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A More Personal Google Discover Is Finally Taking Shape
Google Discover has always promised to bring the internet to you before you even search for something. Open the Google app, scroll through the feed, and an endless stream of articles, videos, recommendations, products, and news appears based on what Google thinks you care about.
But there has always been a frustrating gap between personalization and real control.
Google knows what you search for. It knows which articles you open, which topics you interact with, and, when your accounts and settings allow it, activity across services such as YouTube can contribute to its understanding of your interests. Yet the user has traditionally had only limited ways to explain what they actually want.
That is now changing.
Google is introducing a more conversational approach to Discover personalization that could make the feed feel less like an algorithm guessing your interests and more like an assistant listening to your instructions.
Google Is Giving Discover a Voice
The biggest change is a new chat-based personalization feature that lets users describe what they want to see in natural language.
Instead of repeatedly pressing buttons such as “Not interested” or blocking individual publishers, users will be able to explain their preferences directly.
You could tell Google:
“Show me long-form smart home technology analysis, but avoid product announcements and press releases.”
Or:
“Show me Pixel 11 Pro XL setup guides, hidden features, and troubleshooting tutorials.”
The important difference is that these are not simple yes-or-no preferences.
They are instructions.
Three Major Personalization Improvements
Google’s latest Discover changes are not limited to the conversational feature.
The company is also introducing a Preferred Sources button that publishers can embed on their websites. This allows readers to follow a publication directly without leaving the page.
Google is also expanding personalized audio experiences in Google News, allowing users to create daily audio briefings around subjects that interest them.
But the Discover chat functionality may ultimately have the biggest impact on everyday users because it changes the relationship between the user and the recommendation algorithm.
Discover Has Always Been Heavily Algorithmic
Google Discover does not operate like a traditional news website.
There is no simple chronological list of everything published by your favorite websites.
Instead, Discover attempts to predict what you might find interesting.
Your searches, browsing behavior, interactions with content, and other signals can influence what appears in the feed. The result can sometimes feel surprisingly accurate.
But algorithmic accuracy does not mean perfect accuracy.
When Personalization Gets It Wrong
Most Discover users have probably experienced the same strange moment.
You read one article about a particular subject, and suddenly your entire feed becomes flooded with it.
Search for a new phone once, and suddenly you are apparently a professional smartphone reviewer.
Watch one video about a car, and your feed starts behaving as though you are preparing to buy ten vehicles.
Algorithms are extremely good at identifying patterns.
They are not always good at understanding why you showed interest in something.
The Missing Context Was the Real Problem
Imagine searching for information about electric vehicles because you are writing an article.
Google may interpret that activity as evidence that you want more electric vehicle content.
But perhaps you only wanted to research the subject once.
The algorithm sees an interaction.
The human understands the context.
That difference has always been one of the biggest weaknesses of recommendation systems.
Natural Language Could Change That
Google’s new approach attempts to close that gap by allowing users to describe their preferences directly.
Instead of forcing the algorithm to infer everything from behavior, users can provide additional context.
You can effectively say:
I like this, but not that.
That distinction is incredibly important.
Users Can Define Positive Preferences
The system can potentially be used to tell Discover what you actively want.
For example, a technology enthusiast could request:
Cybersecurity research
Long-form AI analysis
Linux tutorials
Smartphone troubleshooting
Hardware benchmarks
Privacy investigations
Detailed technical explainers
Instead of simply asking for “technology,” the user can describe a much narrower information diet.
Users Can Define Negative Preferences
The other half of personalization may be even more valuable.
Users can tell Discover what they do not want.
Someone interested in smartphones may want technical guides but have no interest in launch rumors.
Someone interested in artificial intelligence may want research papers and security analysis but not corporate press releases.
Someone following cybersecurity may want vulnerability analysis but not endless generic articles repeating the same announcement.
This is where natural-language personalization becomes particularly interesting.
Discover Could Become More Like a Personal Information Filter
The larger idea is not simply better recommendations.
It is the possibility of turning Discover into a personal information filter.
Instead of Google asking:
What does this person usually click?
the system can increasingly ask:
“What has this person explicitly told us they want?”
That is a meaningful philosophical shift.
Google Says Changes Can Happen Immediately
According to the announcement, Discover will adjust the feed after the user provides instructions and remember those requests.
That immediate response is important.
Personalization becomes much less useful when users have to wait days for an algorithm to understand a preference.
If someone tells Discover that they no longer want smartphone launch rumors, they expect to see a difference quickly.
Memory Makes the Feature More Powerful
The fact that Google says the requests will be remembered makes the feature substantially more interesting.
A one-time conversational instruction is useful.
A persistent preference is much more powerful.
It means users can establish an information profile without repeatedly correcting the feed.
But Memory Also Creates New Questions
The same capability that makes the system convenient raises privacy and transparency questions.
What exactly is Google storing?
How long are these preferences retained?
Can users review the instructions they have given?
Can they delete individual preferences?
Can users reset their Discover personalization completely?
These questions become increasingly important as recommendation systems become more conversational.
Discover Is Moving Toward AI-Driven Personalization
The timing is significant.
Google is integrating conversational AI into more parts of its ecosystem, and Discover is another natural place for that technology to appear.
Instead of presenting personalization as a collection of buttons and menus, Google is making it conversational.
This is exactly the kind of interaction people have become accustomed to with modern AI assistants.
A Better Example: The Technology Reader
Imagine someone who follows technology but has very specific interests.
They might tell Discover:
“Show me deep cybersecurity research, operating system vulnerabilities, AI security, Linux development, and hardware security. Prioritize technical analysis and original research. Avoid recycled press releases, celebrity technology stories, and generic product announcements.”
That is dramatically more expressive than simply tapping “interested” on individual articles.
The Difference Between Keywords and Intent
Traditional personalization often depends heavily on behavioral signals and topic classifications.
Natural-language instructions can communicate something closer to intent.
There is a major difference between saying:
I like Apple.
and:
“Show me Apple security updates and operating-system research, but skip rumors about unreleased products.”
The second statement contains context, priorities, and exclusions.
That is exactly what recommendation systems have historically struggled to understand.
The Preferred Sources Button Adds Another Layer
Google’s Preferred Sources feature tackles a different problem.
Sometimes users already know where they want information to come from.
Rather than relying entirely on an algorithm to determine which publication deserves attention, users can explicitly follow a publisher.
This gives users another mechanism for controlling the information entering their feed.
Publishers Benefit Too
The feature could also be important for publishers.
If readers can follow a publication directly from its website, publishers have another way to establish a relationship with their audience.
That matters in an internet increasingly dominated by recommendation algorithms.
A publication may produce excellent journalism, but if an algorithm does not consistently surface it, many readers may never see it.
Preferred Sources could help bridge that gap.
Google News Audio Is Part of the Same Strategy
The customizable daily audio briefing also fits into Google’s broader effort to make news consumption more personalized.
Instead of simply presenting headlines, Google can package information around topics that matter to an individual user.
This reflects a broader technology trend.
People increasingly expect software to organize information for them rather than forcing them to search manually.
Convenience Is Becoming the New Interface
The bigger story here is not just Discover.
It is the transformation of the interface itself.
For years, customization meant navigating menus.
Now customization increasingly means talking to software.
That change is happening across search, operating systems, smartphones, assistants, productivity software, and recommendation platforms.
Discover is simply another place where this shift is becoming visible.
The Feature Could Be Especially Useful for Researchers
People who constantly research specific subjects could benefit considerably.
A user following cybersecurity, for example, may want:
CVE disclosures
Exploit research
Malware analysis
Security advisories
Incident reports
Reverse-engineering content
Technical vulnerability analysis
But they may not want general consumer technology news.
Being able to communicate that distinction could dramatically improve the signal-to-noise ratio.
The Signal-to-Noise Problem Is Getting Worse
The internet contains more information than any individual can realistically consume.
That creates an uncomfortable paradox.
We have access to more information than ever, yet finding the right information can be increasingly difficult.
Recommendation engines were supposed to solve that problem.
Instead, they sometimes create another one: information overload personalized around our previous behavior.
Personalization Is Not the Same as Relevance
A personalized feed can still be irrelevant.
If you clicked one article about cryptocurrency, the algorithm may reasonably conclude that cryptocurrency interests you.
But reasonable does not necessarily mean correct.
A conversational system gives the user an opportunity to correct that assumption directly.
This Could Reduce Algorithmic Guesswork
The most interesting potential benefit is that explicit preferences could reduce the amount of guessing required.
The more information Google receives directly from the user, the less it has to infer from individual clicks.
That does not mean behavioral signals disappear.
It means explicit instructions could become another layer of context.
There Is Still a Risk of Over-Personalization
There is also a downside.
If Discover becomes extremely good at showing exactly what users already like, people could end up seeing fewer unexpected ideas.
That can create an information bubble.
Sometimes discovering something outside your normal interests is valuable.
A perfectly personalized feed could accidentally become a perfectly comfortable one.
The Discovery Problem
The word Discover is important.
A feed should not only deliver what users already know they like.
It should also occasionally introduce something surprising.
The challenge for Google will be balancing control with serendipity.
Give users too little control, and the feed becomes frustrating.
Give them too much control, and the feed could become predictable.
Personalization Needs an Escape Hatch
A good system should therefore allow users to say both:
Show me more of this.
and:
Surprise me.
The second option could become just as important as the first.
An intelligent Discover system should understand when to respect strict preferences and when to introduce something adjacent and potentially useful.
The Android Ecosystem Becomes More Intelligent
The Discover changes also fit into
Android is increasingly becoming an environment where AI sits between users and information.
Search, notifications, recommendations, assistants, summaries, photos, email, and productivity tools are all becoming more intelligent.
Discover is another piece of that ecosystem.
Google Is Turning Personalization Into Conversation
That may ultimately be the most important takeaway.
Instead of asking users to understand how the algorithm works, Google is allowing them to explain what they want in ordinary language.
That is a much more human interface.
Most people do not think in terms of recommendation parameters.
They think in sentences.
I want this, but not that.
The Rollout Will Matter
Google says the Discover chat personalization feature is arriving in the Google app in the coming days.
As with many Google features, availability may vary by account, platform, region, or rollout stage.
So even after the announcement, not every user should expect to see the feature immediately.
What This Means for Everyday Users
For ordinary users, the biggest benefit could simply be less frustration.
Instead of repeatedly dismissing irrelevant stories, users may eventually be able to describe their preferences once and allow Discover to adapt.
That could turn a feed that feels noisy into one that feels genuinely useful.
What This Means for
For Google, the feature represents something bigger than a Discover improvement.
It demonstrates how conversational AI can be used to control traditional algorithmic systems.
The AI does not necessarily need to generate the content.
It can simply become the interface through which humans control the recommendation engine.
That may prove to be one of the most practical uses of AI.
Deep Analysis
The Technical Architecture Behind the Idea
At a high level, conversational Discover personalization can be understood as another layer placed above Google’s existing recommendation infrastructure.
The traditional system collects signals, assigns relevance, ranks content, and displays recommendations.
The conversational layer introduces explicit user instructions into that process.
Behavioral Signals
Existing recommendation systems can use signals such as searches, interactions, subscriptions, content engagement, and topic affinity.
These signals help construct a continuously changing user-interest profile.
The new system potentially adds explicit instructions to that profile.
Natural-Language Preferences
A sentence such as:
Show me Linux security research but skip product announcements
cannot simply be treated as one keyword.
It contains multiple semantic instructions.
The system needs to identify:
Topic = Linux security
Preference = increase
Content type = research
Exclusion = product announcements
That is a much richer representation than a simple “like” button.
A Simplified Preference Model
Conceptually, a recommendation system could represent preferences like this:
INTERESTS
+ Linux security
+ vulnerability research
+ technical analysis
EXCLUSIONS
– product announcements
– generic press releases
CONTENT DEPTH
+ long-form
PRIORITY
+ original research
This is not
Developers Can Inspect the Google App Package
Android users with ADB access can inspect installed packages with:
adb shell pm list packages | grep google
The Google app package is commonly associated with:
com.google.android.googlequicksearchbox
This can help advanced Android users identify the installed Google Search application.
Checking the Package
A more targeted command is:
adb shell pm path com.google.android.googlequicksearchbox
If the package exists, Android will return the installed APK path.
This does not enable Discover personalization by itself. It is simply a useful diagnostic command for understanding the Google app installation.
Launching the Google App
ADB can also launch the Google application using:
adb shell monkey -p com.google.android.googlequicksearchbox 1
Again, this is an Android diagnostic technique rather than an official method for activating the new Discover feature.
Why Commands Matter Here
The important technical point is that the feature itself is not really about a hidden Android command.
It is about natural-language preference translation.
Google is building a human-friendly control layer over an extremely complicated recommendation engine.
Recommendation Systems Are Ranking Engines
A modern feed has to process enormous quantities of content.
For each user, the system has to estimate which stories are likely to be relevant.
Conceptually, ranking might resemble:
score =
topic_relevance
+ user_interest
+ freshness
+ source_quality
+ engagement_probability
– negative_preferences
The real system is vastly more complicated, but the principle is useful.
The new conversational interface could influence several of these variables.
Explicit Preferences Could Become High-Value Signals
A direct instruction may be more informative than an accidental click.
If a user says:
Avoid smartphone rumors.
that statement communicates a clear preference.
A click on one smartphone rumor, however, could have many explanations.
Maybe the user disagreed with it.
Maybe they were researching it.
Maybe they accidentally opened it.
Intent is difficult to infer from behavior.
AI Could Become the Translator
The conversational interface can potentially translate ordinary language into structured preference signals.
The user does not need to know anything about machine learning.
They simply describe what they want.
The AI handles the interpretation.
That is where conversational AI becomes practically useful rather than merely entertaining.
The Hardest Problem Is Ambiguity
Natural language is powerful because it is flexible.
It is also difficult because it is ambiguous.
Consider:
Show me AI news but skip the hype.
What counts as hype?
That decision requires interpretation.
Another user might define “AI news” as research papers.
Someone else might mean product launches.
Someone else might want investment news.
The system therefore has to make judgment calls.
User Feedback Will Become Critical
The best recommendation systems will probably combine explicit instructions with continued feedback.
If the system interprets a request incorrectly, users need an easy way to correct it.
That creates a feedback loop:
User instruction
↓
AI interpretation
↓
Feed adjustment
↓
User feedback
↓
Preference refinement
The quality of that loop will determine whether the feature actually feels intelligent.
Personalization Could Become More Granular
Today, users often control recommendations at the article or topic level.
Tomorrow, they may be able to control them at much finer levels.
For example:
Topic → AI security
Format → Long-form
Source → Research organizations Depth → Advanced Frequency → High Exclusions → Product marketing
That is much closer to building a personal information dashboard.
The Privacy Layer Cannot Be Ignored
The more detailed a preference profile becomes, the more valuable that profile becomes.
A record showing that someone wants cybersecurity research is relatively simple.
A profile containing dozens of highly specific interests can reveal considerably more about a person’s intellectual habits and activities.
That makes transparency important.
Users Need Visibility
A mature implementation should ideally allow users to see:
What Google thinks I like
What I explicitly requested
What I blocked
What sources I follow
What data influences Discover
Without visibility, personalization can feel mysterious.
With visibility, users can actually manage it.
Users Need Deletion Controls
Any long-term personalization system should also provide a straightforward way to delete individual instructions.
A preference from six months ago may no longer represent the user’s interests.
People change.
Their interests change.
Their jobs change.
Their devices change.
Their information needs change.
The Algorithm Should Understand Temporary Intent
One of the most interesting future possibilities would be temporary instructions.
For example:
For the next week, show me coverage of AI security conferences.
That is very different from:
I permanently want AI security content.
If conversational personalization eventually understands duration, it could become dramatically more useful.
Time-Based Preferences Could Solve Many Problems
Imagine saying:
Show me smartphone launch coverage until September 1.
After that date, the preference disappears automatically.
This would prevent temporary interests from permanently distorting a user’s feed.
Discover Could Become a Personal Research Assistant
At its most advanced, Discover could move beyond passive recommendations.
It could become a continuously updated research stream.
A user could define an information mission:
Track Linux kernel security,
AI agent vulnerabilities,
cloud security incidents,
and major zero-day disclosures.
The system could then continuously curate material around those subjects.
That Would Change How People Consume News
Instead of opening dozens of websites every morning, users could increasingly rely on personalized information streams.
This is convenient.
But it also makes the recommendation layer incredibly influential.
Whoever controls the filter controls much of what the user sees.
Source Diversity Will Matter
A truly useful personalized system should not simply provide ten versions of the same story.
It should recognize when multiple sources are reporting the same event.
It should ideally prioritize original reporting and meaningful analysis.
Otherwise personalization simply produces repetition faster.
The Best Feed May Not Be the Most Personalized Feed
This is the central tension.
A good information feed should contain:
Relevance + quality + diversity + surprise.
Remove relevance, and it becomes noisy.
Remove quality, and misinformation can dominate.
Remove diversity, and the user becomes trapped in a narrow information bubble.
Remove surprise, and Discover stops being discovery.
Google Has an Opportunity Here
The conversational Discover system could become much more than another AI feature.
It could become a genuine control mechanism for the modern information environment.
But that will depend on how accurately Google interprets requests and how much transparency it provides.
The User Finally Gets a Seat at the Table
For years, recommendation algorithms have quietly decided what millions of people see.
Now Google is experimenting with a more direct conversation between the user and the algorithm.
That is a small interface change with potentially enormous implications.
What Undercode Say:
The Real Story Is Control
The most important part of this announcement is not the chat box.
It is the fact that Google is giving users a more direct mechanism for influencing an algorithm that has historically operated mostly through inferred behavior.
Explicit Intent Is More Valuable
A user saying what they want can be more meaningful than a dozen accidental clicks.
That is why natural-language personalization has so much potential.
Discover Has Always Had a Guessing Problem
Google has enormous amounts of behavioral data, but behavioral data does not always reveal intent.
A click is evidence.
It is not an explanation.
AI Can Bridge That Gap
Conversational AI gives Google a mechanism for converting human explanations into machine-readable preferences.
That could significantly improve recommendation quality.
The User Experience Could Become Simpler
Instead of hunting through menus, users can simply explain what bothers them.
That is a major usability improvement.
Negative Preferences Matter Just as Much
People often know what they dislike more clearly than what they like.
“Don’t show me rumors” can be more useful than “show me technology.”
The Feature Could Help Heavy Information Consumers
Researchers, developers, journalists, analysts, students, and technology enthusiasts may benefit disproportionately.
They often need narrow information streams rather than generic topic feeds.
Publishers Could Gain More Direct Relationships
Preferred Sources could help users intentionally follow publications instead of depending entirely on algorithmic discovery.
That could strengthen publisher loyalty.
But Algorithmic Power Is Still Centralized
Users may have more control without actually controlling the underlying system.
Google still determines how preferences are interpreted.
That distinction matters.
Transparency Will Determine Trust
If users cannot see why their feed changed, frustration will return.
Personalization must be explainable enough to remain trustworthy.
Persistent Memory Is Both Useful and Sensitive
Remembering preferences makes the feature powerful.
It also creates another layer of user data that deserves careful handling.
Google Needs Good Reset Controls
Users should be able to undo preferences easily.
An incorrect instruction should not permanently contaminate a feed.
Temporary Interests Should Be Supported
Not every interest is permanent.
The ideal system should understand short-term research projects and temporary obsessions.
Discover Should Preserve Serendipity
The goal should not be an internet completely filtered around existing preferences.
Unexpected discoveries can be valuable.
Diversity Is a Feature
A good personalized feed should sometimes challenge assumptions and introduce adjacent viewpoints.
Personalization should not become intellectual isolation.
Repetition Is Another Hidden Problem
If every publication repeats the same news, a highly personalized feed can still feel terrible.
Google needs to recognize duplicated stories and prioritize unique information.
Source Quality Should Matter
Personalization without quality control could simply make low-quality content more visible.
The algorithm needs to understand the difference between relevance and credibility.
Natural Language Is Not Perfect
People use sarcasm, ambiguity, shorthand, and contradictory instructions.
The system will inevitably misunderstand some requests.
Feedback Must Be Easy
If Google gets something wrong, correcting it should take seconds.
That is where conversational interfaces have a major advantage.
The Interface Should Explain Changes
Users should ideally understand why certain content appeared or disappeared.
A little transparency can prevent a lot of confusion.
Discover Could Become More Like an Information OS
Google already sits between users and enormous amounts of information.
A conversational Discover layer could turn that relationship into something resembling a personal information operating system.
This Is Bigger Than a Feed Feature
The same concept could eventually influence search results, Google News, YouTube recommendations, Chrome content, and other Google services.
The feed may be the testing ground.
AI Does Not Always Need to Generate Content
This is an important distinction.
AI can provide enormous value simply by organizing information.
Personalization may be one of the most useful non-generative applications of AI.
The Recommendation War Is Changing
Companies have spent years competing over who can recommend content most accurately.
The next stage may be about who can allow users to control recommendations most naturally.
Google’s Advantage Is Scale
Google already has massive search and content infrastructure.
If conversational personalization works, the company can potentially deploy the concept across a huge ecosystem.
But Scale Magnifies Mistakes
A small personalization error affecting one person is annoying.
A systematic bias affecting millions becomes much more serious.
That makes testing and transparency essential.
Users Should Remain in Control
The ideal system should never make personalization impossible to escape.
Users need the ability to reset, modify, inspect, and override their preferences.
The Best Algorithm May Be a Collaborative One
The future could involve a continuous collaboration between the algorithm and the user.
The algorithm recommends.
The user corrects.
The system learns.
The cycle repeats.
Discover Could Finally Feel Personal
Google has called Discover a personalized feed for years.
This feature moves that idea closer to its literal meaning.
Instead of personalization being something Google silently does in the background, users can increasingly participate in the process.
The Biggest Question Is What Comes Next
Today’s feature allows users to describe what they want.
Tomorrow’s system could potentially understand priorities, duration, quality, sources, depth, and context.
That is when Discover could become genuinely powerful.
Undercode’s Bottom Line
Google is not simply adding another button to Discover.
It is experimenting with a new relationship between people and recommendation algorithms.
If Google gets the balance right, users could spend less time fighting irrelevant content and more time discovering information that actually matters.
The real victory will come when personalization does not merely predict what we want, but gives us the ability to clearly explain it.
✅ Google Is Introducing New Discover Personalization Controls
The supplied article accurately describes
✅ Users Can Describe What They Want in Natural Language
The central feature described is a conversational mechanism that allows users to provide more detailed instructions about the subjects and types of content they want to see. This represents a significant expansion beyond traditional “Not interested” controls.
✅ Google Says Preferences Can Be Remembered
The article states that Discover will adjust after the user’s request and remember the preference. That makes the feature more powerful than a one-time feedback button because it can influence future recommendations.
⚠️ The Feature Does Not Mean Users Completely Control the Algorithm
Natural-language instructions provide additional control, but they do not give users direct access to Google’s underlying ranking system. Google still determines how requests are interpreted and how content is ultimately ranked.
⚠️ Availability May Vary
The announcement says the feature is arriving in the Google app in the coming days. That does not guarantee immediate availability for every Android user, account, country, or app version.
Prediction
(+1) Discover Will Become Much More Conversational
Google is likely to expand natural-language controls beyond basic topic preferences. Users could eventually specify sources, content depth, formats, frequency, and temporary interests through ordinary conversation.
(+1) Explicit Preferences Will Become More Important
Recommendation systems are likely to increasingly combine behavioral data with instructions users intentionally provide. This could reduce some of the frustrating guessing that occurs when algorithms interpret isolated clicks.
(+1) Publishers Will Fight Harder for Preferred-Source Status
If direct publisher following becomes more prominent, established technology, news, and specialist publications will have another reason to encourage readers to mark them as preferred sources.
(+1) AI Will Become the Interface for Personalization
Instead of navigating complicated settings, users will increasingly tell applications what they want in plain language. Google Discover could become an early example of a much broader interface shift.
(-1) Personalization Could Increase Information Bubbles
If users make their feeds extremely narrow, Discover could show fewer unexpected subjects and perspectives. Google will need to preserve enough diversity to prevent personalization from becoming isolation.
(-1) Privacy Questions Will Become More Important
The more detailed a user’s preference history becomes, the more important transparency, deletion controls, and clear explanations of personalization will become.
(+1) The Biggest Future Upgrade Could Be Temporary Preferences
One of the most useful developments would be instructions such as “show me this for the next week.” That would allow users to follow temporary events without permanently changing their recommendation profile.
(+1) Discover Could Evolve Into a Personal Research Engine
If Google combines conversational preferences, source controls, topic tracking, and high-quality ranking, Discover could eventually become less like a generic content feed and more like an always-updated personal research assistant.
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References:
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