What Snapchat Planets mean, how to see them, and what each planet says about your closest friends using Snapchat’s Friend Solar System.
Key Takeaways
Snapchat Planets visually rank your best friends.
Only Snapchat Plus users can access this feature.
Each planet corresponds to a friend’s closeness to you.
You can enable or disable this feature in your Snapchat settings.
Planet rankings change based on your interaction habits.
With the rise of Snapchat Plus, a new feature called “Snapchat Planets” has captured the curiosity of users worldwide. If you’ve ever wondered what these planets mean or how they relate to your Snapchat friends, you’re in the right place. In this blog, we’ll explain everything—from Snapchat planets’ meaning to how you can see them, and what each planet represents in your friend list.
As Snapchat continues to evolve, understanding new features like planets becomes essential. Whether you’re just exploring or already a Snapchat+ subscriber, learning what these icons symbolize will help you make the most of your social connections.
What Are Snapchat Planets?
Snapchat Planets are a unique feature introduced with Snapchat Plus that visually ranks your closest friends. Each planet corresponds to a friend based on how frequently you interact with them. This idea gamifies the social connection, allowing users to see their top friends orbiting like planets around their “sun.”
These planets offer a fun and visual way to interpret digital relationships. Instead of just listing names or using emojis, Snapchat uses an astronomical metaphor to illustrate your closest connections.
Each Snapchat Planet represents a specific rank on your Best Friends list. For example:
Mercury = Your #1 Best Friend
Venus = #2 Best Friend
Earth = #3
Mars = #4
Jupiter = #5
Saturn = #6
Uranus = #7
Neptune = #8
The planet icons usually show up with their own design and orbit around a Bitmoji sun (you).
How to See the Planets on Snapchat
To view Snapchat Planets, you need to be a Snapchat Plus subscriber. Once subscribed:
Go to your friend’s profile.
Tap on the “Best Friends” badge.
You’ll see a planet icon which indicates their friendship rank.
This feature is not visible to the friend—you alone can see your Snapchat Solar System.
Why Snapchat Planets Matter
Enhances the way you see and manage relationships
Reflects real-time friendship shifts
Encourages engagement
Useful for influencers tracking top fans or engagement
These Snapchat Plus planets explain how deeply you’re connected with your inner circle.
How to Check Your Snapchat Planets
Checking your Snapchat Planets is simple:
Open the Snapchat app and go to your friend’s profile.
If you’re subscribed to Snapchat+, you’ll find the Solar System feature active.
Tap the badge to reveal the assigned planet and see where your friend stands.
Friend Solar Systems Work Explained
Snapchat creates a unique “Solar System” for each user. You’re represented as the sun, and your closest eight friends are shown as planets orbiting you based on interaction levels.
Snapchat Decides Planet Assignments
The assignment of planets isn’t random. Snapchat uses interaction metrics such as snap frequency, chat history, and story engagement to determine which friend gets which planet.
Enable or Disable the Feature in Snapchat App
You can turn the Solar System feature on or off in your Snapchat settings. Simply:
Go to Settings.
Find Snapchat+ Features.
Toggle the Friend Solar System on or off.
Snapchat Planets List in Order
Here’s a breakdown of the planets from closest to farthest:
Rank
Planet
Symbolism
1
Mercury
Closest friend
2
Venus
Second best
3
Earth
Third closest
4
Mars
Fourth friend
5
Jupiter
Fifth connection
6
Saturn
Sixth bond
7
Uranus
Seventh friend
8
Neptune
Eighth in line
This order reflects your communication frequency and mutual interaction.
Snapchat Planets vs. Best Friends List
While both features represent social connections, Snapchat Planets offer a visual and fun alternative to the traditional best friends list. The Planets iconography adds another layer of interaction and emotional bonding between users.
What Do the Planets Mean for Friendships?
These planets aren’t just cute symbols—they’re visual cues for your real-time relationships. If someone is Mercury, you probably talk to them daily. Neptune? Not as often, but still in your top eight.
Is Snapchat Planets Worth It?
If you’re someone who values insights into your digital friendships, then Snapchat Planets is worth the small subscription. It adds both fun and data to your social life.
Why Are Snapchat Planets Important?
Snapchat Planets help users better understand their social habits. Whether you’re growing distant or becoming closer to someone, your planet position reflects that. For digital marketers and influencers, it’s a way to track high-engagement users.`
How to Get Planets on Snapchat Plus
You must subscribe to Snapchat Plus to access Planets. It’s a premium subscription that unlocks several other features:
Custom app icons
Rewatch indicator
Ghost trails
Story boost
Monthly and annual plans are available, with occasional regional discounts.
Real-Life Example from Reddit
A Reddit user shared their experience after subscribing to Snapchat Plus:
“At first, I thought the planet icons were just a gimmick, but I was surprised how accurate it felt. My Mercury really is my closest friend!”
These testimonials highlight how users are relating emotionally to the Planets feature.
Are Planets Based on Mutual Interaction?
Yes, while the ranking is primarily based on your interaction frequency with others, mutual interaction can also influence who appears in your planet system. Sending snaps back and forth, watching each other’s stories, and chatting regularly can all impact the planet rankings.
Can Planet Positions Change Over Time?
Absolutely. The Snapchat Planet order is dynamic and can shift depending on how often you interact with friends. If someone you snap often starts to fade and a new user becomes more active in your conversations, the planet ranking will adjust accordingly.
FAQs:
How can I view my planets on Snapchat?
To view your Snapchat Planets, you must be a Snapchat Plus subscriber. Once subscribed, go to any friend’s profile who’s among your top 8 best friends. If the Friend Solar System feature is enabled, you’ll notice a badge under their Bitmoji that you can tap. This reveals their corresponding planet icon, helping you understand their rank in your digital orbit. The closer the planet, the stronger the bond.
Are these planets visible to other users?
No. The Snapchat Planets feature is completely private. Only you can see the planet ranks in your solar system. Your friends cannot view their own planet position in your system unless they also have Snapchat+ and are checking their solar system. This privacy ensures you can enjoy the feature without worrying about making others feel left out or ranked.
Can the planet order change over time?
Yes — and that’s part of what makes the feature dynamic and fun. The planet positions are not fixed. They automatically adjust based on how frequently and mutually you interact with your friends. For example, if someone you used to snap every day becomes less active and another friend becomes more engaged, their planet rank will rise. This creates a living social graph that updates as your interactions evolve.
Snapchat uses multiple signals such as:
Snap sending and receiving frequency
Chat durations
Story views and reactions
Group activity and more
So if your communication patterns shift, your Friend Solar System will reflect it in near real-time.
Is Snapchat Plus worth it for this feature alone?
If you’re someone who enjoys tracking social trends, gets curious about who you interact with the most, or simply loves aesthetic features like Snapchat Planets, then yes—it may be worth the upgrade just for this.
Here’s why:
It gamifies friendships in a fun, visual way.
It offers insights you wouldn’t normally get from the default Best Friends list.
It gives you a sense of emotional proximity with friends.
You also get access to other Snapchat Plus features like Ghost Trails, Story Rewatch Indicator, and custom icons.
For casual users, it may not be essential. But for highly social users, influencers, or digital connectors, it adds a new dimension to how you experience relationships on the platform.
Machine learning (ML) projects are becoming increasingly complex, requiring seamless coordination across experimentation, deployment, and monitoring phases. For businesses and data scientists, this complexity often leads to reproducibility issues and inefficient workflows. This is where MLflow on GCP AI Platform comes into play.
In this guide, we explore how to effectively integrate MLflow with Google Cloud Platform’s AI services, boosting productivity, collaboration, and model lifecycle management. We also unpack essential tools and best practices to optimize your cloud-based ML pipeline.
It’s built to scale, allowing you to deploy and manage ML workflows with speed and reliability.
Why Use MLflow with GCP?
Integrating MLflow with GCP provides the best of both worlds: flexible experiment tracking from MLflow and scalable infrastructure from Google Cloud. Benefits include:
Centralized experiment logging across your team
Cost-effective, scalable training and deployment
Tight integration with Vertex AI pipelines
Setting Up MLflow on Google Cloud
Here’s how to get started:
Create a GCP Project: Set up billing and enable AI Platform APIs.
Provision Cloud Storage: Use it for MLflow artifact storage.
Use Cloud SQL or BigQuery: For storing MLflow tracking metadata.
Deploy MLflow Tracking Server: Use Google Kubernetes Engine (GKE) or Vertex AI Workbench.
Configure Authentication: Use IAM roles and service accounts.
Tip: Containerize MLflow for seamless deployment using Cloud Run or GKE.
Core Features of MLflow in GCP Integration
Experiment Tracking: Track parameters, metrics, and artifacts.
Automated Pipelines: Connect MLflow experiments to Vertex AI pipelines.
Version Control: Tag and register models in the MLflow Model Registry.
Collaboration: Share experiment results via BigQuery dashboards.
Use Cases Across Industries
1. Healthcare: Model version control for diagnostic AI. 2. Finance: Real-time fraud detection pipelines. 3. Retail: Demand forecasting and dynamic pricing models. 4. Manufacturing: Predictive maintenance using sensor data.
Key Challenges and How to Overcome Them
Challenge 1: Authentication Errors
Solution: Use Google Cloud IAM and set up OAuth2 credentials.
Challenge 2: Artifact Storage Conflicts
Solution: Ensure Cloud Storage buckets have proper access roles and are regionally matched with MLflow deployment.
Challenge 3: Metadata Overload
Solution: Use BigQuery partitioning to handle large experiment logs efficiently.
Challenge 4: Pipeline Integration Gaps
Solution: Use Vertex AI’s SDK to connect MLflow-tracked experiments directly into pipeline components.
Example Project: Retail Forecasting with MLflow and GCP
Problem: A retail brand wants to predict weekly sales for its top-performing stores.
Solution Using MLflow + GCP:
Use MLflow Projects to version feature engineering and modeling scripts.
Store metrics and models in GCP Cloud Storage.
Visualize performance trends via BigQuery + Looker Studio.
Deploy final model with Vertex AI.
Result: 22% improvement in forecasting accuracy and reduced development time by 30%.
MLflow and GCP Best Practices
Use Cloud Build for CI/CD pipelines.
Schedule model retraining using Cloud Scheduler.
Leverage Vertex AI pipelines for orchestration.
Implement automated model validation before deployment.
Use IAM roles to ensure secure access to MLflow artifacts and metadata.
Common Questions Answered
How do I connect MLflow to Vertex AI?
Use the Vertex AI SDK to send outputs from MLflow-tracked experiments to a pipeline step.
What storage can I use for MLflow artifacts?
Google Cloud Storage buckets (with correct IAM policies).
Can I run MLflow in a GCP notebook?
Yes, deploy MLflow in a Vertex AI Workbench instance and connect via local host.
Is BigQuery better than Cloud SQL for MLflow tracking?
BigQuery is ideal for large-scale experiment logs. For small teams, Cloud SQL is sufficient.
How do I manage ML models after deployment?
Use MLflow’s model registry and Vertex AI’s model monitoring together.
Final Thoughts
By integrating MLflow with GCP, data teams can create a cohesive, cloud-native ML workflow. From experiment tracking to deployment and monitoring, the tools complement each other to support rapid, reliable, and reproducible machine learning development.
Use modern tools to improve investigations and graph drawing with AI, data mapping, and real-time visualization for better, faster insights
Key Takeaways
Graph tech simplifies insights, enhances accuracy, and speeds investigation.
Tools like Tableau and Gephi transform raw data into meaningful visual stories.
AI adds predictive power to investigations.
Start with your data needs, then match the right tool.
Whether you’re analyzing scientific experiments, financial anomalies, or crime patterns—accurate visual interpretation is essential. Gone are the days of hand-drawn charts and slow investigations. With AI, cloud-based dashboards, and graph analytics software, professionals can now process complex data visually, fast, and smart.
In this blog, we explore how to use tech to improve investigation and graph drawing, offering practical insights for scientists, educators, analysts, and law enforcement teams alike.
Case Study: Investigating Academic Misconduct Using Data Graphs
A university used Gephi to map suspicious exam similarities across departments. With tech, they found hidden patterns in grading and identified several coordinated cheating networks.
Result: A thorough investigation and tighter assessment protocols.
Takeaway: Graphs can do more than explain—they expose.
How AI Is Revolutionizing Data Investigation
Artificial intelligence boosts traditional methods by adding speed, intelligence, and prediction.
AI Tools in Action:
NLP to scan and extract key evidence from documents
Predictive analytics for fraud detection and risk analysis
Machine learning for behavior profiling
Common Questions About Tech in Investigations & Graph Drawing
1. How does tech improve investigation methods?
Smart tools offer speed, accuracy, and deeper insights through AI and data visualization.
2. What are the best digital graphing tools?
Power BI, Tableau, and Gephi offer dynamic and shareable visual charts.
3. Can AI help solve crimes or detect fraud?
Yes. AI can reveal patterns and anomalies that are hard to spot manually.
4. What role does graph drawing play in investigations?
It maps out relationships, sequences, and timelines for clarity and communication.
5. Which software should analysts start with?
Start with free tools like Google Charts or Microsoft Excel, then upgrade as needed.
Insight from Reddit: Real-World User Feedback
“We switched from spreadsheets to Power BI and it transformed our forensic accounting team. Graphs now expose fraud loops in seconds.” — Reddit user, r/ForensicAccounting
Key Stats to Know
91% of analysts report faster findings with visual tools
Visualization improves data comprehension by 40%
60% of law enforcement now use digital graphing for criminal cases
Choosing the Right Tool for Your Needs
Before jumping in, define your purpose:
Need complex network analysis? Try Gephi.
Want dashboards? Use Power BI or Tableau.
Need basic, free visuals? Start with Google Charts.
Check for integrations, export options, and team collaboration features.
Use Lucidchart to help students visualize experiments
Teach with Google Charts for entry-level data skills
Law Enforcement:
Adopt Palantir or IBM i2 for network investigation
Use data maps to visualize suspect connections
Business Analysts:
Create interactive dashboards with Power BI
Train teams in data storytelling and visual analytics
Conclusion
In every industry, using tech to improve investigation and graph drawing isn’t just smart—it’s essential. The right combination of tools empowers teams to uncover truth faster, communicate clearly, and act decisively.
Whether you’re a teacher, detective, or data analyst—visualization and AI tools give your work more power.
Explore Zoho’s Zia LLM launch featuring prebuilt agents, MCP, and a no-code custom agent builder to transform your AI workflow
The rise of AI in enterprise software continues to reshape how businesses operate, and Zoho is making serious moves in this space. With the announcement “Zoho launches Zia LLM and deepens AI”, the company has entered a new era of intelligent productivity and automation. As competition in AI intensifies, Zoho AI updates aim to make a significant impact through their Zia LLM for businesses initiative.
In this blog, we’ll explore how Zoho’s AI-powered features, especially the new Zia large language model, are being integrated across their productivity ecosystem. Whether you’re a small business owner using Zoho CRM AI updates or an enterprise leader looking into Zoho generative AI tools, this guide reveals how Zia is redefining intelligent business support.
We’ll also examine Zia’s natural language processing capabilities, how it boosts productivity, and what this means for users of Zoho productivity tools moving forward.
Zia LLM (Large Language Model) is Zoho’s in-house generative AI engine developed to understand and generate human-like language across Zoho’s suite of apps. It’s a bold move from the company to build its own model rather than relying entirely on third-party AI.
Zia is not new—it’s been around in Zoho CRM and other tools. But now, with the launch of the Zia LLM capabilities, it becomes smarter, faster, and much more intuitive.
Key Features of Zia LLM:
Contextual email and message generation
Smart data summaries in CRM and analytics
Enhanced chatbot interactions
Voice-enabled task automation
Zoho’s AI-Powered Features Now Go Deeper
Zoho’s investment in enterprise AI with Zoho is evident. From Zoho analytics with AI to AI in business applications, Zia LLM is no longer a side tool—it’s core to the Zoho experience.
Key areas where Zia is being used:
Zoho CRM: Predictive sales trends, email drafting, and lead scoring
Zoho Projects: Smart deadline suggestions and task classification
Zoho Desk: Auto-responses, sentiment analysis, and ticket summaries
Whether it’s writing reports or responding to customer queries, the Zia integration in Zoho apps means businesses save time and reduce human error.
Zia’s LLM Integration in CRM and Productivity Tools
If you use Zoho CRM, you’ve probably seen Zia before. But now, with Zia LLM, these features get a major upgrade:
Generate email responses based on previous conversation threads
Suggest next best actions using machine learning
Automatically categorize leads and detect buying intent
This seamless Zia’s LLM integration in CRM enhances user experience and allows sales teams to act faster.
How Zia’s Natural Language Processing Improves User Interactions
Natural Language Processing (NLP) is at the heart of Zia’s new capabilities. Users can:
Ask questions in plain English and receive contextual responses
Use voice commands to automate workflows
Search across Zoho apps more intuitively
This level of interaction is similar to what major LLMs like ChatGPT and Gemini offer, but tailor-made for Zoho users.
Zia LLM is Zoho’s proprietary large language model that powers smart features across its apps, improving content generation, automation, and decision-making.
2. How does Zoho use artificial intelligence?
Zoho integrates AI into its tools through Zia, enabling automation, analytics, natural language queries, and more.
3. Which Zoho apps support Zia LLM?
Zoho CRM, Zoho Desk, Zoho Projects, Zoho Mail, and Zoho Analytics currently support Zia LLM features.
4. Is Zoho Zia LLM free to use?
Zia’s basic features are available on most paid plans. Advanced generative AI tools may come with premium tiers.
5. How does Zia LLM improve business productivity?
By automating repetitive tasks, generating smart suggestions, and streamlining workflows across departments.
Case Study: How a Marketing Agency Transformed with Zoho Zia
A mid-sized digital marketing firm switched from traditional tools to Zoho Workplace and CRM. Within three months:
Response time to client queries dropped by 40%
Lead conversion rose by 27%
Content turnaround times reduced by half
The team cited Zia’s LLM integration in CRM and Zia AI performance as critical factors in their improved efficiency.
Pinterest Insight: Zia in the Creative Workflow
“Loving the new Zoho Zia tools. They’re helping us generate blog outlines, summarize emails, and even brainstorm hashtags faster!” — Pinterest User @MarketingProTools
This quote reflects how even creative teams are embracing Zia’s AI-powered automation.
The Future of AI in Zoho’s Ecosystem
Zoho is clearly aiming to become a major player in AI-powered business applications. With continued focus on privacy (Zoho doesn’t sell user data), the Zoho AI roadmap looks promising.
What’s coming next?
Expanded support for third-party LLMs via Zia’s interface
More voice-command features in mobile apps
Smarter analytics summaries using generative AI
Facts You Should Know
Zoho serves over 90 million users globally
Zia was first introduced in 2017 and has evolved rapidly since
Zoho built Zia LLM in-house, prioritizing data security
Zia is now integrated into over 10 core Zoho apps
Generative AI in CRM increases sales productivity by up to 33%
Key Takeaways
Zoho launches Zia LLM and deepens AI, signaling a powerful shift in enterprise AI tools
The LLM enhances automation, natural language search, and smart suggestions
Businesses benefit from time-saving features, better decision-making, and improved customer service
With continued updates, Zoho’s AI-powered features are making the platform more competitive
With Zoho Zia AI features improving rapidly, the company is setting a strong example for privacy-focused, integrated enterprise AI. Whether you’re using it in CRM, support, marketing, or analytics, the new Zia LLM capabilities promise better automation, insights, and productivity.
As AI continues to evolve, Zoho’s approach—developing in-house, focusing on user-centric tools, and expanding intelligently—positions it as a serious contender in the global AI race for business tools.
Exploring the real risks of cyberattacks on Europe’s energy infrastructure and how governments and businesses are preparing.
Table of Contents
How Hackers Could Access the Power Grid
Major Vulnerabilities in Europe’s Energy Network
Cyber Threats to Energy Sector: Real-World Case Studies
Questions Answered
How Europe is Strengthening Cyber Defenses
Reddit Insight: Public Concern Over Grid Attacks
Key Technologies at Risk: SCADA and Smart Grids
The Future of Cybersecurity in Energy
Key Takeaways
As Europe continues to digitize its energy infrastructure, the threat of cyberattacks has never been more pressing. With power grids managed by real-time monitoring systems and smart grid technology, even a single breach could lead to widespread electricity supply disruption.
This blog explores how hackers could take down Europe’s power supply, the cybersecurity in the energy sector, and what’s being done to prevent a blackout caused by digital sabotage.
Governments across Europe are now facing increasing pressure to address European power infrastructure risks and protect national energy security from digital threats.
3. Has Europe experienced power outages from cyberattacks?
Yes. Ukraine, a nearby European nation, faced major grid outages due to malware attacks.
4. What technologies do hackers exploit in the power sector?
SCADA, IoT, smart grids, and real-time grid monitoring systems are all common targets.
5. How is Europe protecting its power supply from hackers?
Through legislation, penetration testing, public-private cooperation, and AI-driven threat detection.
6. How Europe is Strengthening Cyber Defenses
European governments and businesses are actively investing in grid protection:
GDPR and NIS2 Directive: Enforce cybersecurity responsibilities.
National security agencies now work with energy companies on penetration testing.
Public-private partnerships focus on building AI-enhanced early warning systems.
Employee training programs on phishing, malware, and insider threats.
These steps aim to reduce the likelihood of grid blackout from cyberattacks.
7. Reddit Insight: Public Concern Over Grid Attacks
“We talk a lot about AI risks, but what if someone flips a switch on the grid and we’re back to candles?” — Reddit user, r/cybersecurity, 2025
Public awareness is rising as more citizens fear electricity outage from hackers. This fuels pressure on policymakers to act quickly.
8. Key Technologies at Risk: SCADA and Smart Grids
Smart grid systems offer efficiency but also introduce new vulnerabilities:
Smart meters can be manipulated for fraud or shutdown.
SCADA systems were not originally built with internet exposure in mind.
IoT devices often have weak password protection.
These flaws allow hackers to launch ransomware attacks on utilities or hijack power delivery remotely.
9. The Future of Cybersecurity in Energy
Cyber defense must evolve with threats:
AI-based anomaly detection will help flag abnormal grid behavior.
Zero-trust frameworks will limit internal breach risks.
Governments will enforce stricter compliance reporting for energy companies.
Long-term success requires global coordination to prevent cyber warfare on infrastructure.
10. Key Takeaways
Europe’s power grid is increasingly digital—and vulnerable.
Hackers can access systems through phishing, malware, and remote exploits.
SCADA and smart grid systems present the biggest risks.
Governments are investing in AI, regulation, and training to prevent attacks.
Citizens and companies must remain alert as threats grow.
Conclusion:
The question isn’t if but when a cyberattack will target Europe’s power supply. Preparing with the right security layers today is essential to avoid chaos tomorrow.
