Hace 3 meses se estrenaba la nueva versión de “Himno de mi corazón”, donde se reunían nada menos que Andrés Calamaro, Cachorro López y Daniel Melingo. Todos los fans festejaron y se emocionaron con este regreso inesperado. A su vez, las nuevas generaciones descubren un legado de canciones inoxidables, que nos dejaron Los Abuelos de la Nada.
Hoy se estrena otra de las perlas de este álbum que presentará nuevas versiones de los temas de Los Abuelos. Se trata nada menos de “Así es el calor”, aquel hit de los 80’ que, en la voz de Andrés Calamaro (co-autor junto a Gringui Herrera), sonó y suena en todas las radios del país y que además cuenta con millones de reproducciones en Plataformas Digitales.
Esta vez, es con la participación de El Zar, junto a Cachorro López y más los coros del propio Andrés Calamaro. Una versión fina, exquisita, que cuenta con un videoclip dirigido por Valentine, para la productora Feel Co. Mientras que Cachorro aporta toda su experiencia y sabiduría, los chicos del El Zar le dan un toque renovador a este hit.
Las canciones de Los Abuelos de la Nada siguen vivas. En la memoria de la gente, en la poesía de Miguel Abuelo, en aquella química increíble que se dio a principio de los ’80 entre Miguel, Cachorro López, Andrés Calamaro, Daniel Melingo, Gustavo Bazterrica y Polo Corbella.
Este es el segundo adelanto de un álbum que, con producción de Cachorro López, verá la luz en abril de 2024, y que también contará con la participación de Gustavo Bazterrica, el guitarrista de la formación original. No faltarán los grandes hits de Abuelos, aquellos que marcaron toda una época.
Así es el calor
(Andrés Calamaro-Gringui Herrera)
Ni me acuerdo mi nombre
Muy tranquilo en la arena
El rumor de la calle
Poco me interesa
Aquí, bajo el sol
Aquí, ¡hace tanto calor!
No, no me saquen de aquí, por favor
Estoy demasiado tranquilo
No quiero enterarme de nada hoy
¡Así es el calor!
La del medio está buena
Yo me siento muy bien también
En la apacible vereda
La vida se renueva
Aquí, bajo el sol
Aquí, ¡hace tanto calor!
No, no me saquen de aquí, por favor
Estoy demasiado tranquilo
No quiero enterarme de nada hoy
¡Así es el calor!
This is exactly what I was looking for. Clear, concise, and helpful.
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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.
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.
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 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.
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.
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.
Question: Have you tested this approach with expired domains? We’re running some experiments now and the results are… mixed. Your methodology seems safer.
Finally, someone said it. The old school «blast and pray» method is dead. Precision and camouflage are the new standard.
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.
The shift towards «entity-based» indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
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.
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 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 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.
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 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.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
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.
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.
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.
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’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.
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.
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 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.
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.
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.
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.
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.
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.
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’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.
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’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.
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.
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’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.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
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.
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.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
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.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
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.
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.
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 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.
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.
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.
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.
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.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between «diversity» and «randomness» is what saves you during a Core Update.
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.
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 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.
Finally, someone said it. The old school «blast and pray» method is dead. Precision and camouflage are the new standard.
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 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.
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.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
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 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.
Is there a specific tool you recommend for tracking the velocity? We’ve been doing it manually but it’s becoming unscalable.
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.
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 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’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.
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’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.
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.
Brilliant articulation of the problem. The industry has been too focused on metrics like DA/DR instead of actual traffic flow and user behavior.
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.
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.
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.
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.
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 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.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
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.
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’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.
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.
The shift towards «entity-based» indexing is real. Your strategy seems to leverage that by building entity associations rather than just keyword matches. Smart.
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.
Finally, someone said it. The old school «blast and pray» method is dead. Precision and camouflage are the new standard.
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.
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 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.
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.
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.
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’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.
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.
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.
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 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’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.
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.
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.
Great resource. I’ve sent this to a few colleagues who are still stuck in 2015-era SEO tactics. Hopefully, this wakes them up.
Finally, someone said it. The old school «blast and pray» method is dead. Precision and camouflage are the new standard.
Question: Have you tested this approach with expired domains? We’re running some experiments now and the results are… mixed. Your methodology seems safer.
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’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.
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.
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.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between «diversity» and «randomness» is what saves you during a Core Update.
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’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 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.
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.
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.
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.
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.
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.
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.
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.
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.
For anyone reading this, pay attention to paragraph 4. That subtle distinction between «diversity» and «randomness» is what saves you during a Core Update.
Question: Have you tested this approach with expired domains? We’re running some experiments now and the results are… mixed. Your methodology seems safer.
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.