There is a strange thing about modern technology.
We spend enormous amounts of time talking about databases, APIs, microservices, cloud computing, artificial intelligence, operating systems, containers, networks, recommendation engines, social media platforms, and distributed systems.
But underneath many of these systems is a much simpler idea.
Things are connected.
A user connects to an account.
An account connects to transactions.
A transaction connects to a bank account.
A product connects to a category.
A person connects to other people.
A web page connects to other web pages.
A computer connects to other computers.
A service connects to another service.
A piece of code depends on another piece of code.
A city connects to another city through roads.
A disease can spread from one person to another.
A packet travels from one machine to another.
A recommendation engine says:
“You are probably interested in this because of the things you are already connected to.”
This is the world of graphs.
And graphs are everywhere.
Not just in computer science textbooks.
Not just in algorithms courses.
Not just in graph databases.
Graphs are quietly becoming one of the most powerful ways of thinking about modern technology.
When you understand graphs, you begin to see software differently.
You stop seeing applications as collections of screens.
You start seeing them as networks of relationships.
You stop seeing an API as a collection of endpoints.
You start seeing it as a graph of capabilities and dependencies.
You stop seeing a database as tables.
You start seeing entities connected by meaning.
You stop seeing social media as posts.
You start seeing a massive dynamic graph of humans, content, interests, interactions, communities, and influence.
You stop seeing the internet as a collection of websites.
You start seeing a gigantic graph.
That shift in perspective is powerful.
Because sometimes the deepest abstraction in technology isn't the object.
It is the relationship between objects.
1. The Basic Idea: Everything Is Connected
A graph is conceptually simple.
You have nodes and edges.
A node represents something.
An edge represents a relationship.
For example:
Alice ---- friends with ----> Bob
|
|
works at
|
v
Company X
Alice and Bob are nodes.
The friendship is an edge.
Company X is another node.
Alice's employment relationship is another edge.
That's basically it.
But this simple abstraction scales extraordinarily well.
Consider an e-commerce application.
You might have:
User
|
| purchased
v
Product
|
| belongs to
v
Category
|
| similar to
v
Product
Suddenly, recommendations become a graph problem.
If Alice purchased a laptop, and Bob purchased the same laptop plus a particular keyboard, the system might discover:
Alice
|
| purchased
v
Laptop
|
| purchased by
v
Bob
|
| purchased
v
Keyboard
The system can infer:
Alice might be interested in the keyboard.
That is graph reasoning.
The interesting part isn't necessarily the objects.
The interesting part is the connections.
2. Databases Traditionally Teach Us to Think in Tables
For decades, relational databases trained developers to think in tables.
You might have:
users
----------------
id
name
email
Then:
orders
----------------
id
user_id
total
And:
products
----------------
id
name
price
Relationships are represented using foreign keys.
For example:
orders.user_id -> users.id
This is extremely powerful.
But something interesting happens when applications become more connected.
Suppose your system contains:
- users
- organizations
- projects
- repositories
- commits
- teams
- permissions
- devices
- locations
- subscriptions
- payments
- recommendations
- social connections
Now relationships become increasingly important.
The data isn't merely:
“What is this object?”
It becomes:
“What is this object connected to?”
That question can be much more interesting.
Imagine a cybersecurity platform.
A security analyst might ask:
Which users are connected to machines that accessed a suspicious domain through accounts associated with a compromised organization?
That's not simply a lookup.
That's a traversal.
Conceptually:
User
↓
Account
↓
Machine
↓
Network connection
↓
Domain
↓
Threat
The value comes from following the edges.
This is why graphs become so useful as systems become more interconnected.
3. The Internet Itself Is a Graph
Perhaps the most obvious example is the web.
A web page can link to another web page.
Therefore:
Page A ---> Page B
|
+-----> Page C
Now imagine billions of pages.
You get a gigantic graph.
Search engines can analyze that graph.
A page isn't valuable only because of its text.
Its relationships matter too.
Who links to it?
What pages does it link to?
Which pages are authoritative?
Which pages are connected to similar topics?
Which communities reference the same information?
The web is therefore not merely a collection of documents.
It is a network of documents.
This idea changed search.
Instead of treating every page as an isolated document, search systems could reason about relationships between pages.
One famous example is the idea behind PageRank.
At a conceptual level, links can act as signals about importance.
A page referenced by many important pages can become more important.
That's graph thinking.
4. Social Media Is Basically a Graph With a User Interface
Open almost any social media application.
You see:
- profiles
- posts
- likes
- comments
- followers
- followers of followers
- groups
- communities
- recommendations
- messages
But underneath the interface?
A graph.
Consider:
Derek
|
| follows
v
Developer A
|
| follows
v
Developer B
|
| posted
v
Article X
The application can discover relationships.
Maybe you don't follow Developer B.
But several people you follow do.
Maybe Developer B writes about Rust.
You frequently read Rust content.
The recommendation engine can connect those signals.
You
|
| interested in
v
Rust
^
|
creates content about
|
Developer B
Now the system has a reason to recommend Developer B.
This is why recommendation systems often feel strangely intelligent.
They're not necessarily “understanding” everything in a human sense.
They're discovering patterns in enormous relationship networks.
5. Recommendation Engines Are Graph Machines
Netflix-style recommendations.
Music recommendations.
Shopping recommendations.
Friend recommendations.
Job recommendations.
Video recommendations.
News recommendations.
They can all be modeled using relationships.
Imagine:
User A
|
| listened to
v
Song X
^
|
| listened to
|
User B
|
| listened to
v
Song Y
If User A and User B have similar listening patterns, Song Y becomes a candidate for User A.
You can keep expanding the graph.
User
↓
Song
↓
Artist
↓
Genre
↓
Other artists
↓
Other songs
↓
Other users
The graph becomes a map of collective behavior.
And once you have a map, you can search it.
You can rank paths through it.
You can discover clusters.
You can calculate similarity.
You can identify communities.
You can predict missing relationships.
That last one is especially interesting.
Modern recommendation systems often perform a conceptual operation that looks like:
“There is probably an edge here that we haven't observed yet.”
For example:
Alice ---- likes ----> Programming
Alice ---- likes ----> Rust
Rust ---- related to ----> Systems Programming
Alice ---- ? ----> Systems Programming
The system predicts the missing connection.
Graph thinking turns recommendation into relationship prediction.
6. Artificial Intelligence Is Increasingly Graph-Shaped
AI is often presented as neural networks, transformers, embeddings, tokens, and matrices.
And those things are enormously important.
But graphs still appear everywhere.
Knowledge graphs are a particularly obvious example.
Suppose an AI system knows:
Zambia
|
| capital
v
Lusaka
|
| located in
v
Zambia
Or:
Nikola Tesla
|
| invented
v
Induction Motor
Knowledge can be represented as entities and relationships.
This gives AI systems a structured representation of the world.
The combination of language models and graph structures is particularly interesting because they provide complementary strengths.
Language models are good at dealing with unstructured information.
Graphs are good at representing explicit relationships.
Imagine asking:
“Which companies are connected to this technology through partnerships, acquisitions, employees, and investments?”
A graph can represent those relationships explicitly.
An AI model can then reason over or communicate insights about them.
This creates a powerful combination:
Language understands descriptions.
Graphs organize relationships.
7. Graphs Are Also Hiding Inside Software Dependencies
Here's a graph you probably interact with every day without thinking about it.
Your application's dependencies.
Imagine:
Application
|
+----> Django
| |
| +----> Python
|
+----> PostgreSQL
|
+----> Redis
|
+----> Celery
|
+----> Redis
This is a dependency graph.
Every modern software project has one.
Your package manager is essentially solving graph problems.
When you install a library, that library may depend on another library.
Which depends on another.
And another.
Eventually you get:
A
├── B
│ ├── D
│ └── E
└── C
├── E
└── F
The package manager has to determine:
- which versions are compatible
- which dependencies are duplicated
- which packages conflict
- what order things should be installed
- what can be safely removed
That's graph theory wearing a developer-tooling costume.
8. Build Systems Are Graph Engines
Consider compiling a large software project.
File A depends on File B.
File B depends on File C.
Therefore:
A -> B -> C
If C changes, perhaps B must be rebuilt.
And then A.
But if File X is completely unrelated, there is no reason to rebuild it.
Build systems exploit dependency graphs to determine what needs work.
This is why graph algorithms aren't just academic exercises.
They can dramatically reduce computation.
Instead of saying:
“Rebuild everything.”
The system can say:
“Only rebuild the nodes affected by this change.”
That's a graph traversal.
Modern build systems are essentially asking:
“Which parts of the dependency graph are reachable from this changed node?”
Again:
graphs.
9. Distributed Systems Are Graphs Too
Now we get to one of my favorite applications of graph thinking.
Distributed systems.
Imagine a microservice architecture:
API Gateway
|
+----> Auth Service
|
+----> User Service
|
+----> Order Service
|
+----> Payment Service
|
+----> Inventory Service
That's a graph.
Services are nodes.
Communication paths are edges.
Once you think about the architecture this way, interesting questions appear.
What happens if Payment Service fails?
Which services are affected?
What is the shortest path from the API to the database?
Which service has the most dependencies?
Which service is a bottleneck?
Which services form a tightly coupled cluster?
Which component is a single point of failure?
These are graph questions.
10. The Most Dangerous Service Might Be the Most Connected One
Suppose your architecture looks like this:
Service A
|
v
Service B -> Core Service -> Service C
^
|
Service D
The Core Service is highly connected.
That can be good.
But it can also be dangerous.
If the Core Service goes down, many parts of the system may become unreachable.
In graph terminology, some nodes have enormous structural importance.
A distributed systems engineer might therefore care about concepts such as:
- centrality
- connectivity
- reachability
- bottlenecks
- paths
- clusters
- failure propagation
Suddenly, graph theory becomes architecture theory.
11. Networks Are Literally Graphs
Computer networks are perhaps the cleanest example.
Routers are nodes.
Connections are edges.
Packets travel across edges.
For example:
Computer
|
Router A
|
Router B
/ \
C D
\ /
Server
Routing algorithms ask:
What path should a packet take?
That's a graph problem.
The simplest version is shortest path.
But real networks introduce additional complexity.
Maybe one connection is faster.
Another is cheaper.
Another is unreliable.
Another is congested.
Now edges have weights.
Instead of:
A ---- B
You get:
A --10ms-- B
or:
A --$0.03-- B
Now graph algorithms can optimize routes according to different objectives.
The physical internet becomes a mathematical object.
12. Graphs Explain Infrastructure
Cloud infrastructure can also be represented as graphs.
Imagine:
User
|
Load Balancer
|
API
|
Database
|
Storage
But real systems become much more complicated:
Internet
|
CDN
|
Load Balancer
|
API Gateway
/ | \
A B C
| | |
DB Redis Queue
| |
Storage Workers
Infrastructure tools can model these dependencies.
If you understand the graph, you can reason about:
- failure domains
- cascading failures
- deployment dependencies
- security boundaries
- network reachability
- service dependencies
Architecture diagrams are essentially visual graphs.
That's why a good architecture diagram can reveal problems that hundreds of lines of code hide.
13. Cybersecurity Is a Graph Problem
Cybersecurity might be one of the most interesting applications.
Imagine an attacker compromises one account.
That account has access to:
User Account
|
+----> Server A
|
+----> Database B
|
+----> Storage C
Server A has access to another server.
Database B contains credentials.
Those credentials can access another service.
Now you have a path:
Compromised User
|
v
Server A
|
v
Credential Store
|
v
Service D
|
v
Sensitive Data
Security analysts care about these paths.
The question isn't merely:
“Is this account compromised?”
The more important question can be:
“What can an attacker reach from this account?”
That's graph traversal.
Identity systems can also be modeled this way.
User
|
Member Of
|
Team
|
Has Role
|
Permission
|
Resource
Security becomes a reachability problem.
14. Fraud Detection Loves Graphs
Consider financial fraud.
Suppose five accounts send money to each other in suspicious patterns.
A traditional system might inspect individual transactions.
A graph system can inspect relationships.
Account A ---> Account B
^ |
| v
Account D <--- Account C
Maybe these accounts share:
- phone numbers
- devices
- IP addresses
- addresses
- bank accounts
- beneficiaries
- transaction patterns
The objects themselves might look normal.
The network might not.
This is one of the fundamental strengths of graph analysis:
relationships can reveal patterns that individual records hide.
15. Graphs Are Excellent at Representing Time Too
Graphs don't have to be static.
Relationships can change.
Consider a social network.
Today:
Alice -> follows -> Bob
Tomorrow:
Alice -X-> Bob
The edge disappears.
Now imagine transactions.
Account A -> $100 -> Account B
Account B -> $90 -> Account C
Account C -> $80 -> Account D
You don't only care about who is connected.
You care about when the connection happened.
This gives us temporal graphs.
Now edges have properties:
A --[amount=$100, time=10:30]--> B
Modern systems often need exactly this kind of reasoning.
16. Graph Databases Exist Because Relationships Matter
This is where specialized graph databases become interesting.
A relational database can absolutely represent relationships.
But some workloads are fundamentally about traversing those relationships.
Imagine asking:
Find all friends of friends of friends who attended the same university and work in companies connected to this organization.
That's a multi-hop traversal.
A graph database is designed around the idea that relationships are first-class data.
Instead of thinking:
SELECT ...
JOIN ...
JOIN ...
JOIN ...
you think:
Node -> Edge -> Node -> Edge -> Node
The conceptual difference is important.
A relational database asks:
“How do I join these datasets?”
Graph-oriented systems ask:
“How do I traverse these relationships?”
Neither model is universally better.
They optimize for different shapes of problems.
The important lesson is:
data modeling should reflect the questions your system needs to answer.
17. APIs Can Be Viewed as Graphs
This is an especially interesting idea for backend developers.
Most developers think about an API as:
GET /users
POST /users
GET /users/:id
POST /orders
GET /orders/:id
But you can also think of the API as a graph.
Endpoints expose capabilities.
Resources connect to other resources.
For example:
User
|
has
v
Orders
|
contains
v
Products
|
belongs to
v
Categories
An API client navigates this conceptual graph.
This perspective becomes particularly useful with systems that expose relationships dynamically.
The API becomes less like a list of URLs and more like a map of capabilities.
That leads to an interesting future:
APIs may increasingly behave like navigable graphs rather than static collections of endpoints.
The client doesn't merely ask for data.
It discovers what it can do next.
18. Graphs Help Us Understand Complexity
Here's something I find fascinating.
Complexity often comes from relationships.
Imagine ten independent components.
That's manageable.
Now imagine ten components where every component communicates with every other component.
The number of possible relationships explodes.
For n nodes, the number of possible undirected edges is:
n(n - 1) / 2
For 10 nodes:
10 × 9 / 2 = 45
For 100 nodes:
100 × 99 / 2 = 4,950
For 1,000 nodes:
1,000 × 999 / 2 = 499,500
The nodes grew linearly.
The possible relationships grew quadratically.
This is one reason highly interconnected systems become difficult to maintain.
The complexity isn't necessarily inside individual components.
It is in the connections between them.
This is why software architecture is often a graph-management problem.
19. Microservices Can Accidentally Become a Graph Nightmare
Microservices were supposed to make systems modular.
And they can.
But there's a trap.
Suppose you have:
Service A -> B
Service B -> C
Service C -> D
Fine.
Now suppose:
A -> B
A -> C
B -> C
B -> D
C -> A
C -> D
D -> B
Now you have a highly connected system.
Changing one service can affect many others.
Deployments become complicated.
Debugging becomes difficult.
Failures propagate unpredictably.
The architecture becomes harder to understand.
This is why good architecture isn't simply:
“Use microservices.”
It is:
“Design the dependency graph carefully.”
20. Graph Thinking Changes How You Debug
Suppose a user reports:
“The checkout page is broken.”
A conventional debugging process might inspect:
- frontend
- API
- database
- payment service
But graph thinking asks:
What is the dependency path for checkout?
Maybe:
Browser
↓
CDN
↓
Frontend
↓
API Gateway
↓
Checkout Service
↓
Inventory Service
↓
Payment Service
↓
Bank API
Now suppose the Bank API is slow.
That latency can propagate backward.
Bank API
↑
Payment Service
↑
Checkout Service
↑
API Gateway
↑
Browser
Suddenly the user-visible bug isn't necessarily in the checkout code.
It's somewhere downstream in the graph.
Distributed tracing systems essentially help engineers observe these paths.
21. Graphs Are Also About Communities
One of the most beautiful things about graphs is that they can reveal communities.
Imagine a network of developers.
Some people interact frequently.
Those interactions create dense regions.
You might discover:
Group A
/-----------\
A1 A2 A3 A4
\-----------/
Group B
/-----------\
B1 B2 B3 B4
\-----------/
There may be only a few connections between the groups.
Graph algorithms can identify these clusters.
This can be useful for:
- social networks
- scientific collaboration
- fraud detection
- recommendation systems
- marketing
- knowledge discovery
- cybersecurity
- organizational analysis
The system discovers structure that wasn't explicitly programmed as a category.
22. The World Is Full of Graphs
Think outside computers.
A road network:
City A ---- City B
| |
City C ---- City D
An airline network:
Lusaka
/ \
Nairobi Lagos
\ /
Johannesburg
A power grid.
A water network.
A supply chain.
A telecommunications network.
A family tree.
A company's organizational structure.
A scientific citation network.
A protein interaction network.
A language's semantic relationships.
A blockchain transaction history.
They are all graphs.
The abstraction is incredibly universal.
23. Blockchain Is a Graph With Money Attached
A blockchain can be interpreted as a transaction graph.
Addresses are nodes.
Transactions are edges.
For example:
Wallet A
|
$50
v
Wallet B
|
$20
v
Wallet C
Now add thousands or millions of transactions.
You get a huge transaction network.
Blockchain analysis can examine:
- transaction flows
- address clusters
- repeated relationships
- movement of assets
- suspicious paths
- ownership patterns
The ledger is not just a sequence of transactions.
It is also a network of relationships.
24. Graphs and Algorithms: The Mathematics of Movement
Once you represent a problem as a graph, algorithms become available.
Some of the most famous include:
Breadth-first search
Useful for exploring layers of a graph.
Start
↓
Neighbors
↓
Neighbors of neighbors
↓
...
Depth-first search
Useful for exploring deeply along paths.
Dijkstra's algorithm
Useful for shortest paths in graphs with non-negative edge weights.
A*
Useful for pathfinding when you have a useful heuristic.
Topological sorting
Useful for dependency graphs.
Minimum spanning trees
Useful for connecting nodes efficiently.
PageRank
Useful for ranking nodes based on graph structure.
Community detection
Useful for discovering clusters.
These aren't just university exercises.
They're tools for understanding the structure of real systems.
25. The Most Important Graph Might Be the One You Cannot See
Here's where things become philosophically interesting.
Technology often hides relationships behind abstractions.
A developer writes:
user.save()
But underneath that line might be:
Application
↓
ORM
↓
Database Driver
↓
Connection Pool
↓
Network
↓
Database
↓
Storage
One line of code sits on top of a graph.
You call:
POST /checkout
But underneath:
Client
↓
CDN
↓
Load Balancer
↓
API
↓
Authentication
↓
Database
↓
Payment Provider
↓
Banking Network
One HTTP request travels through a graph.
You open Spotify.
You see:
Play.
But underneath:
User
↓
Recommendation Model
↓
Candidate Songs
↓
Artist Graph
↓
Similarity Model
↓
Content Delivery Network
↓
Storage
The interface hides the graph.
Modern technology is full of invisible topology.
26. Graph Thinking Is a Superpower for Engineers
Once you start thinking in graphs, architecture becomes easier to reason about.
When you encounter a new system, ask:
What are the nodes?
What are the important entities?
What are the edges?
How do those entities interact?
Which relationships are directional?
Does A depend on B, or do they simply relate?
Which edges have weights?
Latency?
Cost?
Probability?
Bandwidth?
Risk?
Which nodes are highly connected?
These may be bottlenecks or critical components.
Which nodes are isolated?
Maybe they represent unused resources or broken integrations.
Where are the cycles?
Cycles can indicate feedback loops or dependency problems.
What happens when a node disappears?
This reveals failure propagation.
What paths matter most?
This reveals critical flows.
That is a remarkably effective way to understand complex systems.
27. Graphs Also Change How You Think About AI Agents
The future of software may involve increasingly autonomous agents.
An agent might have access to:
- APIs
- databases
- documents
- users
- tools
- services
- workflows
- external systems
Imagine representing all those capabilities as a graph.
Agent
|
+----> Search
|
+----> Database
|
+----> Calendar
|
+----> Payments
|
+----> Email
|
+----> Analytics
But now add permissions.
Agent
|
Role
|
Permissions
|
Resources
The agent's world becomes a capability graph.
Instead of simply asking:
“What tools does the agent have?”
you can ask:
“What can the agent reach from its current state?”
That's a much more interesting security and architecture question.
28. Graphs Are the Language of Relationships
Programming languages often teach us to think about:
variables
functions
objects
classes
modules
Databases teach:
rows
columns
tables
queries
Networks teach:
packets
addresses
ports
routes
Distributed systems teach:
services
messages
queues
nodes
AI teaches:
tokens
embeddings
parameters
vectors
But underneath many of these abstractions is another universal concept:
relationships.
Graphs give us a language for those relationships.
That is why they keep appearing.
Not because graphs are fashionable.
Because the world itself is relational.
29. The Future Will Be Even More Graph-Shaped
Technology is becoming increasingly connected.
The number of APIs is increasing.
Cloud infrastructure is becoming more distributed.
AI systems are integrating with more tools.
Businesses are connecting more services.
IoT devices are creating networks of physical objects.
Knowledge systems are connecting enormous quantities of information.
Software dependencies are growing.
Digital identities are becoming interconnected.
Financial systems are becoming increasingly programmable.
Everything is becoming more networked.
And as connectivity increases, relationship complexity increases.
That means graph-based thinking becomes more valuable.
We may see more systems built around:
- knowledge graphs
- dependency graphs
- service graphs
- identity graphs
- security graphs
- transaction graphs
- recommendation graphs
- organizational graphs
- event graphs
- agent capability graphs
The graph isn't necessarily replacing the database.
It is becoming another fundamental abstraction for understanding systems.
30. The Programmer's Mental Model Is Evolving
There was a time when programming largely meant:
Take input, execute instructions, produce output.
Then software became object-oriented.
We started thinking:
Objects interact with other objects.
Then distributed computing became dominant.
We started thinking:
Services communicate with other services.
Then AI became increasingly important.
We started thinking:
Models interact with data, tools, and context.
And now another idea keeps appearing:
Everything exists inside a network of relationships.
That is graph thinking.
It changes the fundamental question.
Instead of asking:
“What does this component do?”
we can ask:
“What is this component connected to?”
Instead of:
“What data does this user have?”
we can ask:
“What relationships does this user participate in?”
Instead of:
“Why did this failure happen?”
we can ask:
“What path allowed this failure to propagate?”
Instead of:
“Why was this recommendation generated?”
we can ask:
“What path through the recommendation graph produced it?”
Those questions are often much more powerful.
Conclusion: Learn to See the Edges
One of the easiest mistakes in technology is focusing too much on the things and not enough on the relationships between them.
We look at users.
But the interesting thing might be who those users interact with.
We look at services.
But the interesting thing might be how those services depend on one another.
We look at transactions.
But the interesting thing might be the network formed by those transactions.
We look at APIs.
But the interesting thing might be how capabilities connect.
We look at data.
But the interesting thing might be how pieces of data relate.
We look at AI models.
But the interesting thing might be the network of entities, tools, concepts, and information surrounding the model.
This is why graphs are so powerful.
They provide a mental model for complexity.
A graph says:
Here are the things.
Here are the relationships.
Here are the paths.
Here are the clusters.
Here are the bottlenecks.
Here are the dependencies.
Here are the missing connections.
And once you can see those things, complicated technology starts becoming less mysterious.
A distributed system becomes a graph.
A social network becomes a graph.
A recommendation engine becomes a graph.
A dependency manager becomes a graph.
A road network becomes a graph.
A fraud investigation becomes a graph.
A knowledge base becomes a graph.
A security model becomes a graph.
An API architecture becomes a graph.
Even the internet itself becomes a graph.
Perhaps that is the deeper lesson.
Modern technology isn't really a collection of isolated machines, databases, applications, and APIs.
It is an enormous web of relationships.
And graphs give us a language for describing that web.
The next time you look at a complicated piece of technology, don't just ask what the components are.
Look at the edges.
Ask what connects to what.
Ask which paths matter.
Ask which nodes control the system.
Ask where information flows.
Ask where failures propagate.
Ask where relationships are forming that nobody explicitly designed.
Because sometimes the most important part of a system isn't hiding inside the nodes.
It is hiding between them.
And once you learn to see those connections, you start seeing technology differently.
You start seeing the world as a graph.
And suddenly, a lot of modern technology makes sense.
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