This is the missing piece of the puzzle for us. We had the content and the technical SEO, but the off-page signal diversity was lacking. Thanks for the clarity.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between “diversity” and “randomness” is what saves you during a Core Update.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
This complements the “Entropy” theory perfectly. If you don’t introduce randomness, you’re just painting a target on your back. Glad to see others advocating for smarter engineering.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
I’d argue that the content relevance is even more critical now. We’ve seen perfectly good links get devalued just because the semantic match wasn’t tight enough.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
The analogy of the “immune system” is perfect. You need to build resistance before the virus (update) hits. Too many people react instead of prepare.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
Have you considered the impact of mobile-first indexing on these placements? We’ve noticed that some “desktop-safe” strategies are flagging on mobile crawls.
I’ve been following this topic for a while, and your analysis on the structural shifts really adds a new perspective. We’ve noticed similar patterns in our internal data at SignalLayer, specifically regarding the volatility timeline.
The shift towards “entity-based” indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between “diversity” and “randomness” is what saves you during a Core Update.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between “diversity” and “randomness” is what saves you during a Core Update.
This is exactly why we moved away from automated PBNs. The risk/reward ratio just doesn’t make sense anymore compared to what you’re describing.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
Great read. It reminds me of the strategy we deployed last quarter. The focus on foundational stability really pays off when the algorithm shifts. Thanks for compiling this.
This aligns with the “Signal Noise” theory we’ve been developing. You need enough noise to mask the signal, but not so much that you lose authority. delicate balance.
Does this apply to non-English markets as well? We’re seeing conflicting signals in our EU campaigns compared to what you’ve described here. Would love to hear your thoughts on regional variance.
I’ve been following this topic for a while, and your analysis on the structural shifts really adds a new perspective. We’ve noticed similar patterns in our internal data at SignalLayer, specifically regarding the volatility timeline.
I’d argue that the content relevance is even more critical now. We’ve seen perfectly good links get devalued just because the semantic match wasn’t tight enough.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
Have you considered the impact of mobile-first indexing on these placements? We’ve noticed that some “desktop-safe” strategies are flagging on mobile crawls.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
I’m curious about the sample size for these conclusions. We saw a 15% deviation in our own datasets, but the overall trend aligns with your findings. Good work.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.
I’m skeptical about the timeline you proposed, but I’m willing to test it. If this holds up, it changes how we structure our entire outreach program.
Great read. It reminds me of the strategy we deployed last quarter. The focus on foundational stability really pays off when the algorithm shifts. Thanks for compiling this.
One minor correction: the update rollout was actually 14 days, not 10. But that doesn’t change your main point—the volatility window is getting wider.
This is a solid breakdown. One thing I’d add is that the impact of these updates often lags by 2-3 weeks. We tracked this across multiple projects and found the recovery phase is where most people give up too early.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
I’ve been following this topic for a while, and your analysis on the structural shifts really adds a new perspective. We’ve noticed similar patterns in our internal data at SignalLayer, specifically regarding the volatility timeline.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.
One minor correction: the update rollout was actually 14 days, not 10. But that doesn’t change your main point—the volatility window is getting wider.
This aligns with the “Signal Noise” theory we’ve been developing. You need enough noise to mask the signal, but not so much that you lose authority. delicate balance.
Just wanted to say thanks for the detailed case study. It’s rare to see actual data backing up these claims. We’ll be adjusting our Q4 roadmap based on some of these insights.
Actually, I have to disagree slightly with the second point. In our testing, we found that over-optimization was less of a factor than pure engagement metrics. It’s interesting to see how different niches react differently.
We’ve been A/B testing this exact hypothesis. Group A (your method) is outperforming Group B by 40% in terms of ranking stability. The data speaks for itself.
This aligns with the “Signal Noise” theory we’ve been developing. You need enough noise to mask the signal, but not so much that you lose authority. delicate balance.
Have you considered the impact of mobile-first indexing on these placements? We’ve noticed that some “desktop-safe” strategies are flagging on mobile crawls.
Have you considered the impact of mobile-first indexing on these placements? We’ve noticed that some “desktop-safe” strategies are flagging on mobile crawls.
I’d argue that the content relevance is even more critical now. We’ve seen perfectly good links get devalued just because the semantic match wasn’t tight enough.
I bookmarked this for my team. The section on avoiding footprints is crucial. We recently audited a site that got hit exactly because they ignored that principle. Good catch.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.