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Business leaders continue to view geopolitical tensions as the biggest risk to economic growth, say McKinsey senior partner Sven Smit and coauthors. To learn how companies can proactively respond to global risks while focusing on value creation, check out the latest edition of the Five Fifty.
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by "McKinsey Quarterly Five Fifty" <publishing@email.mckinsey.com> - 05:14 - 21 Jan 2025 -
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P.S. Don’t forget, you’re just one funnel away…
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by "Russell Brunson" <noreply@clickfunnelsnotifications.com> - 03:31 - 21 Jan 2025 -
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by "ByteByteGo" <bytebytego@substack.com> - 12:23 - 21 Jan 2025 -
How LinkedIn Scaled User Restriction System to 5 Million Queries Per Second
How LinkedIn Scaled User Restriction System to 5 Million Queries Per Second
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In this eBook, you'll learn about business observability best practices through real world use cases from Datadog customers. You'll also get an actionable framework for selecting and implementing a business observability strategy yourself.Disclaimer: The details in this post have been derived from the LinkedIn Engineering Blog. All credit for the technical details goes to the LinkedIn engineering team. The links to the original articles are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
One of the primary goals of LinkedIn is to provide a safe and professional environment for its members.
At the heart of this effort lies a system called CASAL.
CASAL stands for Community Abuse and Safety Application Layer. This platform is the first line of defense against bad actors and adversarial attacks. It combines technology and human expertise to identify and prevent harmful activities.
The various aspects of this system are as follows:
ML Models: The ML models analyze patterns in user behavior to detect anything unusual or suspicious. For example, if a user suddenly sends hundreds of connection requests to strangers or repeatedly posts harmful content, the system can flag these activities for review.
Rule-Based Systems: These systems work based on pre-defined rules. Think of them as guidelines that help the platform decide what’s acceptable and what’s not. For instance, certain words or actions that violate LinkedIn’s policies (like hate speech or spam) automatically trigger alerts.
Human Review Processes: Not everything can be left to machines. A dedicated team of human experts steps in to review flagged activities and make decisions on borderline cases.
Multi-Faceted Restrictions: Not all harmful activities are the same. This is why LinkedIn uses multi-faceted restrictions. Some restrictions might involve temporarily limiting a user’s actions, like stopping them from sending connection requests for a while. Other situations may require more severe measures, such as permanently blocking an account if it poses a significant threat.
Together, these tools form a multi-layered shield, protecting LinkedIn’s community from abuse while maintaining a professional and trusted space for networking.
In this article, we’ll look at the design and evolution of LinkedIn’s enforcement infrastructure in detail.
Evolution of Enforcement Infrastructure
There have been three major generations of LinkedIn’s restriction enforcement system. Let’s look at each generation in detail.
First Generation
Initially, LinkedIn used a relational database (Oracle) to store and manage restriction data.
Restrictions were stored in Oracle tables, with different types of restrictions isolated into separate tables for better organization and manageability. CRUD (Create, Read, Update, Delete) workflows were designed to handle the lifecycle of restriction records, ensuring proper updates and removal when necessary.
See the diagram below:
Source: LinkedIn Engineering Blog However, this approach posed a few challenges:
As LinkedIn grew and transitioned to a microservices architecture, the relational database approach couldn’t keep up with the increasing demand.
The architecture became cumbersome due to Oracle’s limitations in handling high query volumes and maintaining low latency.
Server-Side Cache Implementation
To address the scaling challenges, the team introduced server-side caching. This significantly reduced latency by minimizing the need for frequent database queries.
A cache-aside strategy was employed that worked as follows:
When restriction data was requested, the system first checked the in-memory cache.
If the data was present in the cache (cache hit), it was served immediately.
If the data was not found (cache miss), it was fetched from the database and asynchronously updated in the cache for future requests.
See the diagram below that shows the server-side cache approach:
Restrictions were assigned predefined TTL (Time-to-Live) values, ensuring the cached data was refreshed periodically.
There were also shortcomings with this approach:
The server-side cache wasn’t distributed, meaning individual hosts had to manage their caches.
The approach worked well for low-traffic scenarios, but struggled under high cache-hit demands, necessitating further improvements.
Client-Side Cache Addition
Building on the server-side cache, LinkedIn introduced client-side caching to enhance performance further. This approach enabled upstream applications (like LinkedIn Feed and Talent Solutions) to maintain their local caches.
See the diagram below:
To facilitate this, a client-side library was developed to cache the restriction data directly on application hosts, reducing the dependency on server-side caches.
Even this approach had some challenges as follows:
Client-side caching increased operational complexity as engineers had to maintain consistency between client and server caches.
Refresh operations strained the database, especially during updates or restarts when caches had to be reloaded.
Full Refresh-Ahead Cache
To overcome some of the challenges with client-side caching, the team adopted a full refresh-ahead cache model for the system.
In this approach, each client stored all restriction data in its local memory, eliminating the need for frequent database queries. A polling mechanism regularly checked for updates to maintain cache freshness.
This led to a remarkable improvement in latencies, primarily because all member data was readily available on the client side. There was no need for network calls.
However, the approach also had some limitations and trade-offs:
The memory footprint was significant, as each client needed enough memory to store the entire dataset.
During application restarts or deployments, the system experienced high resource consumption to reload all data, resulting in performance bottlenecks.
The increased database load during refresh operations led to latency spikes and stressed the Oracle database infrastructure.
Bloom Filters
To address scalability and efficiency challenges, LinkedIn implemented Bloom Filters.
Bloom Filter is a probabilistic data structure designed to handle large datasets efficiently. Instead of storing the full dataset, it used a compact, memory-efficient encoding to determine whether a restriction record existed in the system. If a query matched a record in the Bloom Filter, the system would proceed to apply the restriction.
The main advantage of using Bloom Filter was to conserve valuable resources. It reduced the memory footprint compared to traditional caching mechanisms. Also, queries were processed rapidly, improving system responsiveness.
However, Bloom Filters also had some trade-offs:
Bloom Filters are probabilistic, meaning there is a small chance of false positives where the filter incorrectly indicates a restriction exists.
Despite this, the trade-off was deemed acceptable for LinkedIn’s use case, as it ensured high performance and scalability.
Second Generation
As LinkedIn grew and its platform became more complex, the engineering team recognized the limitations of its first-generation system, which relied heavily on relational databases and caching strategies.
To keep up with the demands of a billion-member platform, the second generation of LinkedIn’s restriction enforcement system was designed.
The second-generation system had several important goals such as:
High QPS (4-5 million): The system had to handle restriction enforcement for every request across LinkedIn’s extensive product offerings.
Ultra-Low Latency (<5 ms): The new system relied on in-memory lookups for fetching restriction data, eliminating the need for repeated database queries. This approach drastically reduced response times, ensuring a seamless experience for LinkedIn members and applications.
High Availability (Five 9’s): The system was designed to achieve 99.999% availability, a level of reliability critical for enforcing restrictions without interruptions. By distributing data across multiple nodes and data centers, the system minimized the risk of downtime.
Horizontal Scaling: To support the growing volume of restrictions and requests, the system could scale horizontally by adding more nodes or servers.
Data Freshness and Synchronization: Real-time updates through Kafka ensured that all restriction data remained synchronized across the platform, avoiding inconsistencies.
Adoption of NoSQL Distributed Systems
One of the key innovations in this generation was the migration of restriction data management to LinkedIn’s Espresso, a custom-built distributed NoSQL document store.
This is because relational databases like Oracle struggled with the high query throughput and latency requirements of LinkedIn’s growing platform. Espresso, being a distributed NoSQL system, provided better scalability and performance while maintaining data consistency.
Espresso was tightly integrated with Kafka, LinkedIn’s real-time data streaming platform. Every time a new restriction record was created, Espresso would emit Kafka messages containing the data and metadata of the record. These Kafka messages enabled real-time synchronization of restriction data across multiple servers and data centers, ensuring that the system always had the latest information.
See the diagram below to understand the architecture of the 2nd generation restriction enforcement system.
Despite its many advancements, the second-generation system faced some operational challenges, particularly during specific scenarios:
Bootstrapping Data During Restarts:
When a server or application restarted, it needed to reload all restriction records from the database into its memory.
This process, known as bootstrapping, was resource-intensive and time-consuming, often taking over 30 minutes for large datasets.
The high load during bootstrapping could strain the Espresso database and impact system performance.
Handling Large-Scale Growth: While the system scaled well horizontally, the sheer volume of restriction records and requests during periods of high adversarial activity tested the limits of infrastructure.
CAP Theorem
LinkedIn had to make critical architectural choices concerning CAP Theorem trade-offs while designing the second-generation system.
The CAP Theorem states that a system can only achieve two out of the following three guarantees at any given time:
Consistency (C): Every read receives the most recent write or an error.
Availability (A): Every request receives a non-error response, regardless of the state of any individual node.
Partition Tolerance (P): The system continues to operate even when there is a network partition.
LinkedIn prioritized consistency (C) and availability (A) over partition tolerance (P). The decision was driven by their latency and reliability goals.
The restriction enforcement system needed to provide accurate and up-to-date data across the platform. Incorrect or outdated restriction records could lead to security lapses or poor user experiences. Also, high availability was essential for ensuring that restrictions could be enforced seamlessly, even during peak activity periods.
LinkedIn's previous experiences with partitioned databases revealed that partitions could introduce latencies that conflicted with their stringent performance requirements (for example, ultra-low latency of <5 ms).
The use of Espresso allowed LinkedIn to handle consistency and availability more effectively within their system design. Integration with Kafka ensured that restriction records were synchronized across servers in real-time, maintaining consistency without significant delays.
Third Generation
As LinkedIn grew even further, the second-generation restriction enforcement system, though robust, began to show strain under the increasing volume of data and adversarial attacks.
Therefore, the LinkedIn engineering team implemented a new generation of its restriction enforcement system. See the diagram below:
The third generation introduced innovations focused on optimizing memory usage, improving resilience, and accelerating the bootstrap process.
1 - Off-Heap Memory Utilization
One of the major bottlenecks in the second-generation system was the reliance on in-heap memory for data storage. This approach led to challenges with Garbage Collection (GC) cycles, which caused latency spikes and degraded system performance.
To address these issues, the third-generation system moved data storage to off-heap memory.
Unlike in-heap memory (managed by the Java Virtual Machine), off-heap memory exists outside the JVM’s control. By shifting data storage to off-heap memory, the system reduced the frequency and intensity of GC events.
Some benefits of this approach were as follows:
Off-heap memory provided more space for storing restriction data without overloading the JVM heap.
This change reduced GC interruptions, resulting in smoother and more consistent system performance.
Hosts could handle larger datasets without increasing the risk of hitting memory limits.
2 - Venice and DaVinci Framework
To further optimize the system, the LinkedIn engineering team introduced DaVinci, an advanced client library, and integrated it with Venice, a scalable derived data platform.
Here’s how these tools work together:
DaVinci operates as an eager cache, meaning it proactively loads all restriction data into memory at the start. This eliminated the need for frequent on-demand lookups.
The restriction data was stored in bitset-like data structures, which are highly memory-efficient. These structures allowed the system to manage large datasets while minimizing memory footprint.
Venice, a distributed platform designed for derived data, enabled seamless integration and synchronization of restriction data. It allowed DaVinci to fetch and store data efficiently, ensuring high-speed performance even during periods of intense activity.
The innovations in the third-generation system addressed many of the limitations of its predecessors. A couple of benefits were as follows:
Faster Bootstrapping Processes: With DaVinci’s eager caching, restriction data was loaded into memory more quickly during server restarts or deployments. This reduced downtime and ensured the system could respond to restrictions almost immediately after initialization.
Greater Resilience: The system was better equipped to handle organic growth (more users and data) and adversarial data growth (spikes in restrictions due to malicious activity). By leveraging memory-efficient data structures and off-heap storage, the system could scale without running into performance bottlenecks.
Conclusion
From the early reliance on relational databases to the adoption of advanced NoSQL systems like Espresso, and the integration of cutting-edge frameworks like DaVinci and Venice, every stage of development of the restriction enforcement system showcased LinkedIn's focus on innovation.
However, this journey was not just about innovation. It was also guided by a clear set of principles such as:
Start Simple, Scale Thoughtfully: Early designs avoided unnecessary complexity, focusing on solving immediate problems in a straightforward manner.
Proactive Problem Identification: This approach allowed the engineering team to anticipate challenges, such as memory pressure or latency spikes, and address them with strategic solutions.
Collaboration Across Teams: Cross-team collaboration enhanced efficiency by promoting knowledge sharing and reducing redundant efforts.
Benchmarking and Testing: Time-bound experiments and performance benchmarks allowed the team to evaluate approaches quickly.
Continuous Improvement: Each generation of the restriction enforcement system is built upon the successes and shortcomings of the previous one.
References:
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by "ByteByteGo" <bytebytego@substack.com> - 11:34 - 21 Jan 2025 -
ENDS in 4 hours… (plus replay inside)
This is it – your LAST CHANCE to secure your FHL ticket and be entered into a drawing for your chance to win VIP Experience and hang w/ me at FHL
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Grab your FHL ticket here now before this chance ends in 4 hours →
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P.S. Don’t forget, you’re just one funnel away…
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by "Russell Brunson" <noreply@clickfunnelsnotifications.com> - 03:25 - 21 Jan 2025 -
How leaders can start to strengthen global cooperation
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:27 - 21 Jan 2025 -
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by "EMMA Garside" <garsideemma08@gmail.com> - 09:37 - 20 Jan 2025 -
RE : Seeking for Indonesia agent
Dear agent,
Greeting from Cara and GLA family. Trust you are doing great.
I sincerely invite you to join GLA platform and cover the increasing request from/to Indonesia from our GLA members.
Above 75% of GLA Members are first-class agents with wide range of primary resources. GLA fulfills all your needs for your business development and resources integration, in terms of finding reliable agents and getting huge volume of inquiries from/to your area.
Besides, We are planning to hold the 12th GLA Global Logistics Conference:
<![if !supportLists]>Ø <![endif]>Event Name: The 12th GLA Global Logistics Conference
<![if !supportLists]>Ø <![endif]>Host By: GLA Global Logistics Alliance
<![if !supportLists]>Ø <![endif]>Event Date: May 15 - 18, 2025 (4 days)
<![if !supportLists]>Ø <![endif]>Host City: Dubai, UAE
<![if !supportLists]>Ø <![endif]>Expected Attendee: 1500+
<![if !supportLists]>Ø <![endif]>From: 130+ countries
<![if !supportLists]>Ø <![endif]>1-on-1 Meeting: 51 times per pax
We expect you to join us! Please don’t miss this opportunity to attend the conference as a GLA member J
If you want to know more, please don’t hesitate to contact me.
Best regards,
Cara Chen
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Mobile:
(86) 190-7616-6926 (WhatsApp|Wechat)
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The 12th GLA Conference – Dubai, UAE on 15th - 18th May 2025 – click here for registration
<![if !supportLists]>Ø <![endif]>The 11th GLA Conference in Bangkok Thailand, online album
<![if !supportLists]>Ø <![endif]>The 10th GLA Conference in Dubai UAE, online album
<![if !supportLists]>Ø <![endif]>The 9th GLA Conference in Hainan China, online album
【Notice Agreement No 7】
7. GLA president reserves the right to cancel or reject membership or application.
company shall cease to be a member of GLA if:
a) the Member does not adhere to GLA terms and conditions
b) the Member gives notice of resignation in writing to the GLA.
c) No good reputation in the market.
d) Have bad debt records in the GLA platform or in the market
by "Cara Chen" <member66@glafamily.com> - 09:18 - 20 Jan 2025 -
Our New Coding Patterns YouTube channel
Our New Coding Patterns YouTube channel
Forwarded this email? Subscribe here for moreOur New Coding Patterns YouTube channel
BIG announcement: we’ve launched a new YouTube channel! – Coding Patterns
The first video is already live, and we'll aim to post new videos fortnightly.
This channel will be run by my colleague Shaun Gunawardane.
Our goal is to provide high-quality, entertaining explanations on coding patterns, and interview tips, without making you feel like you’re stuck in a boring lecture. Whether you’re prepping for a big coding interview, sharpening your problem-solving skills, or just looking for something fun to watch during your lunch break, this channel has you covered.
Here's a sneak peak into some titles you might see in the future:
- When BFS beats DFS
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- Never ignore problem constraints
- Why you never remember coding solutions
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 20 Jan 2025 -
A leader’s guide to the fastest-growing industries
Leading Off
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Welcome to the latest edition of Leading Off. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
The business landscape over the next 15 years will be transformed by industries that are more dynamic and growing faster than others. A McKinsey Global Institute report identifies 18 such sectors, or “arenas of competition,” that could gain a much larger share of the global economy by 2040. This wide-ranging group includes electric vehicles, AI software and services, video games, and drugs for obesity. “Understanding potential arenas is relevant for entrepreneurs and incumbent companies that want to compete directly in arenas, other companies that would be affected by the emergence of arenas, as well as investors looking to allocate capital to these industries,” say McKinsey’s Chris Bradley, Kweilin Ellingrud, Michael Birshan, and their coauthors. According to their analysis, these industries’ collective share of global GDP could jump from 4 percent today to 10 to 16 percent by 2040—and they could generate $29 trillion to $48 trillion in revenues, and $2 trillion to $6 trillion in profit, over the same period.
That’s the number of cities worldwide where shared autonomous vehicles (AVs) currently operate, including Beijing, Oslo, and San Francisco. McKinsey’s Ani Kelkar, Kersten Heineke, Martin Kellner, Philipp Kampshoff, and their coauthors note that while few AVs (one of the 18 arenas of competition) are on the road, even in cities where they’ve been deployed, we should expect to see more of them as they become more sophisticated. “For these vehicles to gain scale and expand into new locations, stakeholders cannot just strive for technological improvements; they must also focus on affordability, safety, accessibility, and sustainability while ensuring that all businesses along the value chain can profit,” the authors say.
That’s McKinsey senior partner Ryan Brukardt on the importance of the space economy (another arena of competition). In an episode of The McKinsey Podcast, Brukardt notes that satellite communications enable common activities such as ordering food online and using GPS navigation while driving. With costs to access space decreasing and space-related job creation increasing in many countries, Brukardt says that companies across sectors should include space in their strategies. “Space can really change the world even more than it already has,” he says.
Keeping up with the speed of technological innovation is a common need in fast-growing industries—and especially critical for video game makers whose products are most popular with younger consumers. In an interview with McKinsey senior partner Erik Roth, Electronic Arts chief strategy officer Mihir Vaidya explains that the technologies used to develop video game visuals are changing almost daily. “It would be like if Martin Scorsese had to relearn how to use a camera every time he went to make a new movie,” Vaidya says. In addition to tech innovations, the gaming industry is contending with changes to their business and consumption models as younger generations seek more choice and more connection with other gamers. “We think in terms of how the resources we deploy will enable bigger communities to have greater depth of engagement,” Vaidya says.
It’s hard to consider the future of industries without taking AI into account. Just this month at the CES 2025 tech conference, companies unveiled AI-powered innovations such as a memory-boosting app, a TV that allows viewers to isolate different audio sources, and a device that can read a book to kids using their parents’ voices. But AI’s reach goes far beyond cool electronics. For example, gen AI’s impact on the beauty industry could add $9 billion to $10 billion to the global economy. McKinsey’s Kristi Weaver, Megan Pacchia, Sara Hudson, and coauthors note that gen AI use cases in the beauty sector could include hyperpersonalized marketing, virtual try-ons, and rapid packaging and product development. “While much of the beauty industry’s products are cosmetic, gen AI applications in beauty are more than skin-deep,” they say. “Integrating the technology alongside other digital and AI tools and boosting organizational capabilities can differentiate leaders in beauty for years to come.”
Lead by competing for the future.
— Edited by Eric Quiñones, senior editor, New York
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Copyright © 2025 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007
by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:57 - 20 Jan 2025 -
[MS part 5 of 5] Your Path to Success
I’ve been telling you all about my Marketing Secrets trilogy this week.
If there’s ONE thing to take from it…
It’s that every single successful person has come up against the same challenges as you and then found a way to keep moving forwards.
Most of the time they discovered a ‘secret’ from someone who had gone before them.
And that secret changed everything…
Each book in the Marketing Secrets trilogy is packed full of secrets.
- DotCom Secrets teaches you to build profitable sales funnels
- Expert Secrets teaches you to hone your messaging
-
Traffic Secrets teaches you win new customers
Today I want to give you an offer to get ALL THREE BOOKS for an amazing price!
And one more thing.
Included in the bundle is a workbook called Unlock the Secrets which you will fill out as you go along.
Unlock the Secrets is your path to success.
You can keep coming back to it to stay focused on your offer, your message, your strategy and a whole bunch of other things that point the way to success.
If you want to grow your business…
If you want to take control of your own sales funnel…
If you want to change your life…
Then get this offer today!
You can buy the ‘Secrets Trilogy’ here.
I’m so excited to see how much success it will bring you.
Talk tomorrow,
Russell Brunson
© Etison LLC
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by "Russell Brunson" <newsletter@marketingsecrets.com> - 03:44 - 20 Jan 2025 - DotCom Secrets teaches you to build profitable sales funnels
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What’s on the agenda for Davos 2025?
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
Davos dialogues. The theme for the 2025 WEF meeting, “Collaboration for the Intelligent Age,” highlights the promise of technology and innovation to lift productivity and improve living standards amid increasing polarization. As a strategic partner of WEF, McKinsey has several Davos 2025 participants—senior partners Shelley Stewart III and Solveigh Hieronimus, among other experts—engaging delegates in constructive dialogues on the meeting’s five interconnected priorities.
•
Networking opportunities. With some 2,500 delegates and hundreds of others gathering in Davos—possibly making it the largest assembly of global decision makers all year—the opportunities for social interactions abound. “Davos is all about making those unusual connections,” says McKinsey senior partner Kate Smaje. “It’s often about those coincidental meetings that happen in the margins of the sessions.” From finding new sources of future growth to making progress on climate goals, consider the key priorities for Davos 2025.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:33 - 20 Jan 2025 -
Your chance to win a VIP ticket to FHL ends tomorrow!!
Call me Wonka… because there is a frenzy for this VIP “golden ticket” and rightly so, seeing as this opportunity is as RARE as they come.
This is the only chance RIGHT NOW, in the world, where you can meet and hang with me back-stage at FHL in Vegas!!
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But you have to act now!!! (So smack that link and grab your ticket before it’s too late…)
As a reminder, here’s everything that’s included with the VIP Golden Ticket experience:
Meet ME (Russell Brunson!!) backstage at FHL!! Join me backstage where we can chat, take pictures and get some autographs!
Get an exclusive behind-the-scenes look of the event!! Get the rare chance to see how the sausage is made – what it REALLY takes to put on a marketing event of this magnitude…
(Hint: it’s like launching a rocket while putting on a parade AND doing back-flips all at the same time…)
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Room UPGRADE to a SUITE* Spend the last ever Funnel Hacking Live in luxury and style in your very own hotel suite!! (*must have previously booked your standard room at Harrahs or the Linq)
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Mini event #2 – “Methodology That I Took From Varied Sources For MY ‘Success System’ That Never Fails”
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Before retiring, Dan typically charged upwards of $20k for a full day of consulting/mentoring…
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RESERVE YOUR FHL TICKETS NOW TO BE ENTERED IN THE DRAWING FOR FREE →
If you’ve already purchased your FHL 10 ticket, good news! You’ve already been entered into the drawing for the VIP Golden Ticket Experience. No need to take any additional steps!
For those who are still on the fence OR simply plan on buying their tickets last minute…
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GET YOUR FHL TICKETS NOW TO BE AUTOMATICALLY ENTERED FOR THE VIP GOLDEN TICKET EXPERIENCE!! →
See you backstage!
Russell Brunson
P.S. Don’t forget, you’re just one funnel away…
© Etison LLC
By reading this, you agree to all of the following: You understand this to be an expression of opinions and not professional advice. You are solely responsible for the use of any content and hold Etison LLC and all members and affiliates harmless in any event or claim.
If you purchase anything through a link in this email, you should assume that we have an affiliate relationship with the company providing the product or service that you purchase, and that we will be paid in some way. We recommend that you do your own independent research before purchasing anything.
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by "Russell Brunson" <noreply@clickfunnelsnotifications.com> - 06:13 - 19 Jan 2025
